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Proline Suite User Guide

1. a E i No matter which User Interface Studio or Web you will use Proline s workflow remains the same 1 If you plan to use the quantitation features of Proline you ll need to register the Raw Files and their corresponding MzDB file into Proline s database in first place This is an action you can perform aside from any project 2 Once your files are ready to use you must create a project A project is attached to your user account but you can share it with other users so they can see it from their own account 3 The first thing you need to do in your fresh new project is to import Result Files into it and associate them with the Raw Files you have registered so that you will be able to create identification datasets you want to validate and quanitation experimental design based on your analyses 4 Once your files are imported you assemble them as datasets on which you will launch validation tasks with dedicated parameters 5 You can then launch a quantitation analysis by using your validated identification summaries by re creating you experimental set up your own parameters and ratios Register and Pair Raw amp MzDB Files Proline Database MzDB Files RAW Files Filel raw mzdb Filel raw File2 raw mzdb Select Register amp Pair The Raw Files and their corresponding MzDB files are stored on your servers but they must be registered and paired into Proline s database See how to do this in Pro
2. e max connections Number of concurrent SQL sessions each Proline Server task can use to 5 SQL sessions each Proline Studio instance can use some SQL sessions Default value 100 e tcp keepalives idle Number of seconds before sending a keepalive packet on an otherwise idle TCP connection Help with broken router firewall and checking for dead peers Default value 0 2 hours 300 5 minutes e shared buffers Use about 1 4 of physical memory dedicated to the PostgreSQL instance Default value 32MB 2048MB e checkpoint segments Use shared buffers 16 max 64 or 256 for write heavy bulk loading Default value 3 128 e checkpoint completion target 0 9 for high value of checkpoint segments Default value 0 5 0 9 e temp buffers Per session Used for temporary tables creation Default value SMB 512MB e work mem Several per session Used for hashing sorting and IN operator when processing queries Default value 1MB 4MB to 64MB e maintenance work mem Used for intial index creation and vacuum operations Default value 16MB 1024MB e effective cache size Assumption about the effective size of the disk cache to optimize index use Monitor physical memory allocated by system to disk cache operations Default value 128MB 4096MB
3. Apply MS Query Click Clicking on an MS Query will automatically load the corresponding Peptide Matches in the MS Query Peptides Matches table Double Click Actions Double clicking on a Peptide Match item will open a Peptides viewer tab focused on the corresponding peptide Display Proteins In order to browse the protein set data of a validated identification click on the validated identification node in the project tree on the left side panel of the dataset explorer and then open the Proteins tab Each table of the Result Summary data viewer provides a set of Filters for Numerical Text and Boolean data placed on the left of the grid IMPORTANT Please note that the Protein Table only displays the validated proteins You can reset this filter by clicking on the Remove All button of the proteins table filter panel or by clicking on the circle arrow in the upper right corner of the table E Projects Browser rey New Project Reload M Show RSM Selection Settings Dataset tree G MS Queries amp My Projects i n Baloo Filters Proteins a Text Data Boolean Data Identification Trees Numeric Data 58 Toggle Grouping T Export Accessions h Lo sesame i Group Accession Prot ID Description Coverage Protein Ma Protein Se Val Pepti Is Validate Is Selected A TEST identification 1 So TEST 2 38 Quantitations Search Results J Exported Files Q6FPX5 spjQ6FPX5
4. Rename Clear Delete To validate a Search Result Select one or multiple Search Results to validate Right Click to display the popup Click on Validate menu Validation Dialog WeiMGication Validation I PSM Prefilter s Rank lt z Select gt FOR Filter E Ensure FDR lt 5 0 Protein Set Prefilter s z Select gt FOR Filter E ProteinFDR lt 5 0 Scoring Type Standard Set Typical Protein Match Using rules in priority order Rule 0 Typical Protein Match on Protein Accession Rule 1 Typical Protein Match F on Protein Accession aa Rule 2 Typical Protein Match on Protein Accession aa any string any character In the Validation Dialog fill the different Parameters see Validation description you can add multiple PSM Prefilter Parameters Rank Length Score e Value Identity p Value Homology p Value by selecting them in the combobox and clicking on Add Button you can ensure a FDR on PSM which will reached according to the variable selected Score e Value Identity p Value Homology p Value you can add a Protein Set Prefilter on Specific Peptides you can ensure a FDR on protein Sets you can set the choice for the Typical Protein of a Protein Set by using a match string with wildcards or on Protein Accession or Protein Description See Chang Typical Protein of Protein Sets Note FDR
5. X Axis Calc Mass w Y Axis Score E 150 mp E 160 150 oe Or i re 140 P Wi i o i 4 a ix 130 k y a E j jay s i e s m y b i j pi 120 Pn 42 ts 4 e E A 5 i i me f 3 a 4 og Wi LEd za 3 r a ma sof Seats bee a pte a a J x F m Q WO ey 100 z sa 2 a z 7 Guu f Ed a r a a rua id g tel 1400 1600 1800 2000 2200 ae 3000 3200 E z Remove Vertical Grid It is possible to select linear or log axis by right clicking on an axis Zooming Selection Zoom in Press the right mouse button and drag to the right bottom direction A red box is displayed Release the mouse button when you have selected the area to zoom in Zoom out Press the right mouse button and drag to the left top direction When you release the mouse button the zooming is reset to view all Select Press the left mouse button and drag the mouse to surround the data you want to select When you release the button the selection is done Or left click on the data you want to select It is possible to use Ctrl key to add to the previous selection Unselect Left click on a empty area to clear the selection Calc Mass al Y Axis 2 i 3 Calc Mass Statistical Reports MSDiag In order to launch MSDiag Reports statistical reports simply select a node on the tree and choose Compute statistical reports and wait for the results to appear This applies to a searc
6. and Name of the UDS Db configuration file Java properties format ire proline modulesseq udsDoContigurattonr le po Uds properc Les Paths must exist regular file or directory and multiple paths must be separated by character fr proline module seg localFASTAPaths Y sequence D Temp Admin FAS TAS Java Regex with capturing group for SEDbInstance release version string CASE INSENSITIVE fr proline module seq defaultReleaseRegex D Decoy _ fasta UniProt style SEDb FASTA file name must contain this Java Regex CASE INSENSITIVE mubtaple Regex separated by 7 character fr proline module seq uniProtSEDbNames AUP ISA D Java Regex with capturing group for SEDbIdentifier value without double quote P UniProt EntryName TTA wW A Pel Vl AA eC Se UniProt UnigquelIdentifier primary accession number Ute tS Nh ee GENERIC Regex gt S Fr proliane moou le seq uniProLSEDoldentit rerkRegex gt weil2t VA ey eel CANS Note e fr proline module seq localFASTAPaths only one instance should be defined For linux system fr proline module seq localFASTAPaths ocal data fasta local mascot sequence e fr proline module seq defaultReleaseRegex Regular expression to extract release version CASE_INSENSITIVE from the fasta files e fr proline module seq uniProtSEDbNames Regular expression to identify Uniprot like fasta The entry of these files will be parse using specific r
7. Add Raw Files to Selection button in order to select Raw Files e In the right grid use the Add MzDB Files to Selection button in order to select MzDB Files e You can then make automatic pairs based on file names by clicking on the Make Couples from RAW Files button This will automatically add matching files into the Raw amp MzDB Couples grid If you want to pair files whose names differ you can proceed as follows e Select a file either Raw or MzDB in its grid e Click on the Put in Couple button e Select it in the Raw amp MzDB Couples grid e Select the corresponding file of the other format RAW or MzDB in its grid e Click on its Put in Couple button After this you must choose an Instrument Name and select the owner of the file in the users list Finally click on Register Note that this operation will fail if one of your raw file has already been registered Close the Settings Window Installation Of the Dataset Explorer Application The Proline Web Desktop works like your Operating System you need to install applications before you can use them In order to install the Dataset Explorer application please proceed as follows click on the Start Button in the bottom left corner of the page Be Start click on App Library to open the Application Installation Menu od Change Password Soe App library oy Logout select the D
8. Extraction moz tolerance ppm 5 Extract XIC from All detectable features Deisotoping Identification Based e Feature Clustering moz tolerance ppm 5 0 time tolerance s 15 0 time computation Most Intense intensity computation Most Intense XIC Results When the XIC Design has been generated it is added in the Quantitation Tree You can display its properties especially the configuration used by the right mouse button popup File Window Help Projects Properies XC eal gjer tip F A A t ii sa Quant Processing Config aln_method_name Ta Identifications normalization _method i FF All Imported detect_features A dsi detect_peakels se ds2 start_from_validated_peptides a i Trash extraction_params moz_tol extraction_ params moz_tol_unit clustering_params moz_tol clustering_params moz_tol_unit clustering_params time_tol clustering_params intensity_computation clustering_params time_computation aln_params mass_interval aln_params smoothing _method_name aln_params max_iterations aln_params smoothing_params window_size aln_params smoothing_params window _overlap aln_params smoothing_params min_window_ landmarks 1 fLmapping_params moz_tol Properties ns ftmapping_params moz_tol_unit ns ft mapping_par iti tol Refine Protein Sets Abundances arain E name Display Abundances operator alue Display SC Export A
9. If we consider the following case where Samplel Identification Summary is the merge of Replicatl and Replicat2 Fa on w proteins Fi fp gd A Ho N r pes l a peptides Sample1 oat PSMs Replicatt NEI B Sample Replicat2 O P3 P2 ped oe per ka l Ao i A kS ped i par ri x 77 p P Y gt A P3 D ll P2 S P2 Replicat Saale If the spectral count calculation is done at each child level aligning protein sets identified in parent to protein sets in child we get the following result Samplel ProteinSets Replicatl Replicat2 Ref Prot BSC SSC WSC Ref Prot BSC SSC WSC PR o RS 8S PB 7 7 7 B WA UNU ee N e ee We can see that when different parent protein sets are seen as one protein set in a child the spectral count is biased This calculation was not retain Now if we align on child protein rather than protein sets we get the following result Ref Prot BSC SSC WSC Ref Prot BSC SSC WSC P T ee ee DS e ee e e ee ee Again when considering specificity at protein set level the result of spectral count in Replicat2 is not representative as it has a null SSC and WSC This calculation was not retain A way to make some correction is to define the specificity of the peptide and their weight at the parent level and apply it at the child level Therefore specific peptides for P2 are pes and for P3 it i
10. _ HyperTable _ Coco C Pathway Palette 4 Administration Logout SpecLight Todo List Start e Select proline in the Grid Table and then click on Configure __ Application Manager Configuration Window User Administration Services Manager Application P 02 Configure Unregister Selected Application proline ws_ host Services Directory Browser Registered Services http localhost 8080 proline uds_ db dsn Description dbi Pg dbname uds_db host localhost port 5432 Proline Server Connect en data root D proline data ps_db_dsn dbi Pg dbname ps_db host gimli port 5432 pwd_mascot_data_root tol brandir MascotData pwc_mascot_data_root tol brandir MascotData db_username postgres db_password pg99 raw_files_root tol brandir Masse mzdb_files_root tol brandir mzdb 292 Save Configuration Generate Service e Here are some param values you can use O O O ws_host is the adress of the Proline Web Core server Use the name or the IP adress of the server to set the URL Examples http servername 8080 proline http localhost 8080 proline http 198 0 13 37 proline replace 198 0 13 37 by the right IP adress uds_db_dsn is the UDS database connexion configuration DSN stands for Data Source Name It has the following format dbi name of the Driver Pg for PostgreSQL SQLite for SQLite db_name name of the Postgre SQL database u
11. 0 001 O 0 001 0 002 0 003 0 004 0 005 0 006 0 007 Histogram on Delta Moz An Export Dialog is opened you can select the file path for the export and the type of the export 5 Export a XIC You can export the XIC Desing values by the right mouse button popup gi Quantitations r By Spee Properties i i Tras Refine Protein Sets Abundances Display Abundances d Display 5C Export Abundances I Peptides lons Peptides Rename Proteins Sets Delete Proteins Sets Refined You can export the abundances data at different levels the protein sets the peptides the peptides ions the refined protein sets abundances see Refine Protein Sets Abundances How to validate a Search Result See description of Validation Algorithm Starting Validation Projects TasksLog 8 I ProjectTest x id Category v 26 atabase Z e Identifications st pime E All Imported v 24 tabase Z D Replicate1 25 tabase 4 D F067897 Y 22 Database Z D Replicate2 v 21 atabase Z 40 F057898 ll ee S D Replicate3 v 19 tabase Z D FOS 700 y 18 atabase Z Trash Search Result p bervices Identification Summary b kaos Services Properties a Add services Merge Validate Services Change Typical Protein bervices Generate Spectrum matches P Compare With SC p Export Change Description
12. right corner of the desktop screen It opens a small grid where you can see all your tasks Tasks T 9 Clear Completed Failed Tasks Clear ALL tasks Name ID Message Start End State Check Result Files 1 Done 29 09 16 08 29 09 16 08 Jf Import File F054972 dat 2 Running 29 09 16 08 lt 1 Import File F054973 dat 3 Submitted 29 09 16 08 lt k Tasks When a task is done you are notified by a small message in the top of the screen and you can see its status in the tasks window Terminated Tasks Following tasks are done Import File F054972 dat All the Result Files you have imported are listed in the Search Results panel you can access by double clicking the Search Results node in your project s tree Create a new Dataset Once your Result Files have been imported you can use them to create a new Identification Dataset Double Click on the Identification Trees element in the tree on the left side of the window The grid which just appeared is meant to list all your datasets For now click on the New Dataset button from the bar or by right clicking on the Identification Trees fm Dataset Explorer re New Project ty Reload Dataset tree E Rg My Projects z M Test j a Trash 4 Exported Files node 8 Settings lt lt R My Projects PE Project Test J Identifications in Test je New Dataset F Export All Identification Summaries Identification Summary
13. C x a Export Compute Statistical Reports MSDiaq Change Description Rename Task Delete Timestamp Ask Time In the example the user has clicked on Identification Summary gt New User Window and selects the Peptides View as the first view of his window projects Tetog rower i BEB EL cats on w 19 Database A Low vi Database A bo 17 Database A Low You can add other views by using the button Missed Cl Protein Set Count RT GVLGYTEDAY 77 6 3508 72 1190 585 3 25 110 56 VAVDOPSLA a T E wal O LGGVAVS ena e870 aswon a192 m os A o o o aO an uss 2s SSS use on 4 wa E E E a210 wers oa SCS 11 a eZ svs 97495 as el ILF ItsLilst m st il a a a 7 i r z 7 i r FE E E E HE EE ATU In this example the user has added a Spectrum View and he saves his window by clicking on the Disk Button Protein Set Count RT 3008 72 1190 38 110 56 2309 20 C T E E T 1971 88 987 0 D ee E E E E ee a Query 28368 GVLGYTEDAVVSSDFLGDSHSSIFDASAGIQLSPK 250 000 200 000 150 000 intensity 100 000 20 000 250 500 750 1 000 1 250 1 500 1 750 2 000 2 250 2 500 2 750 3 000 3 250 3 500 m z The user selects Peptides Spectrum as his user window name Protein Set Count SYTEDAVYSSDFLSDSHSSIFDASASIQLSPE 200 000 130 000 Intensity 100 000 30 O00 250 500 750 1000 1250 1500 1750 27000 2250 23500 275
14. ID Valic Name New Dataset Export All Identification Summaries You should now see a window asking to choose a source of data for your new Dataset e Choosing Result Set allows you to build a new dataset from both Result Files you have imported and existing identification datasets whether they have been validated or not e Choosing Result Summary will let you build a new dataset from one or more existing validated identification dataset Il will duplicate them into a new Dataset without their validation data For now assuming you are creating your first identification dataset you should choose the Result Set option On the two tabbed panel go to the Result Sets tabs in order to see the list of the files you have imported R amp R My Projects re Project Test re Import Result File to Project Test New Dataset in Test Search Results Data Sets Selection hy Reload Remove Selected Items Name Result File Name Raw File Name File Name Name Gamme Levure UPS1 F054972 dat F054972 dat Gamme Levure UPS1 Ea e e RR a Cae Rea Ea eee rae ana EREE ER Add To Selection DataSet Name Test DS To add one or many files to your selection select them in the grid you can use the Ctrl and the Shift keys to make a multiple selection then click on Add to Selection on the bottom of the window You can also double click on one file to quickly add it to the selection To remove any file from the selection just select the
15. O aasa 293 2 O N karam A sanl ssis sara i oaos o 10911 25 8623431 aa mMOAWTVPAY 2 oaza 4333171788097600 afas783486 00 4f18214218 00 17564344 00 sfugourens a 1075 24 6788 92 su0746125 1 271548300 2 2892440 75 3054018 50 ss aocHaK mrs nsen res ol 35286766 1 238556 27 _235041 39 ar hwseesemn 3 mee sossa zsuneo0 a saess z ais 25 272o 39 siama i ma el areal dnm 2 ETTA ia 40 pipinin 306 7 3003 84 813963 06 0 673493 50 1 686845 56 830070 56 ehmas 2 sra monemo oun a eaaa sn summ O A ee ssa asosa 2 eesis 2 1516386 50 SIO TE a7 LFLESGFTYCQ 2 oanas 550 68 112594262 1 68137575 o 1066247 50 1218140 25 51 GASAGEGLGN J J s sse aosa O dams OOO gt ZE se vswrayveTaR 2 683 87 5309 73 s2a66a475 ol a1mio7Lso of 3618145 50 346394 0 mans ec st somo a eaa sss poms as a sera CE hr cove A AM AT CAT onl AnnAWwce CA J JPA T Te 7 Ano TIO Cr 19774070 Aa Create a User Window You can lay out your own user window with the desired views You can do it from an already displayed window or by using the right click mouse popup on a dataset like in the following example Use menu Search Result gt New User Window or Identification Summary gt New User Window Search Result Identification Summary Properties i Add ic Merge ll 4 Validate E Change Typical Protein iw 7 Generate Spectrum matches B 1 Compare With
16. Server side Proline requirements o aJava SE 8 JRE or above must be installed o the PostgreSQL database server tested versions are 9 X must be installed and configured On windows the automated installer includes the PostgreSQL server which can be installed on the same computer than Proline or a dinstinct one By default PostgreSQL settings are defined for modest hardware configurations but they can be modified to target more efficient hardware configurations See PostgreSQL optimization o Proline Server must run in English locale on Linux OS environment variable LANG en US UTF 8 can be exported before starting ProlineWeb Core If not in english you can also modify the jetty runner sh see installation steps to add Duser language en Duser country US parameters e Client side requirements for Proline Studio o a Java SE 7 JRE or above must be installed on Windows OS Proline Studio installer already includes a JRE 32 or 64 bit distribution If you want to use Proline remotely through a Web client the ProlineWeb components and their requirements must also must installed e Server side ProlineWeb requirements o MongoDB database server must be installed Note this database server can be installed on a distinct computer e Client side requirements for Proline Web o arecent Web Browser IE 9 Firefox 25 Chrome 30 Installing Proline Suite e To install Proline for the first time go here Installing
17. T L j A j i toot 25 000 7 p n w 20 000 3 e wg 15 000 7 10 000 7 5 0004 y2 0 25 50 To 100 15 150 15 300 25 30 75 3200 3 D0 Jb 0 45 gt A0 445 w0 S Co K m z M A Display Decoy Data B Search in the Table using and wild cards C Filter data displayed in the Table D Export data displayed in the Table E Send to Data Mixer to compare data from different views F Create a Graphic histogram or scatter plot G Right click on the marker bar to display Line Numbers or add Annotations Bookmarks H Expands the frame to its maximum other frames are hidden I Gather the frame with the previous one as a tab J Split the last tab as a frame underneath K Remove the last Tab or Frame L Open a dialog to let the user add a View as a Frame a Tab or a splitted Frame M Save the window as a user window to display the same window with different data later N Export view as an image O Generate Spectrum Matches Filter Tables You can filter data displayed in the different tables thanks to the filter button at the top right corner of a table File Edit View Tools Window Help Menwtcatons arorimrotns w My Projects a a Your project name 4 Stara L FO71424 When you have clicked on the filter button a dialog is opened In this dialog you can select the columns of the table you want to filter thanks to the button In the following example we have added tw
18. compare the Spectral Count between the different Identification Summaries ese tegees f 03 ese a ASHE 4 00 300 so a ae soe a DE a E i a E E E CRA P A T T E E E E E E The overview is based on the Basic Spectral Count but it can be changed thanks to the Column Buttons This button allows changing the visibility of the columns too i Protein a a Peptide eBasic eo Weighte Peptide eBasic oe Weighte Peptde m Basic T 374 RSB is 577 8 00 7 6337 7 6 77 17 00 14787 6 77 15 00 am 7 a a ET E E E 376 Jra Select Columns to DERAN 379 YP250 8 00 381 e 100_5 _F054975 E Status No Overview 3 00 2 ENS 100 5 FO54976 Peptide Number a 100 5 F054977 Basic SC Overview on Specific SC 385 R55 Y a 100 5 FO54967 E Specific SC Overview on Weighted Sc 12 00 6 100 5 FO54968 Weighted SC Si a 100 5 Fo54969 ios 388 389 390 391 h 392 me e a a 7 395 YHI9 Y mall a 7 00 7 00 3 5 00 3 00 2 Comparing Spectral Counts If you sort the column Overview by clicking on its header you will be able to easily find the proteins with Spectral Counts different from one Identification Summary to another Display a Spectral Count You can later display again an already generated Spectral Count see Display a Spectral Count XIC Quantitation For description on LCMS Quantitation you can first read the principles in this page Quantitatio
19. gt 8080 proline in your favourite browser The following message must appear ProlineWeb Core working Number of IVersion services lt X gt fr proline core wsl Version Module ProlineWeb Core Version lt XXxX gt fr proline module parser omssa Version Module PM OmssaParser Version ae Ge a fr proline module parser mascot Version Module PM MascotParser Version lt XYZ gt fr proline admin Version Module Proline Admin Version lt ZYW gt fr proline util ScalaVersion Module ProFiI Commons Scala Version lt YZxX gt fr proline util JavaVersion Module ProFI Commons Java Version lt YXZ gt fr proline core service Version Module Proline OMP Version lt WYZ gt Installing and configuring the Sequence Repository Even if this is an optional module it 1s recommended to install it mostly if you want to view the proteins sequences in the user interfaces It can be installed on the same machine running the Proline Server However as this module will parse the mascot fasta files to extract sequence and description from it it will be more efficient if installed on the computer executing your Mascot Server In any case you should also be able to access to the PostgreSQL server from the computer where Sequence Repository is installed Sequence Repository installation Windows users Select this component from the wizard of the automated installer The corresponding program files will be located in the seqrepo sub folder of t
20. in the scatter plot This step was implemented using a moving median smoothing cf figure 5 500 E Sows 600 geltatime Delta time between the reference Delta time between the reference map and the compared map 209 3 map and the compared map if F e i gt P 7 i py riai gt K 26 5 f g 9 4 Tag Me amp gt d gt gt a 7 i a _ Ai A af e j rom s gt ev Te a y s S7 X mo bus eh E P E r NE 4 ta a Pee 18g eee i 6 er oe x ha opel I Se ihe erti otra tl 7 cakes cS Smoothing Oe We Ee eae Seg aS JIN no 200 T EH EA N ip wee a vs tee tee 200 sab pt yt n on St ss ry h A Pied f Ses x gt o tse 1 tay Sia i f l SB w n A gt H A fue A _ A z ni e E PG 0 Se 4 ag o a x Tr E7 lt z ry a n Er es p pes s 3230 sog 3 S008 iA RA Baits hin oe ey Ar 4 say is at 5 a ots te ns pits ol SRE Re Q 1680 3090 3000 4000 S000 777 16000 S 7odg E A ee i Ta Ste za w gt x 5 AEN e E rf 4 r r M Reference map Reference map 200 Fy a elution time A A E SO elution time IAF Or ee 4 n a B x is E g in m A a nef r 400 r a i ae me L ont r n PLT 35 400 PET 5 4 E F e r D g gt d aS oe a Fe de r
21. interpretation of a given set of MS MS spectra given by a search engine or a de novo interpretation process It contains one or many peptides matching the submitted MS MS spectra PSM 1 e Peptide Spectrum Match and the protein sequences these peptides belong to The Search Result also contains additional information such as search parameters used protein sequence data bank etc A Search Result is created when a Result File Mascot dat file or an OMSSA omx file is imported in Proline In the case of a target decoy search two Search Results are created one for the target PSMs one for decoy PSMs Content of a Search Result Importing a Result File creates a new Search Result in the database which contains the following information e Search Settings software name and version parameters values e Peak List and Spectrum information file name MS level precursor m z e Search result data o Protein sequences o Peptide sequences o Spectra o 2 kinds of Matches Peptide Spectrum Matches i e the matching between a peptide and a spectrum with some related data such as the score fragment matches Protein Matches i e the proteins in the databank corresponding to the PSMs identified by the search engine Mascot result importation The peptide matches score correspond to Mascot ion score OMSSA result importation The peptide matches score correspond to the negative common logarithm of the E value e Score logl0 E valu
22. is recommanded to be on the same computer Sequence Repository is recommanded to be installed on the computer where fasta files are accessible Once Proline is installed you must initialize Proline datastores and settings On this purpose the Proline Admin software is provided with the Proline Suite It is available as a command line tool or as Graphical Interface called Proline Admin GUI We will guide you through this process step by step using both these tools Setting up the Datastore You must first configure ProlineAdmin since this component is used to create the databases needed by Proline From graphical tool ProlineAdmin GUI Launch ProlineAdmin GUI Windows users A shortcut Proline Admin is available in the Windows Start Menu under the Proline folder Linux users or manual installation Execute the start sh script located in the folder obtained after Proline Admin GUI archive file extraction ProlineAdmin GUI usage The default configuration file config application conf 1s loaded You can alternatively edit this file see Configuring ProlineAdmin section below or select another conf file of the same format To edit default file press the Edit Proline configuration button You can now edit your file in the newly opened window and save it ee a Proline configuration editor D Dev eclipse workspace proline Proline Admin GUI target config applic _ o 2 amms proline config drive
23. meantime for each map E 100 3000 g s 2500 4 ____ E sie l a NY 2000 4 t 1500 5 40 j j Ayali k 1000 4 2 50 Ants Wack 5 a hey Lt a oe 0 0 i 0 1000 200 3000 4000 5000 6000 7000 map3 mapl map 2 Elution time x Reference map map3 mapl map2 Figure 4 Selection of the reference map The chart on the left shows the time distances between each map and the average map obtained by multiple alignments The chart on the right summarizes the integration of each curve in the chart on the left The map closest to the average map is Selected as the reference map Two algorithms have been implemented to make this selection Exhaustive algorithm This algorithm considers every possible couple between maps 1 For each map compute the distance in time to all the other maps Sum of the distances in seconds 2 The reference map is the one with the lowest distance Iterative algorithm Randomly select a reference map Align this map with all the other maps Compute the distance in time to all the other maps The new reference map is the one with the lowest distance Steps 2 to 4 are repeated unless 1 the reference map remains the same for two consecutive iteration 2 the maximum number of iteration 1s reached default value is 3 ea ae Alignment smoothing The last thing to do 1s to find the path going through the regions with the highest density of points
24. peptide only e Protein WSC SSC weighted peptide spectral count for shared peptides The weight of a peptide for a given protein P1 peptide SC x number of specific peptides of P1 number of peptides specific peptides of all protein identified by the peptide See explanation in previous chapter Quantitation principles This section will describe in details the quantitation principles and concepts e LC MS quantification Different strategies for quantitative analysis e LC MS quantification workflows Workflow and implementation in Proline e mzDB processing Extracting peptidic signals from a file converted into the mzDB format e Label free LC MS quantitation workflow Label Free specific workflow Quantitation configuration The first quantitation step as well as the advanced quantitation see Quantitation principles have some parameters that could be modified by the user e Label free LC MS quantitation configuration e Advanced Quantitation Profilizer configuration LC MS quantification Different strategies for quantitative analysis Although 2D gel analysis has been a pioneer method in this field it has been gradually replaced by nanoLC MS MS analysis allowing nowadays to quantify a larger number of proteins and allowing their identification Quantification is made on thousands of species and requires new and adapted algorithms for the processing of complex data Two major Strategies are available to perform nanoL
25. the Proline Suite The Proline suite is based on different components The following documentation describes the installation procedure for each of this component e Proline server e Sequence Repository e Proline Studio e Proline Web Proline server installation and setup Proline server installation Windows users Download the automated installer from the Proline website http proline profiproteomics fr download The wizard will guide you through the installation process By default the installer will unpack all components on the computer However it is possible to install the Proline components on distinct computers if it fits better your hardware architecture For users who prefer manual installation or witout administrator rights an archive file of the distribution is also available You can follow the installation procedure described in the next section Linux users or manual installation There is no automated installer at the moment First check that all requirements are first installed on the computer Then download the zip archive containing Proline components from the dedicated website http proline profiproteomics fr download The Proline Server archive file contains three others archives corresponding to the different components e Proline WebCore the Proline Server e Proline Admin GUI e Sequence Repository Unzip these components on the appropriated computer Proline Server and Proline Admin
26. the elution time scale detected isotopic profiles of the peptide on different consecutive MS spectra all along its chromatographic elution This process depends on the comparison of experimental data and theoretical known models of isotopic distribution and peptide chromatographic elution The purpose of this analysis is to find a list of features corresponding to all the signals for a single peptide ion with their corresponding coordinates The identification of these peptides can be done from the MS MS spectra matching these features or using a targeted approach in a second acquisition or with a database of a set of previously identified peptides containing information such as the peptide sequence mass and elution time This third method is called Accurate Mass and Time Tags or AMT Smith Anderson et al 2002 e Supervised approach the coordinates x y of the peptidic signals to extract are known or predicted In an LC MS experiment the MS signal intensity of an peptide eluting from the chromatographic column can be monitored cf figure 4 The area under the curve of the chromatographic peak is the extracted ion current XIC also called extracted ion chromatogram and it is proportional to the peptide s abundance in the sample Indeed it has been proved that the XIC is linearly dependant of the quantity of the peptide Ong and Mann 2005 Therefore the signal analysis consists of extracting the intensity of the signal at a speci
27. ys AT E gt ULES D l e b a ma 2 R o p Fe a f yE RN f 3 a 8 gt A n Pi e f FES i ae eve A oes erst eT 600 d t OR TI a LETON SO Figure 5 Alignment smoothing of two maps using a moving median calculation The scatter plot represents the time variation in seconds of multiple landmarks between the compared map and the reference map against the observed time in seconds in the reference map A user defined window is moved along the plot computing on each step a median time difference left plot The smoothed alignment curve is constituted of all the median values right plot 4 Creation of the master map Once the maps have been corrected and aligned the final step consists of creating a consensus map or master map It is produced by searching the best match for each feature detected on different maps The master map can be seen as a representation of all the features detected on the maps without redundancy cf figure 6 Feature mapping Master map a al y y RT l E a T E m z Figure 6 Creation of the master map by matching the features detected on two LC MS maps The elution times used here are the ones corrected by the alignment step The intensity of a feature can vary from one map to another it can also happen that a feature only appears in one map During the creation of the master map the algorithm will first consider matches for
28. 0 3000 3250 3500 mz Now the user can use his new Peptides Spectrum on a different Identification Summary gt Identifications ZN iz All Imported On F067897 Search Result Identification Summary P PSM Properties o Peptides Protein Sets Add Merge New User Window Validate Manage User Windows Change Typical Protein Generate Spectrum matches Compare With C Peptides Spectrum Export Compute Statistical Reports Change Description Rename Delete List of Abbreviations Calc Mass Calculated Mass Delta MoZ Delta Mass to Charge Ratio Ion Parent Int Ion Parent Intensity Exp MoZ Experimental Mass to Charge Ratio Missed Cl Missed Clivage Next AA Next Amino Acid Prev AA Previous Amino Acid Protein S Matches Protein Set Matches PSM Peptide Spectrum Match PTM Post Translational Modification RT Retention Time Frame Toolbars Functionnalities TasksLog F067897PSM_ a l e Liy F Calc Mass Exp Moz Pom Charge Missed Cl Ion Parent Int PTM Dr me 7 574 31 575 31 0 51 1 T lal 4 574 31 575 31 0 48 1 1 5 574 31 575 31 0 51 1 0 1 598 42 599 42 0 69 1 0 2 598 42 599 42 0 69 i 0 4 598 42 599 42 0 69 1 0 1 616 32 617 33 0 23 i o 2 616 32 617 33 0 26 1 0 i 629 34 630 35 0 41 1 0 3 633 35 634 36 0 36 1 0 1 634 29 635 30 1 29 1 0 3 647 40 648 41 0 42 1 0 il 35 000 7 R a s G a y f 30 000 4 m j ie y R
29. 2 31 Unassigned 1971 69 Unassigned Assigned Export Data Image There are four ways to do an export Export a Table thanks to the export button supported format are xlsx xls csv Export data thanks to a Copy Paste from the selected rows of a Table to an application like Excel Export all data corresponding to an Identification Summary Export an image of a view 1 Export a Table ProlineStudio 0 File Edit View Tools Window Help teenutestons Erorr a My Projects 5 E Your project name m E All Imported 3 KA Replicate 1 nF o7 1423 a aA Replicate2 F071424 a KA Replicate3 F071425 i it TRASH ia click on the Export Button at the left top of a abe To export a table An Export Dialog is opened you can select the file path for the export and the type of the export Excel xlsx Excel xls or CSV To perform the export click on the OK Button The task can take a few seconds if the table has a lot of rows and so a progress bar is displayed 2 Copy Paste a Table To copy Paste a Table Select rows you want to copy Press Ctrl and C keys in the same time Open for example excel and press Ctrl and V keys in the same time to paste the copied rows 3 Export an Identification Summary To Export all data of an Identification Summary you must right click on a Identification Summary to open the contextual popup and sele
30. 3 2p Test DS 5 2 r A F054972 dat 1 a i Group Name Group 1 A F054973 dat y2 Quantitations Samples Add New Sample Remove selected S Sample Analyses Remove selected Sample Analysis Search Results Name Identification Summary Raw File Name Exported Files Ech1 O Re Af Group Name Group 2 Samples Add New Sample Remove selected S Sample Analyses Remove selected Sample Analysis Name Identification Summary Raw File Name Ech 2 F054973 dat 2 OEMMA121101_56_copy raw The Experimental design tab 1s where you define your Groups and Samples By default two groups are created and each one contains a sample You can create new groups by clicking on the Add Group button in the top bar of the tab In each group you can manage your sample by using the buttons in the left grid To add a Search Result to one of the samples of the group select it in the left grid and drag and drop a validated result set from your project s tree to the Sample Analyses grid Once you have prepared all your group and samples click on the Next button Abundance Extraction parameters Abundance Extraction Feature extraction strategy Start extraction from Extraction Params Extraction m z Tolerance m z Tolerance unit Clustering Params Time Tolerance sec M z tolerance m z Tolerance unit Time Computation Cluster Intensity Computation Alignment Computation Method Max number of Iterations M z tole
31. B is running If it s not please start it manually e In order to start the PWX server go to its installation folder On Windows platforms launch the start bat script On Linux platforms execute the start sh script TODO create this script Connect to the Proline Desktop Once PWX is running you can connect to the Proline Web Desktop by opening a Web Browser and go to the address of this form name of the machine 9000 or local ip of the machine 39000 for instance 192 168 0 30 The default user is admin and its password is proline Don t forget to change its password from the start menu button once you re logged in Setup the Proline Service e The connection between the desktop and the proline core server is provided by the proline service This service 1s included with the Proline Web eXtension server but it needs to be configured The following steps will explain how e To set it up go to Start gt Administration gt Services Manager On the Services Directory Browser you should see the proline service appear in the the table M Application Manager User Administration Services Manager Application Manager Server Settings Services Directory Browser Registered Services Create New Service Register Remove Service Title Name Name proline Author Desc amp admin Dataset Explorer F Change Password C Uniprot F App library Quality Strict
32. C MS MS relative quantification strategies based on isotopic labeling of peptides or proteins in one of the compared conditions and label free based strategies that can be analyzed in different ways There are usually three types of LC MS MS data analyses cf figure 1 Signal Intensity Extraction of a couple of MS signals detected within a single analysis when using a isotopic labeling strategy Counting and comparing the number of fragmentation spectra MS MS of peptides from a given protein detected in parallel analysis Label free quantitation based on spectral counting Extraction alignment and comparison of the MS signal intensities from the same peptide detected in parallel analysis Label free quantitation based on LC MS signal extraction Peptide A sample y m z Tr z Tr Sy uf ey Peptide A sample x K V G m z Tr z oe gt gt or Peptide A Sample Y m z Tr z WV gQ As 7 amp L SOton by PICL y Nass shin cling z M m z Figure 1 Main view of different approaches of LC MS MS quantitative analysis Mueller Brusniak et al 2008 At first nanoLC MS MS quantitative analysis has been made using isotopic labeling strategies Labelling molecules facilitates the relative quantification of two conditions in the same nanoLC MS MS run According to the theory of stable isotope dilution a isotopically labelled peptide is chem
33. ES i t Bg E O AE JE Mala iii Seed elele SEE ede ladle eel z Lr IILSTI Da P a sea reas es ee ore reas ores pve vast 0 _ 0 lt as o 0 8 ot 8 Ls ase 8 ey 0 OTC YEAST SK YEAST E ooo _ 0 C CE 0 A E O ol Note Abbreviations used are listed here Peptides Window If you click on Peptides sub menu you obtain this window Z7AHUMAN P62979upslRS27A 642 10E 0 T Typical Protein ATCC 204508 5288c GN RPS31 PE 1 SV 3 j E p a p r 112 95 2 RS27A_YEAST R 12 27 176 882 0 68 2 aD een PRS2 pP 7 539 76 Em SO emmen To wr OP a 38396 6l 6l 328 ss psz vestr ea 72 106 598 0 52 a oj RSZ7A YEAST o 307 955 53 478 0 72 2 olse r emps eaaa 0 25 2 ol 5 eee eee ee eee a ae R hra o 28 563 27M veast aa aaa eas 0 42 a 0 33 1 Upper View list of all Peptides Middle View list of all Protein Sets containing the selected peptide Bottom Left View list of all Proteins of the selected Protein Set Bottom Right View list of all Peptides of the selected Protein Note Abbreviations used are listed here Protein Sets Window If you click on Protein Sets sub menu you obtain this window TasksLog se F067897 Protein Sets s agg Gera YEAST poPoasolea port fpronsmo KPYK1_YEAST sp P00549 KPYK1_YEAST _ KPYK2_YEAST sp P52489 KPYK2_
34. From Proline Web Desktop You can also create users from the Proline Web Desktop administration interface Create a User and synchronise it with the Proline Core UDS Database Proline STUDIO Server Connection When you start Proline Studio for the first time the Server Connection Dialog is automatically displayed ProlineStudio Beta 2 rc1 epa File Window Help Category Task Description Server Parameter Server Host _http serverHost 8080 proline User Parameters User yourName Password eecceeccces Remember Password You must fill the following fields Server Host this information must asked to your IT Administrator Project User your name an account must have been previously created by the IT Administrator Password password corresponding to your account If you check Remember Password the password will be saved So when you will re start the application Proline Studio will automatically connect to the server and load your projects without opening the Server Connection Dialog Create a New Project Projects amp lt Select a Project gt m f To create a Project Click on button at the right of the Project Combobox The Add Project Dialog is opened Fill the following fields Name name of your project Description description of your project You can spec
35. Mizar Pe LVDLIK ES LVDLLK RS2 A_ YEAST PHSG_YEAST PJ PI MI nm cn fF ee ee a M S eel J He ISG_YEAST box f 2a y j a o o wu a o O s o O O o o e EE e E e asa e E S S EEE 11 Fetes 220 705 38 0 0 37 RIR4_ YEAST TEE a E E E E E Ten oe oe suk sa a d sa san ze AO O OOOO mr C as me senza rea 74 4 256 709 35 3552 68 0 374 RIR4_YEAST 1 pus as fast E E E al vr ee PYRI YEAST F ELEI a If you have clicked on the button the Add View Dialog is opened and you must select the Graphic View TLELI Graphics Histogram Scatter Plot o A 1 1363 75 682 88 0 61 aooo PYRLLYEAST 2639157263 687 32 0 68 2 ___Carbamidomet ENO1_YEAST I I I I Mean 0 9833388249075191 Stdev 1 2922531 425608335 A Display Remove Grid toggle button B Modify colour of the graphic C Lock Unlock incoming data If it is unlocked the graphic is updated when the user apply a new filter to the previous view for instance Peptide Score gt 50 If it is locked changing filtering on the previous view does not modify the graphic D Select Data in the graphic according to data selected in table in the previous view E Select data in the table of the previous view according to data selected in the graphic F Export graphic to image G Select the graphic type Scatter Plot Histogram H I Select data used for X Y axis 7 3 z 3 r 7 RA Graphic Scatter Plot
36. O FOr each peptide match If there is a homology threshold and ions score gt homology threshold Protein score peptide score homology threshold else tf ions score 2 identity threshold Protein score peptide score identity threshold This score has the same benefits than the MudPIT one The main difference is that the minimum value of this modified version will be always close to zero while the genuine MudPIT score defines a minimum value which is not constant between the datasets and the proteins 1 e the average of all the subtracted thresholds FDR Estimation There are several ways to calculate FDR depending on the database search type In Proline the FDR is calculated at peptide and protein levels using the following rules e if the Search has been done on a concatenated Target Decoy bank FDR 2 nbr DecoyPSM nbr TargetPSM bnr DecoyPSM Note when computing PSM FDR peptide sequences matching a Target Protein and a Decoy Protein is taken into account in both cases e if the Search has been done on a separated Target Decoy bank FDR nbr DecoyPSM nbr TargetPSM Validation Algorithm Once a result file have been imported and a search result created the validation is performed in 4 mains steps Peptide Matches filtering and Validation Protein Inference peptides and proteins grouping Protein and Proteins Sets scoring Protein Sets Filtering and Validation saat ae se Finally the Identifica
37. Proline Suite User Guide Proline is a suite of software and components dedicated to mass spectrometry proteomics Proline lets you extract data from raw files or identification engines organize and store your data in a relational database process and analyse this data to finally visualize and extract knowledge from MS based proteomics results Combine Validate Import results Quantify signal xtra Analyze Store amp Organize Explore Control Proline Studio Web Proline suite main features The current version supports the following features e Import identification results OMSSA and Mascot files are currently supported Once imported search results can then be browsed and visualized through a graphical user interface e Validate search results using customizable filters and infer proteins identification based on validated PSM Identification results issued from the validation can obviously be browsed and visualized e Combine individual search results or identification results to build a comprehensive proteome e Export identification results in different formats including standard exchange formats The software suite 1s based on three main components e A relational database management system storing the data used by the software in four different databases e lt A web server handling processing tasks and web data access e Two different graphical user interfaces both allowing users to l
38. RS25_CA Brest _ Q3E792 splQ3E792 RS254_Y ADH P00330 sp P00330 ADH1_YE Test2 Q75DJ1 splQ75DJ1 RS25_AS J Identification Trees je Remove All _ POCOT4 spiPOCOT4JRS258_Y Quantitations Search Results Filters J Exported Files Dat Text Data foan Peptide M Peptide ll Sequence PTMs Charge Experime MissedC Is Validat Is Selecte Protei 2957 2952 EGIKPISK 2 492 81 0 Y Y 3 4238 2602 YVSVSVLVDR 2 568 81 0 v y 4 Protein click action Clicking on a protein will automatically load the related peptides in the bottom table Clicking on the small Magnifier near the AC Number of a protein will open the UniProt app if you have installed id focused on the corresponding protein Double click actions Double Clicking on a peptide of the Peptide table will open a new Peptide viewer tab focused on this peptide Display Identification Summary additional information The Infos panel sums up the validation parameters and results Dataset tree A My Projects g Baloo D Identification Trees E TRASH 1 7 go TEST 26 HA TEST identification 1 85 TEST 2 38 gi Quantitations D Search Results Exported Files gy Test k g Test J Identification Trees Quantitations Search Results D Exported Files If you clicked on an Aggregate node this panel will show the infos of the Merged Result Infos Pec it MS Qu
39. S2 event event 10 scans integration 10 scans integration Ascendant slope 3 For each isotopic profile the intensities are extracted allowing gaps default value is 1 until a minimal intensity is reached This minimal intensity is defined as a ratio of the detected apex intensity default value is 0 001 Only one extraction is done per spectrum hence reducing the extraction time theoretically Isotopic pattern extraction example of signal z 2 scan7 scan6 Adaptive apex determination Imax ns scan5 ____ gt _______ gt scan4 Stop extraction gt gt cause we scan3 reached min Integration until 0 001 of Imax both intensity or sides scan2 Q _ ___ _ gap scan1 scanO 300 1 0027 z 300 5 1 0027 z 301 m z Monoisotopic 4 The peak is detected on the extracted signal corresponding to the isotope signal with the highest relative intensity predicted by the averagine most of the time it corresponds to the monoisotopic peak in conventional conditions such as trypsic digestion The limits of this peak are used to tune all the limits of the isotopes elution peaks To do so two different algorithms are being tested 1 Basic algorithm applying a Savitsky Golay smoothing then looking for the local highest point 2 Wavelet based algorithm using multiple wavelet transformed curves to determine the position of the peaks 5 The last step consists in extracting the peptide s
40. Spectral Count You can display a generated Spectral Count by using the right mouse popup ul Quantitations E IC eAfopectral Coun Properties Refine Protein Sets Abundances Display Abundances Display C Export Abundances Rename Delete To have more details about the results see spectral count result wanes a 333 334 335 340 C 348 YE ea iao 349 RL33A_Y 4 00 350 YMY9_YE 352 CS i E E Bo so A ro 7 356 Sne fh A e o a a a a j AMM e S E E E E E E E Display a XIC To display a XIC right click on the selected XIC node in the Quantitation tree and select Display Abundances and then the level you want to display i Quantitations Properties Refine Protein Sets Abundances Display Abundances Display C Peptides Ions Peptides Proteins Sets Export Abundances Display Protein Sets By clicking on Display Abundances Protein Sets you can see all quantified protein sets For each quantified protein set you can see below all peptides linked to the selected protein set and peptides Ions linked to the selected peptide Tasks Log 22 fF XIC Protein Sets 2 atalab la Proteins Sets 843 5 TREE EA Quant Abundar Pep match Abundar Pep match se Abundar Pep match Abundar Pep match Protein Overview Peplide peptide F067909 count F067911 count F067900 count F06701 count esa giia 6 e 73857 a
41. The purpose of a Data Mixer is to compare join data from different tables To send data to the data mixer you can use the dedicated button that you can find in the toolbar of all views Peptide Score MsQuery Rank Calc Ma i 1 lt 21 38 113 64 T E CO 67 a E ea When you have sent data from two different views in the following example from the PSM view of two different identification summaries You obtain a new window with the two tables linked and you can apply a difference algorithm or a join algorithm Difference Algorithm For the difference algorithm when a key value is not found in one of the data source table the line is displayed as empty For numerical values a difference is done and for string values the lt gt symbol is displayed when values are different Id Peptide Score MsQuery Rank Calc Mass Exp Moz Pam Charge Missed Cl lon Parent Int PTM Protein Sets g ld e Peptide Score MsQuery Rank Calc Mass Exp Moz Pam Charge Missed Cl lon Parent Int PTM Protein Sets Tasks Log _x D F067697PSH_w D Foe7es9 PSM _ m F Data mver x MsQuery Rank Calc Mass Exp MoZ Ppm Missed Cl Ion Pare Join Algorithm In the following example we have used the join algorithm and added a graphics thanks to the to compare the scores of the PSM from two identification summaries Tasks tog u F067597P5M_ F067899P5M m F Data ner MsQuery MsQuery Ran
42. Typical Proteins can take some time During the processing Identification Summaries are displayed grayed with an hourglass and the tasks are displayed in the Tasks Log Window Merge Merge can be done on Search Results or on Identification Summaries See description for Search Results merging and Identification Summaries merging Merge on Search Results Projects 2 E TasksLog i ProjectTest 63 id 1 2 n_i 3 Ei 4 O Search Result Di Identification Summary gt D Properties a Tras Add Merge Validate Compare With SC Export Change Description Rename Delete To merge a dataset with multiple Search Results Change Typical Protein Generate Spectrum matches 1 oo an SRR REE Select the parent dataset Right Click to display the popup Click on Merge menu When the merge is finished the dataset is displayed with an M in the blue part of the icon indicating that the Merge has been done at a Search Result level ea ProjectTest 63 All Imported amp P agai be D F0s7897 Merge on Identification Summaries If you merge a dataset containing Identifications Summaries The merge is done on a Identification Summary level Therefore the dataset is displayed with an M in the orange part of the icon a ProjectTest 63 o 2 Identifications lt E All OE B il Trash Data Mixer
43. WARN Unknown data directory ERROR Can t update Proline configuration choice gt gt Loading Proline configuration finished with error gt ERROR Invalid configuration Please edit the Set up Proline Selected configuration file D Dev eclipse workspace proline Proline Admin GUI target config custom_application conf Edit Proline configuration gt gt Loading Proline configuration ct 2014 15 05 35 111 JavaFX Application Thread c panel ButtonsPanel S prolineConfIs0 roline is not set up 2014 15 05 37 432 JavaFX Application Thread INFO c panel ButtonsPanel prolinelIsSetUp uds reachable false 0 2014 15 05 37 432 JavaFX Application T INFO f p a g p ProlineAdminConnection Action gt gt Loading Proline configuration finished with success gt gt Loading Proline configuration success Using the command line interface ProlineAdmin Edit the configuration file config application conf located in the ProlineAdmin folder see Configuring ProlineAdmin section below Then perform the datastore setup by running the dedicated script Windows users The ProlineAdmin program files are located in the admin sub folder of the Proline installation directory You should find the following script in this folder gt setup proliane bat Linux users or manual installation Execute the setup proline sh script located in the folder obtained after Proline Admin GUI archive f
44. YEAST F Pez VLTIREVLGE QGKDVKIIVK IENQOQGVNNF DEILKVTDGV MVARGDLGIE IPAPEVLAVQ KRKLIARSNLA GRPVICATQM LESMTYNPRP TRAEVSDVGN AI LSGETAKGNY PINAVTTMAE TAVIAEQATIA YLPNYDDMRN PRPTSTTE TVAASAVAAV FEQKAKAIIV LSTSGTTPRL VSKYRPNCPI ILVTRCPRAA RFSHLYRGVFE PFVFEKEPVS DWTDDVEARI NFGIEKAKEF GILKKGDTYV SIQGFKAGAG HSNTLOVSTV MW n c ter pm JN aa pm Jl c ter ana AA P Coverage 82 40 View 1 at the top list of all Protein Sets Note In the column Proteins 8 2 6 means that there are 8 proteins in the Protein Set 2 in the sameset 6 in the subset View 2 list of all Proteins of the selected Protein Set View 3 list of all Peptides of the selected Protein View 4 Protein Sequence of the previously selected Protein and Spectrum of the selected Peptide Note Abbreviations used are listed here Display Additional Informations on Search Result Identification Summary Functionality Access To display properties of a Search Result Identification Summary right click on a Search Result Identification Summary click on the menu Properties Note it is possible to select multiple Search Results Identification Summaries to compare the values projects le Broectest OAH Identifications oO Search Result Identification Summary Add Merge Validate Change Typical Protein Generate Spectrum matches Compare With SC Change Description Renam
45. abel free LC MS data processing 1 Generation of the LC MS maps LC MS maps can be imported from files generated by others peak picking LC MS tools or directly created through Proline with its own feature extraction algorithms 2 Feature clustering Maps generated with peak picking algorithms cannot be 100 reliable and often contain redundant signals corresponding to the same compound Furthermore modified peptides having the same sequence can have different PIM polymorphisms that can give different MS signals with the same m z ratio but having slightly different retention times Comparing LC MS maps with such cases is a problem as it may lead to an inversion of feature matches between maps Creating feature clusters is a way to avoid this issue This operation is called Clustering cf figure 2 RT 4 features found by a peak picking software G D ae 3 isotopes of a same ion lt gt 1 feature l Mii LD Time between features gt tolerance gt No clustering m z Time between features lt tolerance gt features are clusterized Figure 2 grouping features into cluster All features with the same charge state close m z ratio and retention times are grouped in a single cluster The other features are stored without clustering The processing consists of grouping in a given LC MS map the features with the same charge state close in retention time and m z ratio Default tolerance
46. all Proteins containing the currently selected Peptide Note Abbreviations used are listed here Proteins Window If you click on Proteins sub menu you obtain this window TasksLog s2 F067897 Proteins s LS be lE Ea Ea 761i TZ s ooo a gt ooo y ee eo rr E E E E E E E D E E a E S ooo B ii ES F Ekl F 1 HILSIK i 30 48 1912 1917 274 2 709 45 355 73 0 13 Ep TT m aaa a a a aT E E S 5 O E E E E E Ko 5 R 13 R o i i4 Ro o i EWG 5R FE sn on mt 16 E er er e a O e 17 lt peski va as s a OO a al o A O 2 isik_ s LYVPPADNK Hs 38 85 206 214 assa al 1015 53 s5os 77 oas 2 SoS Upper View list of all Proteins Bottom View list of all Peptides of the selected Protein Note Abbreviations used are listed here Display PSM Peptides or Protein Sets of an Identification Summary Functionality Access To display data of an Identification Summary right click on an Identification Summary click on the menu Identification Summary gt and on the sub menu PSM Peptides or Protein Sets 4 F067 Identification Summary b PSM 0 Replicate Properties Peptides Trash Add Merge User Defined Validate Change Typical Protein Generate Spectrum matches Compare With SC Export Change Description Rename Delete EMS PSM Window If you click on PSM sub menu you obtain this window N
47. as enabled the development of label free quantification methods This methodology is easy to implement as it is no longer necessary to modify the samples it allows an accurate quantification of the proteins within a complex mixture and it considerably reduces the cost of the analysis An LC MS MS acquisition can be seen as a map made of all the MS spectra generated by the instrument This LC MS map corresponds to a three dimensional space elution time x m z y and measured intensity Z m z oe 7 5 he APA oe mere ren es ae x ee eT b aie aP e A AR S R I z Se a We EE aran traan cme ats iah A Ae ee ee gt ae f 2 2 oo oo 2A Seg ne 7 Re FO TOE A Kyi otaa SP ne Sut te icma 4 aye tamed o wS lt z s v lt p is demas Temps d lution Figure 3 image generated using MsInspect representing an LC MS map The dashed square up right is a zoomed view of the map and gives an idea of the data s complexity The blue points correspond to the monoisotopic mass of the peptide ions Analyzing MS data can be done in several ways e Un supervised approach it consists of detecting peptide signals from a LC MS map cf figure 3 below The detection is done by first using peak picking algorithms then grouping together the peaks that correspond to a same peptide at the same time on the m z scale different isotopes of an isotopic profile and different charge states of a peptide and on
48. ataset Explorer Line and click on Install Application Library App Library Installed Apps refresh SpecLight Marc Dubois MS Angel Julie Poisat A message should inform you that the Application has been well installed and it should now appear in your start menu Open it from the start menu by clicking on it amp admin Apps 4 Change Password SpecLight a 4 Administration MS Angel Logout Start Click on New Project and set a name and a description optional for it __ Dataset Explorer p New Project an Reload 8 Settings Dataset tree pod My Projects Once your project is created you can see it in the tree on the left of the screen Click on it to make its options panel appear __ Dataset Explorer amp X EE New Project T Reload 8 Settings Close All Tabs Dataset tree amp MyProjects Project Test B amp My Projects o Q Import Result File F Select All Identification Summaries Edit Project Infos 88 New Quantitation SB Share with Users 3 Test aL ee Identifications Quantitations Trash Identification Trees 4 gi Quantitations Search Results Exported Files ID Name Description Fractions Creation Import Result Files The first thing to do in your brand new project is to import Result Files To do so Click on the Import Result File Button in the toolbar of the project overview window or via a right click o
49. aunch tasks and visualize their data Proline Studio which is a rich client interface and Proline Web the web client interface An additional component is used by administrators to setup and manage Proline called ProlineAdmin Setup and Install Read the Installation amp Setup documentation to install start the different modules used by Proline or upgrade your installation with a newer version Getting Started Discover Proline s workflow and how to execute it with Proline Studio and Proline Web How to Find quick answer to your questions in this How to section Concepts Read the Concepts amp Principles documentation to understand main concepts and algorithms used in Proline Releases Both interfaces Studio and Web are based on a set of databases Raw file conversion to mzDB This procedure is detailed in the mzDB Documentation section Installation amp Setup This page gives you a short overview of Proline components architecture and explains how to install and setup the different components Architecture Overview The suite is based on different components see figure below e A Relational Database Management System Proline Datastore storing the data used by the software in different databases e lt A web server Proline Server handling processing tasks and web data access e Two different graphical user interfaces both allowing users to launch tasks and visualize their data o ProlineStudio which is a r
50. bundances Rename Delete You can delete a XIC Design see how to Delete Data AIC EXHAUSTIVE MECN INTENSITY false true true 5 0 PPM 5 0 PPM 15 0 MOST_INTENSE MOST_INTENSE 20000 TIME ANECA 4 200 20 50 5 0 PPh 600 0 INTENSITY ST 0 0 A m You can rename a XIC Design by clicking on Rename in the popup menu You can export the XIC results see how to Export a XIC Refine Protein Sets Abundances Advanced Protein Sets abundances Right click on the selected XIC node in the Quantitation tree and select Refine Proteins Sets Abundances T Quanttatons i Spi Properties Sae Trz Refine Protein Sets Abundances Display Abundances Display C Export Abundances Rename Delete Configuration ka Refine Proteins Sets Abundances E x Use Only Specific Peptides Discard Missed Cleaved Peptides Discard Oxidized Peptides In the dialog you can specify peptides to consider for quantitation configure parameters used for peptides quantitation configure parameters used for proteins quantitation For more details see Post processing of LC MS quantitative results Advanced XIC results You can see the results by displaying the XIC Display a XIC or export them Export a XIC Proline WEB Server Connection Prerequisite You must have an account to login to the server Ask your administrator to create one i
51. can be used only for Search Results with Decoy Data Validation Processing ProjectTest id Category Task Description i i Z 3 Services Validation of Search Result F067899 t ae I 12 Services validation of Search Result F067898 E Al Imported 11 Services _ Validation of Search Result F067897 B D Replicate 1 o 10 Database A Load Search Result and Identification St F067897 wa Database A Load Data for Dataset Replicate3 g 0 Replicate a Database A Load Data for Dataset Replicate LB BEES a Data for Dataset Replicate 1 8 0 Replicate a Load Data for Project ProjectTest a F0578399 5 tee bow Data for Project ProjectTest 7 fl Trash Load Projects for User menetrey joe atabase A Connection to UDS Database Validating a Search Result can take some time While it is not finished the Search Results are shown greyed with an hour glass over them The tasks are displayed as running in the Tasks Log Dialog Validation Done projects Sn ProjectTest to Identifications P All Imported amp Replicate1 O amp T Replicate2 O D Replicate3 OR ka i Trash When the validation is finished the icon becomes orange and blue Orange part corresponds to the Identification Summary Blue is for the Search Result part Change Typical Protein of Protein Sets The protein sets windows are not updates after a Change Typical Protein You should
52. ch an MS Queries viewer tab focused on the corresponding ms query a filter will automatically applied will be opened If you double click on a protein match a Proteins viewer tab focused on the corresponding protein will be opened Validated pe 0 0 0 0 0 MS Queries Table The MS Queries table displays the MS Queries of the Result Summary and offers the same filters options as the others tables Infos Proteins Peptides ip MS Queries MS Query LLSAREK a Protein SPT2_DROME a Filters MS Queries Numeric Data Text Data Boolean Data MS Query ID Initial ID m z Charge Peptide Matches MS Level 1 1 1 304 69608 2 0 2 2 j 2 318 19611 2 0 2 4 3 3 326 70923 2 10 2 4 4 4 346 70810 2 10 2 5 5 5 348 72177 2 8 2 6 6 6 360 20605 2 2 2 7 7 7 362 22202 2 0 2 8 8 8 362 22235 2 1 2 9 9 9 364 72186 2 6 2 f2RemovesAll a NnD Apply 40 40 10 364 72348 2 5 2 Filters MS Query Peptides Matches ee cette Siena Rank Peptide ID Peptide Match Experimental Charge Sequence Missed Cleavi Score Is Validated Proteins 1 6 3909 4508 348 72 2 NPKPIK 0 0 00 NO 1 2 1 3701 4373 348 72 2 NKPIPK 0 4 00 NO 1 3 2 3499 4735 348 72 2 NKPLPK 0 4 00 NO 1 4 5 3390 3234 348 72 2 NPKIPK 1 0 00 NO 2 5 8 3025 3223 348 72 2 NPPIKK 1 0 00 NO 1 6 7 3675 2754 348 72 2 NPKPLK 0 0 00 NO 1 7 4 2736 3110 348 72 2 NPIPKK 1 0 00 NO 1 8 3 4029 3103 348 72 2 NKPPLK 0 4 00 NO 1 Ja Remove All 0 AND 7
53. characters e can replace one character In the following example the user search for a ProteinSet whose name starts with DNAR File Window Help s F072075 Protein Sets A My Projects Protein Set Description c myProject H C testbetas y DNAK tr HOQ6U8 Hogi LE All Imported F ICSA093 GLPK FOG 7955 3 EFG_ECODH sp B1X6J0 EFG_E B j aggt 4 DNAK_ECOLI sp POA6Y8 DNAK 5 C5A050_ECOBW tr CSAOSO CSA0 ar e TNAAECODH BDTI Io 5 IX7N8_ECODH tr B IX 7N8 B 1X7 7 iB nj as s cama ecoaw fricazmnacazn os 9 Hoqsoa ECoLT SCSC QD SIHOOS Gf repeats in csaI62_ECOBW er ICSAI62IC5A1 a7 replicates n e econ bpene a A Trash B1X9X8_EC tr E 1 9X8 B 1X9X You can do an incremental search by clicking again on the search button of the floating panel Graphics Create a Graphic There are two ways to obtain a graphic from data e Inthe windows with PSM of a Search Result or of an Identification Summary you can ask for the display of a histogram in a new window to check the quality of your identification e In any window you can click on the button to add a graphic Scatter Plot or Histogram as a view in the same window Tastes al Fror Calc Mass Exp Moz Ppm 647 40 643 41 0 42 1 647 40 648 41 fh i 647 40 643 41 0 42 1 699 45 350 73 0 28 2 699 45 390 73 0 28 2 sasas 350 75 oa AO O oOo ao s i s an o O E o 4 E a Se oo je ff SSe ww 2 LIFAGK 3
54. close and reopen the window W Open the Dialog PA ProlineStudio 0 1 2b File Edit View Tools Window Help entiation s oo E R My Projects Your project name Ey Replicate1 H F Replicate Search Result El FOIE Identification Summary By Replicate pee ESET 114 Properties abas it TRASH abas Add Merge abas Validate abas Change Typical Protein as A ro abas Compare With C bas Renare abas lename abas Delete To change the Typical Protein of the Protein Sets of a Identification Summary Select one or multiple Identification Summaries Right Click to display the popup Click on Change Typical Protein menu Dialog Parameters BA ProlineStudio 0 1 2b File Edit View Tools Window Help entincations Tesstog a My Projects Your project name id Category Task Description F All Imported v 69 Services alidation of Search Result F071425 ay Replicate 1 6 Services Validation of Search Result F071424 50 Database A Load Search Result and Identification Summar You can set the choice for the Typical Protein of Protein Sets by using a match string with wildcards or on Protein Accession or Protein Description Three rules could be specified and they will be applied in priority order In a Protein Set if no proteins satisfy the first rule the second one will ne tested and so on Processing The modification of
55. ct the Export Menu P ProlineStudio Beta File Window Help Identifications Tasks Log A My Projects id E Your Project name E All Imported 10 R Replicate 1 w la Riss J 1654 3 va ir z Search Result 7 Pa Replicate Identification Summary le Trash Properties E 4 Add E Merge E Validate i Jo Change Typical Protein Export Rename Delete An Export Dialog is opened you can select the file path for the export and the type of the export only Excel xlsx is available for the moment You can select the Export All PSMs option to add a sheet with all PSMs for each Protein Set latabase A Load Vata for Dataset gradient VELUS 7 paisse oad Quantitation Data for Project WP4 PIE oad ldaentiicaton Date tor Proj CEP Description of exported file is available here 4 Export an Image To export a graphics click on the Export Image Button at the left top of the image TasksLog FO66488P5M MsQu Rank Calc Exp Delta Charge Misse Ion Pa 1 516 25 519 26 0 000 ae ERE O RERS ex s s if enal estoos a oo OO m s 39a ena esoo a o mx sa s enal eesto 1 oo O mm 07 a i esl eeose i oo a eT a N Delta Moz Histogram Mean 2 445620038 109993E 5 sidev O 00231622506509342 5 0 008 0 007 0 006 0 005 0 004 0 003 0 002
56. d decoy are sorted by their score A rank Mascot pretty rank is computed for each PSM depending on their score position PSM with almost equal score difference lt 0 1 are assigned the same rank All PSMs with rank greater than specified one are invalidated Minimum Sequence length Filter PSMs corresponding to short peptide sequences length lower than the provided one can be invalidated using this parameter Mascot eValue Filter Allows to filter PSMs by using the Mascot expectation value e value which reflects the difference between the PSM score and the Mascot identity threshold p 0 05 PSMs having an e value greater than the specified one are invalidated Mascot adjusted eValue Filter Proline is able to compute an adjusted e value It first selects the lowest threshold between the identity and homology ones p 0 05 Then it computes the e value using this selected threshold PSMs having an adjusted e value greater than the specified one are invalidated Mascot p value on Identity Filter Given a specific p value the Mascot identity threshold is calculated for each query and all peptide matches associated to the query with a score lower than calculated identity threshold are invalidated When parsing Mascot result file the number of PSM candidate for a spectra is saved and could be used to recalculate identity threshold for any p value Mascot p value on homology Filter Given a specific p value the Mascot homology thres
57. ds_db or path to the SQLite file D path to uds_db sqlite host Postgre SQL erver adress or name sport port of the Postgre SQL server The host and port params are not needed if you use a SQLite database Here are some config examples dbi Pg dbname uds_db host localhost port 5432 dbi SQLite dbname D proline data uds_db sqlite data_root is the directory where the user projects data will be stored You can use something like D proline data Make sure this directory exists on your disk Proline won t create it automatically if it doesn t ps_db_dsn is equivalent to the uds_db_dsn param but points to the PS Database Set up the connection config the same way you did it for the UDS Database pwd_mascot_data_root is the Directory where the PWX server will browse mascot result files Set it up with a simple path like D proline mascot data or a network adresse like servername mascot data Make sure this directory exists on your disk Proline won t create it automatically if it doesn t o pwc_mascot_data_root is the path from which the Proline Web Core will access this directory You should leave it blank if you configured it in the Proline Web Core config file result_files gt root_folder param If you didn t specify any path there then you must enter one here in the Proline Service config This param can also be useful if you installed PWX and Proline Web Core on two different servers and if one of them runs on Linux for exam
58. e Decoy Searches Proline handles decoy searches performed from two different strategies e Concatenated searches o A protein databank is created by concatenating target protein sequence to decoy protein sequence Deoy could be created using reverse or random strategie From Mascot or OMSSA point of view a unique search is done using that databank e Separated searches o Two searches are done using the same peaklist one on a target protein databank and one on a decoy protein databank These searches are then combinated to retrieve usefull information such as FDR Mascot allows user to check a decoy option and will automalically create a decoy databank Decoy and Target Search Result e Concatenated searches o When importing search result from a decoy concatenated databank decoy data are extracted from the Result File and stored in Proline databases as an independant unique Search Result as well as target Search result data These both searches are linked to each other e Separated searches o The two performed searches are stored in Proline databases and are linked together See Search Result to view which information 1s saved Identification Summary An Identification Summary aka ResultSummary is a set of identified proteins inferred from a subset of the PSM contained in the Search Result The subset of PSM taken into account are the PSM that have been validated a filtering process example PSM fulfilling some spec
59. e Delete Properties Window Properties F067897 amp Data Set id dentification Summary ResuttSummary id Description Protein Sets Count Is Quantified Date validation_properties params peptide_expected_fdr params peptide_filters 1 parameter params peptide_filters 1 description params peptide_filters 1 properties threshold_value params peptide_fitters 2 parameter params peptide_filtters 2 description params peptide_filtters 2 properties threshold_value results peptide_results target_matches_count results peptide_results decoy_matches_count results peptide_results fdr results protein_results target_matches_count results protein_results decoy_matches_count Search Result ResultSet id Name Description PSM Count Protein Match Count EIS Properties target_decoy_mode mascot_import_properties ions_score_cutoff mascot_import_properties subsets_threshold Decoy Search Result ResultSet id Name Description PSM Count lt null value 9 septembre 2014 5 0 RANK peptide match rank filter SCORE peptide match score filter 20 69 9346 339 CR_V P2112 Calib 25 10 12 Col reprosil 172 lt null value 40701 6389 CONCATENATED 0 0 1 0 14 CR P2112 Calib 25 10 12 Col reprosil 172 lt null value ATITOT TTA LOI ID ID ID SID SID SID D D D D D E D OD Hoo Ob Display a
60. eatures from 2 different maps of the map set delta time between features must be lower than the time tolerance to be considered as the same feature seen on 2 different maps Normalization method sometimes the ratio distribution is not centered around zero as we could have expected if data were exactly reproducible Intensity normalization by applying a mathematical transformation is thus needed to reduce the impact of experimental artifacts and ensure accurate quantification Three methods are available 1 Median ratio normalization method algorithm first compute sum of feature intensities for each map of the map set and sort maps by computed intensities The map ranking nearest from the median is taken as the reference map Then for each master map feature compute ratio as reference map feature intensity feature intensity for the considered map The normalization factor corresponds to the median of the computed ratios 2 Median normalization method algorithm first compute median intensity for each map set the reference map to median map normalization factor for map M reference map median intensity map M median intensity 3 Sum normalization method algorithm first compute feature intensities sums for each map set the reference map to the median map normalization factor for map M intensities sum of reference map intensities sum of map M Master feature filter type a filter can be applied to the map features to keep the be
61. eries MS Query LL Duplicate El Mowe to Trash qe Export IO Change Name Summary gt Validation Params Peptide Filters Peptide Expected FDR 5 RANK Description peptide match rank filter gt Threshold 1 00 MASCOT EVALUE gt Description peptide match mascot e value filter Threshold 0 13 Protein Filters protein set validator expected fdr 1 E Results Peptide matches results FDR 5 56 Target peptide matches count 15 Decoy peptide matches count 1 Protein set results Target protein sets count amp Decoy protein sets count 0 r Infos ProjectID 5 RSMID 2 Summary and a grid listing all of the identifications of this aggregate Export Data To export any of the Result Summary data table just click on the save icon on the top of the table Please note that any current filter applied to the table will be applied to the exported data OUO g Proteins Peptides MS Queries Filters Proteins Numeric Data Text Data Boolean Data 888 Toggle Grouping I Export Accessions ID Group Accession Prot ID Description Coverage Protein Ma Protein Sei Val Pepti Is Validatet Is Selected Group 1 3668 062823 sp O062823 CASA1_B 3979 P02662 sp P02662 CASA1_B 6056 P04653 sp P04653 CASA1_S 5210 P18626 sp P18626 CASA1_C Group 2 Ja Remove All AND JP Apply 5 4060 P33048 sp P33048 CASB_CA Filters 3 Peptides Nume
62. es shared between different protein sets will be discarded from the statistical analysis e Discard missed cleaved peptides if checked peptides containing missed cleavages will be discarded from the statistical analysis It has to be noted that perfect tryptic peptides whose sequence is included in an observed missed cleaved peptide are also discarded if this option is enabled e Discard oxidized peptides if checked peptides containing the Oxidation M modification will be discarded from the statistical analysis It has to be noted that non modified peptides whose sequence is the same than an observed oxidized peptide are also discarded if this option is enabled Peptide and protein common parameters e Normalization the normalization factors are computed as the median of the ratios distrubutions between each run and a run of reference A similar procedure is used for the normalization of LC MS features Aggregation of peptides in proteins Peptide abundances can be summarized into protein abundances using several mathematical methods e sum for each quantitative channel raw file the sum of observed peptides abundances is computed e mean for each quantitative channel raw file the mean of observed peptides abundances is computed e mean of TOP3 peptides same procedure but applied on the 3 most abundant peptides Peptides are sorted by descending median abundances computed accross all compared samples for peptide Then the 3 fir
63. f you don t have any After the installation the default account is admin with password admin Open your Google Chrome web browser and connect to the address of the server ask your administrator iPB a 9 B Name nicolas Password Enter your username and password and click OR To create a project please follow the instructions detailed on this page Register Raw amp MzDB Files In order to create and run Quantitation analyses you must register your RAW files and corresponding MzDB files into Proline databses to do so click on the Settings button in the top bar of the Dataset Explorer application and go to the Raw File Registerer tab Settings Instrument Settings Peaklist Software Protein Match Decoy Rules Raw File Registerer Batch Raw File Editor Raw amp MzDB File Selection Add Raw Files to Selection Putin Couple Make Couples from Raw Files Remov Add MzDB Files to Selection gt Put in Couple Remove Selection Clear Path Name Path Name raw_files_root marc OEMMA121101_50_copy OEMMA121101_50_copy raw mzdb_files_root marc OEMMA121101_50_co OEMMA121101_50_copy raw mzdb raw_files_root marc OEMMA121101_56_copy OEMMA121101_56_copy raw mzdb_files_root marc OEMMA121101_56_co OEMMA121101_56_copy raw mzdb Raw amp MzDB Couples Remove Selection Clear Raw File MzDB File Instrument Names Owners Register e In the left grid use the
64. fic coordinate on the LC MS map and giving the corresponding XIC BE AIC of OTOP MS Expenrant 1 666 2 te 046 2 ares hin OTACOSIODIIOD et a7 2100 oo soi ww i 005 aol f mwi Si y5 y y Eor 38004 501 2055 ani xo 327 1923 900 2035 12 2076 83 2500 AO 2551 599 2400 wie 400 5i 500 550 600 650 ae mz amu sf j 4 A i ae a 25 x a a 1130 LAPA 1303 128 1208 Time tin MS MS spectrum of AIILAAAPGEK XIC of m z 919 514 at 31 8 min AL o _ o Figure 4 Extraction of the MS signal of a peptide previously identified using a search engine The first approach is more exhaustive than the latter as it can find quantitative information on peptides that may not have been fragmented by the mass spectrometer About the second approach we can only assume that knowing the peptide s exact monoisotopic mass should reduce the probability of making mistakes in the quantification but no study to our knowledge has proved it so far In a comparative quantitation analysis both approaches require the matching of the extracted signals cf figure 5 To do this the LC MS Maps have to be previously aligned in order to correct the variability coming from the peptide s chromatographic elution Indeed the difference for the elution time of a given peptide in two LC MS analysis may reach tens of seconds Even if a peptide mass can be precisely measured it is still possible that peptides with very close m z elute at the sa
65. file CONCEPTS Proline Concepts amp Principles e Dataset types o Result File o Search Results o Decoy Searches o Identification Summary e Data Processings o Protein Inference o Protein and Proteins Sets scoring o FDR Estimation o Validation Algorithm o Merge multiple Search Results o Merge multiple Identification Summaries o Compare with Spectral Count o Quantitation Principle LC MS quantification LC MS quantification workflows mzDB processing Label free LC MS quantitation workflow o Quantitation Configuration Label free LC MS quantitation configuration Post processing of LC MS quantitative results e Data Import Export o Identification Summary Export Result File A Result File is the file created by a search engine when a new search is submitted OMSSA omx files and Mascot dat files search engines are currently supported by Proline A first step when using Proline is to import Result Files through Proline Studio or Proline Web Search engines provide different types of searches for MS and MS MS data It is important to highlight that the Result File content depends on the search type Thus Mascot searches must be currently performed using MS MS ions search in order to be properly imported by Proline Peptide Mass Fingerprint and MS MS error tolerant searches will be supported in further versions of Proline Search Result A Search Result aka ResultSet in the database schema is the raw
66. g a single average threshold this will reduce the number of G2 validated proteins leading to a decrease in sensitivity for a same value of FDR In the future we will try to implement such a strategy in order to allow the user to make its own comparison Search Result and Identification Summary A Search Result aka ResultSet in the database schema is the raw interpretation of a given set of MS MS spectra given by a search engine or a denovo interpretation process The content of a Search Result is one or many peptides matching to the submitted MSMS spectrum PSM each peptide being issued from protein sequences The Search Result also contains additional information such as search parameters used data bank etc A Search Result is created when a Result File Mascot dat file or an OMSSA omx file is imported in Proline in the case of a target decoy search two Search Results are created one for the target PSM one for decoy PSM An Identification Summary aka ResultSummary is a set of identified proteins inferred from a subset of the PSM contained in the Search Result The subset of PSM taken into account are the PSM that have been validated a filtering process example PSM fulfilling some specified criteria such as score greater than a threshold value Content of a Search Result Importing a Result File creates a new Search Result in the database which contains the following informations e Search Settings software name and ver
67. h result only Not possible for a dataset Choose the menu option te Identifications zi 125 af a MonDataset 120 F156691 Recover QP F155122 TMPP C13KY 1i 155122 TMPP aiii D Fos0415 Search Result b be il Trash Identification Summary Properties Add Merge Validate Change Typical Protein Generate Spectrum matches Compare With C Export Compute statistical reports M5Diag Change Description AN Rename Delete You then configure some settings before launching the process Score windows x 20 4030 mek DS Your report will appear in a matter of seconds depending of the amount of data to be processed Wu Number of matches per group of scan and score uu Number of matches per minute of retention time and score i Number of matches per resultset and score ul Number of matches per charge and score 0 Repartition of assigned and unassigned spectra ep PE Charge Unassigned Score lt 20 0 20 0 lt score lt 40 0 40 0 lt score lt 60 0 Score gt 60 0 You have other types of display that are possible Histograms Cii Number of matches per resultset and score U Number of matches per group of scan and score Number of matches per minute of retention time and score Matches B Unassigned Score lt 20 0 20 0 score lt 40 0 40 0 score 60 0 E Score 60 0 And pie charts Repartition of assigned and unassigned spectra Assigned 87
68. he Proline installation directory Linux users manual installation This module 1s distributed as an archive file embedded in Proline Server archive and need to be extracted in your preferred folder to be installed Configuration Configuration files are located under the lt seqrepo_folder gt config Datastore description pg_uds properties file define datastore description to access to the UDS database for postgresql database jJavax persistence jdbc driver org postgresql Driver jJavax persistence dbc url jdbc postgresql lt host gt lt port gt lt uds db gt Javax Persistence JIDO user lt user proline jJavax persistence jdbc password lt proline user password gt Note e If you didn t change the default naming scheme of databases the lt uds_db gt uds_db so jJavax persistence dbc url jdbespostgresql lt host gt 5432 uds db e proline_user_password and user_proline are the same as specified in application conf for Proline Admin or Proline WebCore Protein description parsing rule As this module is used to extract Protein Sequence description from a fasta file for a specific protein accession it is necessary to configure the rule used to parse the protein ACC from fasta description line This is similar to the rules specified in Mascot Server To do this retrieve service properties file should be edited In this file it is necessary to escape this means prefix with some characters
69. heck Files before Import let this checked to ensure that your files contain no errors The server will perform a check operation before launching the import In order to add files to your import selection click on Select Result File to open the File Browser that will let you choose one or many result files to import File Browser x Sj Result Files Name Ext Size Last Modif B eps L m F054972 dat dat 488383282 Ko 9 11 2012 20 53 58 em e ia te a eee cs oe tan eee J raw Acocks E E ea SIU PE aOR E 1 OEMMA121101_50_ copy raw raw 1688547295 Ko 5 11 2012 12 13 15 1 OEMMA121101_50_copy raw mz mzdb 1042788352 Ko 16 12 2013 16 48 23 1 OEMMA121101_56 copy raw raw 1706168717 Ko 5 11 2012 20 44 49 OEMMA121101_56 copy raw mz mzdb 1050124288 Ko 17 12 2013 10 55 59 File Type v Ok The left side let you browse the directories and when you click on one of them its content is shown on the main panel Choose one or multiple files then click Ok Back to the Import Result File window you should now see your selection appear in the grid Choose the instrument and the peaklist software corresponding to your files and then select a Decoy Strategy You can now click on Start Import to launch the check and the import tasks The server will check your files first then the import itself will be launched automatically You can follow the current state of your tasks by clicking on the small cake in the bottom
70. hold is inferred for each query and all peptide matches associated to the query with a score lower than calculated homology threshold are invalidated Single PSM Per Query Filter This filter will validate only one PSM per Query To select a PSM following rules will be applied For each query e Select PSM with higher score e If several PSM with same score o Choose PSM which identify the protein which have the max nmber of valid PSM o If still equality Choose the first PSM E l Dror testing purpose it is possible to ask for this filter to be executed after Peptide Matches Validation see bellow In this case the requested FDR in validation step will be modified by this filter This is just to confirm the need or not of this filter and to validate the way we apply it Single PSM Per Rank Filter This filter will validate only one PSM per Pretty Rank If you choose this filter a Pretty rank filter you should have the same behaviour than the Single PSM Per Query Filter In order to choose the PSM following rules will be applied For Pretty Rank of each query e If several PSM o Choose PSM which identify the protein which have the max nmber of valid PSM o If equality Choose the first PSM W filter is actually in test with Single PSM Per Query Filter Finally a decision of with filter and how it is apply will be taken Peptide Matches Validation Specify an expected FDR and tune a specified f
71. ic elution peaks of the monisotopic mass are really corresponding to monoisotopic masses 1 e 1f no elution peak P is present before the considered monoisotopic mass M that has a difference of mass equal to 1 0027 z z being the charge of M having a distance apex to apex P vs M lower than a user defined threshold of number of cycles default value is 5 a Pearson correlation higher than a user defined threshold default value is 0 7 and finally a P M area ratio agreeing with the predicted value for P using averagine If needed a filter of the duration of a peptide signal which is usually peptide specific Considering the signals close to each other in time elution time at the apex vs predicted time Consider the signals close to each other in m z ratio Label free LC MS quantitation workflow Analyzing Label free LC MS data requires a series of algorithms presented below mzdb processing Feature plusieurs XIC MS peptide MsInspect Progenesis Fichiers Decon2LS bruts Analyse Algorithme d alignement de cartes gt Tol MS1 Cartes LC MS Tol rance de m z et de temps gt Lissage de l alignement smoothing gt Choix d une carte de r f rence Traitement des cartes gt Clustering gt Normalisation sur l intensit Delta RT RT Cartes LC MS retrait es Construction de la master map Filtrage seuil d intensit Alignements Master map Figure 1 overview of the differents stages of l
72. ically identical to its unlabeled counterpart Therefore both peptides behave identically during chromatographic separation as well as mass spectrometric analysis from ionization to detection As it is possible to measure the difference in mass for the labeled and unlabeled peptide with mass spectrometry the quantification can be done by integrating and comparing their corresponding signal intensities cf figure below Peptide l ger 100 Extracted Ion Chromatogram XIC 90 Peptide lourd pe o 80 a Ea A B 70 D 60 z O 30 B d lt 20 10 0 LS S a g 869 870 871 872 873 874 875 876 37 36 m z Temps min Figure 2 Extraction of quantitative data from a mass spectrum On the left the visualization of the isotopic profile for each peptide labeled red and unlabelled black On the right the chromatographic peak reconstruction by extracting the signal of the peptide throughtout the duration of the analysis The integration of this peak gives a proportional value to the abundance of the peptide Here the measurement of the areas shows that the abundance of the labelled peptide is 85 that of the unlabelled one Isotopic labeling strategies are very efficient but limited by the maximum number of samples that can be compared eight samples at most for an i1TRAQ 8plex labeling the cost or the constraint due to the introduction of the label The development of high resolution instruments such as the LTQ Orbitrap h
73. ich client interface o ProlineWeb the web client interface e A daemon application Proline Sequence Repository automaltically filling proteins sequences repository from fasta e A system administration application ProlineAdmin to setup and manage the Proline suite This application is available as a command line applicatin or with a graphical user interfaces Sequence Repository Proline components Proline DataStore Proline stores data in six different database schemas Three of them are core database schemas created once at datastore initialization This three databases contains data related to users projects UDS database peptides sequences and post translational modifications PS database and proteins and databank PDI database The Seq database where protein ID and sequence are store is automatically created when running the associated daemon application Sequence Repository This database is needed to have protein sequences and descriptions in user interfaces The PDI database with more information than the sequence database is not available yet The two additional schemas are used to create a new database each time you create a new user project This databases store identification data MSI databases and quantification data LCMS database associated with users projects Requirements The server centric architecture of Proline imposes different requirements for the server computer and the client computers e
74. ied and quantified Peptides non identified but quantified peptides identified but not quantified peptides linked to a quantified protein ee a Eacroses a Eces a De ptides 4893 a Abundanca Pep match a Abundance Pep match a Abundance Pep match m Abundance m zm match Foe7909 COL Foe7o1il count Fo6 7900 Fo6 7901 4655 FSNPAIODTVAR 3659211 50 ela 280 77 25 4659 PLERIN n 1352 Fe oo 3 1700508 88 1 1357973 88 e E E ES 10376421 1724539 12 11509 oo samme amen 13190851 PESTS Tim ean measer segizez a sizing sensi 0 aepoan7s ise01so arazmese E sg ee ee ee pat a ol p F 15362747 00 13190 00 0 he i JRE Ik JHE e F067911 FO67901 IGSIVANR 2 415 2 213 04 soe eo oe 9 29 75 88 1302747 00 Display Peptides Ions By clicking on Display Abundances Peptides Ions you can see all identified and quantified Peptides Ions non identified but quantified peptides Ions TasksLog s XIC Protein Sets XIC Peptides se XIC Peptides Ions s tbe eptides Ions 4616 ia ee Elution a Abundance Fep match a Abundance Pep match a Abundance Abundance F a te Sue E time 5 FO67909 count F067911 count F067900 FOS 7901 25 EIYDQYGLEAAR 2 714 34 3162 22 3126134 50 1 3266753 25 1 3413756 00 3107582 25 a yy 2 eesie 2 ow E asc uae aa A manl sso oas 11 wese 11 seasea 63705200 Oe IE p OATS 3 eanl sonel mi
75. ified criteria such as score greater than a threshold value Content of an Identification Summary Peptide Set Protein Set e Typical Protein e sameset e strict subset e subsumable peptide set Search Results and Identification Summary content Search Result Importing a Result File creates a new Search Result in the database which contains the following informations e Search Settings software name and version parameters values e Peak List and Spectrum information file name ms level precursor moz e Search result data o Proteins o Peptide o Spectrum o 2 type of Matches a Peptide Match is a match between a peptide and a spectrum with score fragment matches a Protein Matches is a match between a peptide and a protein Mascot result importation e The peptide matches score correspond to Mascot ion score OMSSA result importation e The peptide matches score correspond to the negative common logarithm of the E value o Score log10 E value Identification Summary e Protein Set e Peptide Set Protein Inference All peptides identifying a protein are grouped in a Peptides Set A same Peptides Set can identify many proteins represented by one Proteins Set In this case one protein of this Protein Set is chosen to represent the set it is the Typical Protein If only a sub set of peptides identify a or some protein s a new Peptide Set is created This PeptideSet is a subset of the first
76. ify other people to share this new project with them Then click on OK Button Creation of a Project can take a few seconds During its creation the Project is displayed grayed with a small glasshour over it a ProjectTest te Create a Dataset You can create a Dataset to group your data To create a Dataset right click on Identifications or on a Dataset to display the popup click on the menu Add gt Dataset Projects E ProjectTest Be Search Result Identification Summary gt Properties Add gt Search Result Merge Dataset Validate Change Typical Protein Compare With SC Export Change Description Rename Delete F HQuantitations On the dialog opened fill the name of the Dataset choose the type of the Dataset optional click on Create Multiple Datasets and select the number of datasets you want to create a ProjectTest ka Dataset Parameters Name Replicate Type Biological Sample Create Multiple Datasets Let s see the result of the creation of 3 datasets named Replicate I ProjectTest ta Identifications T Replicate 1 D Replicate2 T Replicate3 T i Trash Import a Search Result There are two possibilities to import Search Results import multiple Search Results in All Imported and put them later in different datasets import directly a Search Result
77. ignals containing a strong overlap with the previously extracted signal especially with the first two isotopes The extraction of all the signals corresponding to MS MS events is made in a single iteration on all the runSlices of a mzDB file Also all the peptide signals which mass are contained in the runSlice are detected simultaneously PredictedTimeExtractor algorithm This algorithm is used for cross assignment when a peptidic signal is detected in a file but does not have an equivalent signal in another frequently in DDA In this case the algorithm will try to extract some signal from the file where the signal has not been found The aim of this algorithm is to reduce the number of missing values 1 Extracting a 4 minutes XIC user defined value around l 2 the time predicted by the alignment the ratio m z of the isotope with the highest intensity predicted by the averagine which is estimated from the mean value of the m z of the observed signals in other conditions 2 Peaks are detected with the wavelet based algorithm usually better for a signal made of hundreds of peaks and limits of time are determined The isotopic profiles are extracted for each spectrum using the method as in the MS2Driven algorithm Many peptide signals can be detected and need to be filtered in order to find the best match with the signals in other conditions 3 To do so we verify beforehand that l 2 3 4 The chromatograph
78. il esss3 azer O lesma O o C E E E ser ae E yr a Ma l Chester a Abundana Pep match Abundana Pep match a Abundanc Pep match a Abundance Pep match k F067309 a FO67911 Hit FO06 7900 alee Fo67901 count 1 eae 3341071 00 3 31633 25 i 3538175 50 2 3786233 50 E E E skar e nsa 1 798970400 r 1 somewa fea ao on aa a Peptides Ions 1 i Elution a Abundar Fep Abundar Pep a Abundar Pep a Abundar Pep T time s FO67909 match count F067911 match count FO67900 match count F067901 match count B EREA 68 42 4342 28 384107 3 31763 _ i 353817 2 378623 The overview 1s based on the abundances values For each quantitation channel are displayed The raw abundance The peptide match count by default The abundance by default The selection level By clicking on the Column Display Button I you can choose the information you want to display To display the identification protein set view click right on the selected protein Set and select Display Identification Protein Sets menu in the popup P Quant Abunda Pep a Abundar Pep Abundar Pep a Abundan Pep Piirit Overv Pept Peptide F067909 match count FO67911 match count F067900 match count F067901 match 296 ee ee ee we Fj LT R e wy BPE aP ae SE read we ee ae el eee ee pe ee a 8 1068719 Display peptides By clicking on Display Abundances Peptides you can see identif
79. ile extraction Configuring ProlineAdmin Modify the following lines to fit your DBMS configuration proline contig driver type postgresql valid values are h2 postgresql or sqlite data directory Path to Proline Data Not used actually auth Cconiag 7 user proline user SET TO Database Proline user login password proline user password SET TO Database Proline user password host conrig host your postgresql server host Do NOT put localhost but the real IP address or fully qualified name port 5432 or other port used to access your DBMS Note default naming scheme of databases created by Proline can be modified by editing config application lt dbtype gt conf file Configuring the Proline Server Locating the server folder Windows users The server program files are located in the ws sub folder of the Proline installation directory Linux users or manual installation Open the folder where you have unzipped the Proline Server archive The Proline server folder should contain a sub folder named ProlineWeb Core lt x y z gt Editing the configuration file The configuration file is located at lt proline server folder gt ProlineWeb Core lt x y z gt Proline WEB INF classes Configuring the datastore Edit the application conf file in the same way you did it for ProlineAdmin see Setting up the Datastore If your configuration is valid the Pro
80. ilter in order to obtain this FDR See how FDR is calculated Once previously described pre filters have been applied a validation algorithm can be run to control the FDR given a criteria the system will estimate the better threshold value in order to reach a specific FDR Proline Web installation procedure Protein Sets Filtering Specific peptides Filter Invalid Protein Set that don t have at least x peptides identifying only that protein set The specificity is considered at the DataSet level This filtering go through all Protein Sets from worth score to best score For each 1f the protein set is invalidated associated peptides properties are updated before going to next protein set Peptide property is the number of identified protein sets Protein Sets Val dation Once pre filters see above have been applied a validation algorithm can be run to control the FDR See how FDR is calculated At the moment it is only possible to control the FDR by changing the Protein Set Score threshold Three different protein set scoring functions are available Given an expected FDR the system will try to estimate the best score threshold to reach this FDR Two validation rules R1 and R2 corresponding to two different groups of protein sets see below the detailed procedure are optimized by the algorithm Each rule defines the optimum score threshold allowing to obtain the closest FDR to the expected one for the corresponding group of
81. in a dataset Import in All Imported To import in All Imported right click on All Imported to show the popup click on the menu Add Search Result E ProjectTest B 2 ta Identifications g All Im Display List T Replic Add Search Result D LD Replicates td Trash In the Import Search Results Dialog 99 select the file s you want to import thanks to the file button the Parser will be automatically selected according to the type of file selected select the different parameters click on OK button Note 1 You can only browse the files accessible from the server according to the configuration done by your IT Administrator Ask him if your files are not reachable Look for Setting up Mount points paragraph in Installation amp Setup page Note 2 The Save Spectrum Matches option does no longer exist The Spectrum matches can be generated on demand when the Search Result is imported Note 3 Proline is able to import OMSSA files compressed with BZip2 mascot_data Proline_Tests WP4 F067897 dat mascot_data Proline_Tests WP4 F067898 dat mascot_data Proline_Tests WP4 F067899 dat Parameters Software Engine l Mascot Feaklist Software Mascot Distiller Decoy Parameters Decoy Concataned Decoy Decoy Accession Regex 4REV 22 5 Parser Parameters Ion Score Cutoff 0 0 Subset Threshold 10 Mascot Server URL ind
82. inal step this algorithm checks the matching between experimental and theoretical isotopes ratios 2 Identification based the charge state of each PSM Peptide Spectrum Match is used to combine istotpes signals into an LC MS feature Extraction parameters Parameters use by signal extraction algorithms Extraction m z Tolerance In supervised algorithms this correpsonds to the error tolerance between the precursor ion m z and peaks extracted in the mzDB file In unsupervised algorithms this corresponds to the the error tolerance between each peak apex and other extracted peaks Clustering parameters Clustering must be applied to the imported LC MS maps to group features that are close in time and m z This step reduces ambiguities and errors that could occur during the feature mapping phase e Time tolerance features that are close in time are grouped If delta time between two features is lower than time tolerance features are grouped e m z tolerance features that are close in m z are grouped If delta m z between two features is lower than m z tolerance features are grouped e m z tolerance unit m z tolerance can be provided in PPM or Dalton e Cluster time computation you have the choice between 2 computation methods most intense or median For most intense method the cluster time corresponds to the time of the most intense feature composing the cluster For median method cluster time is the median of the feature times form
83. ing the cluster e Cluster intensity computation you have the choice between 2 computation methods most intense or sum For most intense method the cluster intensity corresponds to the intensity of the most intense feature of features forming the cluster For sum method cluster intensity is the sum of the intensities of features composing the cluster Alignment Computation This is an important step in the LCMS process It consists of aligning maps of the map set to correct the RT values RT shifts of shared features between the compared maps follow a curve reflecting the fluctuations of the LC separation The time deviation trend is obtained by computing a moving median using a smoothing algorithm This trend is then used as a model of the alignment of the compared LC MS maps This model provides a basis for the correction of RT values e Method You have the choice between 2 alignment methods 1 Comprehensive the comprehensive algorithm computes the distance between maps for each possible couple of maps and selects the map with the lowest sum of distances to be the reference map 2 Iterative for the iterative algorithm first a reference map is chosen randomly then each other map is aligned against the reference and the algorithm computes the distance for each couple of maps The map that has the smallest distance becomes the reference map The 2 previous steps are re iterated until either reference map stays the same between two iterat
84. ion2 E fraction1 e f Fo67899 0 Fos7900 Link to Raw Files To be able to perform a XIC design we need to know the source raw files Proline try to find in the database the corresponding Raw Files already registered If a Raw file is not found the icon shows a and you can display the error by expanding the corresponding node In this case you will have to select the Raw File by yourself Note To help you you can display the peaklist tooltip by overriding the Identifcation Node in the Design Tree a Step 1 Drag and Drop Identification Summaries to create your XIC Design 4b Drag amp Drop ts Identifications D exp D condition 1 0P Fos7897 o h F0s7897 E Fos7a98 oP Fo67898 G a condition2 fractioni 7899 a Fo67899 DP Eee c Fos7900 To select a Raw File click on the error and use the following dialog You can select directly a file on the disk or a potential corresponding Raw File registered in the database So XIC Quantitation Wizard db OEMMA121101_40b mzdb OEMMA121101_40ba raw OEMMA121101_40b mzdb XIC Parameters When the XIC Design is finished click on the next button and select the parameters See Label free LC MS quantitation configuration to have more details about the different parameters Note all the parameters are already set with default values o XIC Quantitation Wizard C pi Step 2 Spedfy quantitation parameters XIC Parameters
85. ions or the maximum number of iterations is reached Then all other maps are aligned to this computed reference map and their retention times are corrected Maximum number of iterations this option is available only for iterative method This is a stop condition of the iterative algorithm when the algorithm has reached its maximum number of iterations it stops m z tolerance m z window used to match features between two compared maps m z tolerance unit m z tolerance can be provided in PPM or Dalton Time tolerance in seconds time window used to match features between two compared maps Alignment smoothing When alignment is done a trend can be extracted with a smoothing method permitting the correction of the aligned map retention time Smoothing method you have the choice between 2 smoothing methods time window or landmark range Number of landmarks time interval if selected smoothing method is landmark range time of aligned map is corrected using median computed on windows containing a specified number of landmarks The run is divided in windows of size the specified number of landmarks You have to provide the number of landmarks by window The smoothing method is applied considering the number of landmarks present in the window and computes the median point for this window If selected smoothing method is set to time window time of aligned map is corrected using median in a time window You have to provide the time in
86. is respectively 15 seconds and 10 ppm Some metrics are calculated for each cluster equivalent as those used for the features e Cluster m z is the median of the m z of all features in the cluster e Cluster RT is 2 calculation options o Median median of all the retention times of the features in the cluster o Most intense retention time of the most intense feature e Cluster intensity 1s o Sum sum of the intentisties of all the features in the cluster o Most intense intensity of the most intense feature e Cluster charge state is the charge state of every feature in the cluster e Number of MS in cluster is the sum of the MS1 signal of all features in the cluster e Number of MS2 in cluster is the sum of the MS2 signal of all features in the cluster e Cluster first scan is the first scan of all the features in the cluster e Cluster last scan is the last scan of all the features in the cluster The resulting Maps are cleaner at the end of the algorithm thus reducing ambiguities for map alignment and comparison Quantitative data extracted from these maps will be processes in the following steps It is necessary to eliminate the ambiguities found by the clustering step To do so it is possible to rely on the information given by the search engine on each identified peptide If some ambiguities remain the end user must be aware of them and be able to either manually handle them or either exclude them from the analysis NB do not mix
87. istered yet see how to set up Proline and create a Proline user Create a Proline user Command line ProlineAdmin You can create a proline user with the Proline Admin RunCommand script Open a command line window Maj Right Click on Windows and type the following command e Windows r n Cmd bat create ser L lt user login p lt User Dassword e Linux sii Lun cmd ch create user L lt user Login p lt User password From graphical tool ProlineAdmin GUI You can also use the ProlineAdmin graphical interface open ProlineAdmin GUI and click on the Create user button A new window allows you to set the new user s name and password with password verification T Proline Admin D proline data Edit Proline configuration a Create a new user Create a new project Upgrade databases Refresh DEBUG gt gt Loading Proline cc 03 oct 2014 11 19 15 f p a g c panel Butta 03 oct 2014 11 19 15 f p a gui process Uds 03 oct 2014 11 19 15 f p a g c panel Buttoa 03 oct 2014 11 19 15 f p a g p ProlineAdmi gt gt Loading Proline 03 oct 2014 11 19 15 f p a g c panel Butto 03 oct 2014 11 19 15 f p a gqui process Uds 03 oct 2014 11 19 15 Register f p a g c panel ButtonsPanel _prolinersSetUp uds reachable Refreshed See all users With success Note This functionality will be disable if Proline is not set up see how to set up Proline
88. it T Replicate3 i TEA fitv Drag amp piep Import directly in a Dataset It is possible to import a Search Result directly in an Dataset In this case the Search Result is avaible in All Imported too To import a Search Result in a Dataset right click on a dataset and then click on Add gt Search Result menu projects nal Tass tog B 2 f id Category Task Des w 19 Database A Load All Se 8 powbase A loads Search Result Identification Summary Properties Add Merge Validate Change Typical Protein Generate Spectrum matches Compare With C Export Change Description Rename Clear Delete Delete Data You can delete Search Results Identification Summaries and Datasets in the data tree You can also delete XIC or Spectral Counts in the quantitation tree There are two ways to delete data use the contextual popup or drag and drop data to the Trash Delete Data from the contextual popup Select the data you want to delete click the mouse right button to open the contextual menu and click on delete menu ProjectTest ta Identifications e EB All Imported T Replicate 1 J F057897 D Replicat Search Result D Replicat Identification Summary e f Trash Properties Add p pavio Merge Validate Change Typical Protein Generate Spectrum matches Compare With C Export Change De
89. it is stored in the Proline LCMS database and used to create a QuantResultSummary This object links the quantitative data to the identification data validated in Proline This QuantResultSummary is then stored in the Proline MSI database cf figure below CONVERSION 2557 il ao 1 mzDB file He wth MAARE ss 1LC MS MSrun_ ime 1 EXTRACTION 1 EXTRACTION 1 LC MS map 1 LC MS map 2 COMPARISON 3 SEARCH 1 master map 1 result file aero 4 IMPORT amp VALIDATION 5 QUANTITATION 4 IMPORT amp VALIDATION 1 result summar gt 1 master result summary 1 result summary MSE DB ReGewe Ardre m E poss a ae Figure 9 From raw files to the QuantResultSummary object Label free LC MS quantitation configuration Here is the description of the parameters that could be modified by the user Feature extraction Strategy Defines the algorithms and methods to used for signal extraction and deistotping e Start Extraction from XIC from 1 MS MS Events supervised strategy where each feature extraction is targeted for each acquired MS MS spectrum 2 Validated Peptides same strategy but with a filtering of MS MS events based on the list of validated peptides 3 Raw MS signal analysis unsupervised strategy which tries to detect LC MS features using a signal recognition algorithm e Deisotoping mode 1 Unsupervised an algorithm combining time correlated isotopes elution peaks In the f
90. k Rank Calc Mass Calc Mass Exp Moz Exp Moz Ppm Spectral Count See description of Compare Identification Summaries with Spectral Count Generate a Spectral Count To obtain a spectral count right click on a Dataset with merged Identification Summaries and select the Compare with SC menu in the popup Projects 8 EI Tasks Log I Projecttestio3 DJA te Identifications Search Result Identification Summary Properties Add Merge Validate Change Typical Protein Generate Spectrum matches Compare With C Export Change Description Rename Delete In the Spectral Count window fill the name and description of your Spectral Count and press Next FA Step 1 Define spectral count name and description Spectral Count Name Spectral Count agg Then select the Identification Summaries on which you want to perform the Spectral Count and press OK A Spectral Count window is opened with a label indicating that the calculation is being done and the Spectral Count is added to the Quantitation s Panel I Quantitations a Calculating 1 service s running Awaiting Spectral Count on agg2 Spectral Count Result In the Spectral Count Result Table you will find three types of Spectral Count Basic Specific and Weighted and an overview column to rapidly
91. line Server will be able to use the datastore you ve created using Proline Admin Configuring the mount points Result identification files Mascot dat or OMSSA as well as mzDB files for the XIC Quantitation process are only browsed from Proline Server side Administrator must configure the target directory ies in the entry mount points in the application conf file Mascot dat or OMSSA path should be configured in result files sub entry administrator can add one or more mappings as label lt absolute directory path gt mzDB files path should be set under mzdb files sub entry Label can be any valid string chosen by Administrator to help user identify mount_point If multiple repositories are defined labels must be different Configuration examples mount points result files mascot data Z under window environement omssa_ data local omssa data under linux environement mdp Tales 4 Running the server Administrator can change default amount of memory used by the server in the jetty runner bat jetty runner sh file If the server is configured with large amount of memory it 1s recommended to increase this value Change the value of Xmx option Xmx4g gt Xmx9 g to pass from default 4 GO to 8GO Run jetty runner bat or jetty runner sh on linux system to start the jetty server You should now be able to access ProlineWeb Core by typing http localhost 8080 proline or http lt host
92. line Web In Proline Studio actually only MzDB files are taken into account You specify thme during the quantitation process Create a Project Create an Empty Project A user can has many projects and share them with others It s the place where you will import your Result Files dat omx set up your validation and quantitation datasets Read more about project creation in Proline Studio and Proline Web Import Result Files Proline Database File G Search Results File_A dat Q l Filel raw Filel raw File_B dat Server RAW Files _ File2 raw File2 raw Import Result Files into Project and associate them with Raw Files Once your project has been created you ll need to import result files into it This consists of storing your files data in Proline s databases This is the first task based action will perform Proline will run this action for a few minutes and you will be noticed when it s done See how to import files from the Proline Studio and the Proline Web interfaces Create amp Validate Identification Datasets Identification Dataset Merged Search Result Project Search Results Search Result A Search Result B File _A dat File_B dat Merged Identification Summary Identification Summary A Identification Summary B Create and Validate Search Results Once your files have been imported to your project s database you can use them as datase
93. m and click on Remove selected Items Type a name for your dataset then click on Create The creation of your identification dataset happens as follows e An Aggreagation Dataset Node is created It takes the name that you provided during the creation e One Identification Dataset Node is created for each one of the Result Files you have selected They take the name of the Result File Once your Identification Dataset has been created you can see it on the tree in the left side of the window Source Search Result Search Result 9 Create Dataset Explorer GB New Project By Reload Settings Dataset tree 3 amp My Projects Test 2 Trash 380 Test DS 5 A FO54972 dat 1 A F054973 dat 2 g Quantitations Search Results Exported Files The panel of the Aggregation Node shows a list of your Identification fractions corresponding to each imported file and after the validation process it will display the Merged Result Summary infos Validate Search Result To launch a validation on a dataset click on its node in the Project Tree on the left side of the Dataset Explorer _ Dataset Explorer New Project y Reload 23 Settings is rea Dataset tree A My Projects Project Test Qj Import Result File to Project Test New Dataset in Test gf Test 5 3 My Projects Infos am Test a a Move to Trash y Export gt Change Name 4 am
94. match If there is a homology threshold and ions score gt homology threshold Protein score peptide score homology threshold else tf ions score gt identity threshold Protein score peptide score identity threshold Protein score 1 average of all the subtracted thresholds e if there are no significant peptide matches the protein score will be 0 e homology and identity threshold values depend on a given p value By default Mascot and Proline compute these thresholds with a p value of 5 e In the case of separated target decoy searches we obtain two values for each threshold one for the target search and another one for the decoy search In order to obtain a single value we apply the following procedure o the homology threshold is the decoy value if it exists else the target value o the identity threshold is the mean of target and decoy values The benefit of the MudPIT score over the standard score is that it removes many of the junk protein sets which have a high standard score but no high scoring peptide matches Indeed protein sets with a large number of weak peptide matches do not have a good MudPIT score Mascot Modified MudPIT Scoring This scoring scheme introduced by Proline is a modified version of the Mascot MudPIT one The difference with the latter is that it does not take into account the average of the substracted thresholds This leads to the following scoring procedure Protein score
95. me time frame Figure 3 shows how important the density of the measures is Therefore comparing LC MS maps without aligning their time scale would generate many matching errors Figure 5 Matching of the detected peptides on several LC MS maps Different algorithms have been developed to correct the time scale and are usually optimized for a given approach Supervised method benefits of the knowledge of the peptide identification and thus will be able to align maps with a low error rate More data processing will be needed to obtain quality quantification results Read the LC MS quantitation workflows documentation to get more information about LC MS quantification algorithms in Proline LC MS quantification workflows LC MS quantification algorithms implemented in Proline are based on the prototype software Prosper developed by IPBS There are a large number of LC MS map generation tools However there was no software solution until Prosper able to read and compare data generated by different tools It was thus a tedious work to evaluate the relative performance of the available peak picking solutions Proline overcomes this problem by implementing the Prosper s parsers in order to bring a high level of flexibility in the workflows of LC MS data analysis Users can use data coming from different peak picking software and all the rest of the data processing alignment normalization comparison can be done with Proline s integrated
96. n principles Generate a XIC To generate a XIC right click on the Quantitations Node and select the Extract Abundances menu in the popup Projects amp ProjectDoc to Identifications a E All Imported o ds2 QP F067910 rT Quantitations i ii XIC Extract Abundances a Spectral ore fl Trash Create Design To create the Design of your XIC drag and drop the identifications from the right panel to the left panel If you drop an identification on the XIC Node a Group and Sample parents nodes will be automatically created You can also directly drop on a Group or Sample Node oe n n g x Fi Step 1 Drag and Drop Identification Summaries to create your XIC Design XIC Design 4 Drag amp Drop e Identifications a Group 1 a exp El Sample 1 amp i conditioni ap F067897 D F067897 a F067898 Wi F067898 Group 2 aD condition Sample 1 m f Foe7a99 0P Fos7e00 Rename Design You can rename all the design nodes by different ways e by typing F2 when a node is selected e bya long click on a node e by the right button popup and the menu Rename We recommend to rename at least the XIC node j Step 1 Drag and Drop Identification Summaries to create your XIC Design amp Drag amp Drop exp XIC f Identifications a i condition 1 a exp Gl fractioni D condition1 P Fo67897 p Fo67897 o E Fo67898 f Fos7as8 E a condition2 El condit
97. n the Project node of the tree F __ Dataset Explorer i New Project iW Reload Settings ure Dataset tree lt lt R My Projects re Project Test BR My Projects Import Result File fig Select All Identification St Test a A Identifications Quantitations e F3 Select All Identification Summaries iB dage a g 5 Edit Project Infos p TE New Quantitation 4 j BR Share with Users You should then see this panel appear BS My Projects eal Project Test re Import Result File to Project Test Select Result File Delete y Reset Name Path Parameters Software Engine Instrument Peaklist Software Decoy Parameters Decoy Strategy No Decoy Database v Parser Parameters Please select a Search Engine Check Files before Import V 282 Start Import It allows you to select Result Files and set up following parameters for the import process e Parameters o Software Engine the software which generated your interrogation file o Instrument mass spectrometer used for sample analysis o Peaklist Software the software used for the peaklist creation e Decoy Parameters o Decoy Strategy TODO o Protein Match Decoy Rule TODO Parser Parameters according to your Software Engine this will display some extra parameters o Mascot lon Score Cutoff TODO Subset Threshold TODO Mascot Server URL TODO o Omssa User mod file TODO PTM Composition File TODO e C
98. ncluded in the validation results For example you might want to keep only the peptide of rank 1 To add a filter parameter choose a setting in the selection box click on the button then edit the threshold value of the parameter on its line Validation Thresholds let you define the False Discovery Rate of your peptides and proteins You must define from which parameter the Peptide FDR should be estimated When ready click on Validate to launch the validation task You can see it in the tasks panel Create a Quantitation Double click on the Quantitations node of your project tree to show you Quantitations table panel It s empty if you haven t created any quantitation in this project yet Click on the New Quantitation button to open the Quantitation Creation Panel Note that you can also open it by right clicking on the Quantitation node or on the Project node itself Title Type and Method Infos Name Test Quanti Description Type label_free v Method label free based on the extraction of feature abundance v Next The first tab of the Quantitation creation panel let you define a name a description optional choose a type and a method Once you ve made your choices click on Next Experimental Design Dataset tree lt amp my Projects 533 New Quantitation in Test 3 amp My Projects Experimental Design J Test 38 Add Group Trash 3 Identification Trees R
99. o filters one on the Protein Name column available wildcards are to replace multiple characters and to replace one character one on the Score Column Score must be at least 100 and there is no maximum specified I rere E E F071423 Proteins The result is all the proteins starting with GLPK correspond to GLPK and with a score greater or equal than 100 Note for String filters you can use the following wildcards matches zero or more characters matches one character E F071423 Proteins 3 cl ed HE i Search Tables In some tables a Search Functionality is available thanks to the search button at the top right corner identifications E TasksLog 8 F072075 Protein Sets My Projects l Protein Set Description Ge fi myProject H B testbetas 7 E HOQEU8_ECOLI tr HOQEUB Hi m E Al Import 7 alapkecow iasa E F06795 sceoin o n 2 5 csaoso ECOBW trIcsaosolcs 6 TNAA_ECODH PERIN N ies 7 B1X7N amp _ECODH tr B IX7NS B H a g ay aad amp C4ZTX1_ECOBW tr C4zTx1 C4 oe F replicate 9 H0Q804_ECOLI trJHOQSO4 Ho 2A J replicate3 10 C5A162_ECOBW tr C5A162 C5 H g replicate4 11 TIG_ECODH sp B1XFM4 TI1 Trash 12 xaxa Eco benoepD 1 m When you have clicked on the search button a floating panel is opened In this panel you can fill in the searched expression Two wild cards are available e can replace all
100. one and identified Proteins are Subset Proteins lL _ Io E _ e OOOO IE O sem OOOO H i i pes l pe4 pe6 i ji pe4 pe6 l I I pe8 e o I Il e 1 i l K 1 l peg i I pet l I pet ped I I e pe1 pe5 I o o pe7 o pe7 i l i I o l i O l i i 1 l I l I P8 I P2 P2 P4 P5 l P2 P2 P4 P5 P2 P3 1 P3 P3 P1 l ij I I I I ee N U AE E E AEREE J e In first example P2 and P5 are identified by the same peptide set pel pe4 pe5 pe8 P2 was choosen as typical protein One SubSet composed of pe4 pe5 pes identifies subset protein P4 e In second example Another Protein Set represented by P3 shares some peptides with ProteinSet represented by P2 Both ProteinSets have specific peptides e Sharing could involve many ProteinSet as shown in example 3 i l pe4 pe6 ped pe6 peg i pe9 pan o pet pe5 O pe1 pe5 o o pe7 l l o pe7 l Yi I i P8 loi P8 j P2 1 P2 I P3 P3 All Peptides Sets and associated ProteinSets are represented even if there are no specific peptides In both cases of above example no choice is done on which ProteinSet PeptideSet to keep These ProteinSets could be filtered after inference see Protein sets filtering Proteins and Proteins sets scoring There are multiple algorithms than could be use to calculate the Proteins and Protein Sets score Proteins score are computed during the importation phase while Protein Sets score are compt
101. opic the moment when the MS MS has been triggered usually not the maximum of the elution peak and the charge state of the ion The first and second information can be considered as close coordinates for the peptide signal on the LC MS map The charge state z can provide additional information to simplify the extraction of different isotopes of the features which are approximately separated by 1 z For each MS MS event 1 The runSlice containing the precursor m z of the MS MS event is retrieved default window is 5 Da more details in the mzDB documentation as well as the following runSlice in order to load into memory everything about the peptidic signal including the isotopes The XIC for the MS MS precursor mass can be then easily accessed with a user defined mass precision default is 5ppm sliding gt MS2 events eXtracted Ion Chromatograms XICs detected in the analyzed run slice 2 The apex of the elution peak of the monoisotopic mass of the peptide does not exactly fit to the moment the MS MS was triggered Knowing that the signal on the XIC is integrated on both sides of the moment the MS MS was triggered default value is 10 scans to determine the ascendant slope and in order to find the apex The integration of the signal is done by summing the intensities of n isotopes n being a user defined value default value is 3 including the monoisotopic peak XIC mz MS MS precursor M
102. ord TELI New password Torii Confirm new password seeseteeeeeee8 Yon Display peptides PSM or Proteins of a Search Result Functionality Access To display data of a Search Result right click on a Search Result click on the menu Search Result gt and on the sub menu PSM or Proteins es ProjectTest id Category Task w 13 Services Validat gt Identifications 7 Onas z vle Database a L Load Ge D bafta Search Result boos DP FO67E Identification Summary hae Properties Pe User Defined Add Merge Validate Change Typical Protein Generate Spectrum matches Compare With C Export Change Description Rename Delete Peptides PSM Window If you click on PSM sub menu you obtain this window _ Tasks Log F067897PSM a i T Spectrum Spectrum Error Fragmentation Table es Ea Query 71 DALTR Uy hee swp p SOTTO y4 30 000 D A 1 T i R 25 000 4 i l 7 A D Generate Spectrum Matches m 10 000 5 000 4 gdl l l Lit li uf_ _j_ Li ETT Lit fat diil ULI 0 50 100 150 200 250 300 350 400 450 500 550 Upper View list of all PSM Peptides Middle View Spectrum Spectrum Error and Fragmentation Table of the selected PSM If no annotation is displayed you can generate Spectrum Matches by clicking on the according button Bottom Window list of
103. p Trash 3 Identification Trees Aggregated Identifications summary mae aT O Daet Identification Raw Fle Name Peakdst Name Result Fle Nar Search Resut enfcaon afi Qunttatio aniis 19 l OENMAI2L FIALA 2 F054972 dat D Search Res 2 10 OEMMALZL FOS4973 dat 4 FS4973 at a Exported Fi Q Export 1 Change Name No Data for t You may want fo launch If you have just validate Identification Trees _ Dataset Explorer Si m Click on Launch Validation in the toolbar of the Infos tab You can also right click on the dataset node and click on use the Launch Validation button The following form appears Identification Validation Computer Mode x PSM Filters Rank Score Threshold ee a ol E n Mascot E Value Mascot Adjusted E Value Mascot P Value IT Mascot P Value HT Validation Parameters Ensure Peptide i FDR lt On this Param Mascot Adjusted E Valu 4 gt lt Protein Set FDR 4 lt x Peptide Set Mascot Modified MudPT Y Scoring Merging Mode Identification Summaries v Validate The Validation form handles several settings Merge Search Results choosing YES will merge the Result Sets corresponding to your result files before launching the validation of the merged dataset NO will validate the result sets separately and merge their result after the validation process Filters let you filter the data that will be i
104. ple These two paths pwd_mascot_data and pwc_mascot_data must point to the same folder There are two of them because you need the PWX server and the Proline Web Core server may not access it via the same way Fill it the right way so the both servers can access the same directory If they are on the same network and use the same OS i e Windows you can set up the same path on both fields o db_username is the user name used by the PostgreSQL connection not needed if you use SQLite The default PostgreSQL user is postgres o db_ password is the password you gave to the PostgreSQL user specified above o raw_files root is the path to your raw files root directory o mzdb_ files root is the path to your mzdb files root directory You must set up the right connection information and data directories in order to access the Proline Core server properly You must set up two path to the mascot data root folder one for the desktop pwd_mascot_data_root and one for jetty pwc_mascot_data_root Set up the SQL Server connection settings and the path to the raw files and mzdb files root directory Once it s done simply click Save Configuration To make sure that the admin user is registered in the UDS DB you need to logout and login in order to make the Proline Service to check your status in the UDS DB and create your account if you re not registered yet How to Note Read the Concepts amp Principles documentation
105. protein sets Here is the procedure used for FDR optimization e protein sets are segregated in two groups the ones identified by a single validated peptide G1 and the ones identified by multiple validated peptides G2 with potentially multiple identified PSMs per peptide e for each of the validation rules the FDR computation is performed by merging target and decoy protein sets and by sorting them by descending score The score threshold is then modulated by using successively the score of each protein set of this sorted list For each new threshold a new FDR is computed by counting the number of target decoy protein sets having a score above or equivalent to this value The procedure stops when there are no more protein sets in the list or when a maximum FDR of 50 is reached It is has to be noted that the two validation rules are optimized separately o G2 FDR is first optimized leading to the R2 score threshold The validation status of G2 protein sets is then fixed o final FDR G1 G2 is then optimized leading to the R1 score threshold Only the G1 protein sets are here used for the score threshold modulation procedure However the FDR is computed by taking into account the G2 validated target decoy protein sets The separation of proteins sets in two groups allows to increase the power of discrimination between target and decoy hits Indeed the score threshold of the G1 group is often much higher than the G2 one If we were usin
106. r type postgresql valid values are h2 postgresql or sqlite I data directory lt path to proline data gt auth config user lt db_user gt password lt db_password gt a Proline Admin invalid configuratio host config Menu Edit Proline configuration gt gt Loading P ERROR Can connection properties connectionMode FILE driver org h2 Driver hibernate dialect org hibernate dialect H2Dialect it postgresql config script directory postgresql connection properties connectionMode HOST driver org postgresql Driver hibernate dialect fr proline core orm utils TableNameSequencePostgres Dialect To load a conf file use the menu on the top left and select your file in the file browser 1 Proline Admin invalid configuration Select configuration file pnfiguration gt gt Loading Proline configuration WARN Unknown data directory ERROR Can t update Proline configuration Unknown data directory not created user s choice gt gt Loading Proline configuration finished with error ERROR Invalid configuration Please edit the configuration file or choose a valid one Finish the datastore setup by clicking the newly available button Set up Proline Note This functionnality will be disabled if Proline is already set up or if Proline configuration is invalid ee be eS ee
107. rance m z Tolerance unit Time Tolerance sec Alignment Smoothing Smoothing Method Time Interval Minimum nb landmarks in window Sliding Window Overlap Master Map Creation Mappina m z Tolerance MS MS events PPM 15 5 PPM Most Intense Most Intense Iterative 3 5 PPM 600 Time Window 200 50 20 Previous This tab let you set up you abundance extraction parameters Next lt gt lt gt lt gt lt lt lt gt E lt lt 1 gt lt gt E lt lt gt lt gt lt gt gt Ratios Ratios Numerator Group 1 Denominator Group 2 iM Numerator Group 1 Denominator Group 1 Group 2 Group 2 The purpose of this tab is to define the ratios between the groups your quantitation will rely on Launch the Analysis When you re done just press the Launch Quantitation button You will be noticed when the task 1s finished Delete Datasets You can delete an Identification dataset by clicking on it on the Dataset Explorer left side panel and then click on the Move to Trash button in the toolbar of the Infos tab Projects Browser re New Project Reload Show RSM Selection Settings Dataset tree Infos amp My Projects Baloo Test Identifications Fractions Summary Duplicate 8 Launch Validation El Move to Trash fy Export Change Name ee EES ID Number Result Set ID Identifica
108. ric Data See Sth ie Peptide M Peptide ll Sequence PTMs Charge Score Rank Experime Missed C Is Validat Is Selecte Protei i de M Param Min Max Raw file conversion to mzDB raw2mzDB installation get the zip archive installation of MSFileReader from Thermo here will install all necessary c redistribuables 3 Ensure your regional settings parameters are for the decimal symbol and for the list separator Ne Use case procedure Open a command line window in the directory containing raw2mzdb exe Type raw2mzdb exe i lt rawfilename gt 0 lt outputfilename gt By defaut the raw file will be converted in the fitted mode for the MS1 MS2 is often in centroid mode and can not be converted in fitted mode If the MS2 or superior are acquired in high resolution i e in profile mode you could specify that you want to convert several MSs in the required mode raw2mzdb exe i lt rawfilename gt o lt outputfilename gt f 1 2 will try to convert MS1 to MS2 in fitted mode There are two other available conversion modes 1 profile the command line is then raw2mzdb exe i lt rawfilename gt o lt outputfilename gt p 1 means you want profile mode for MS1 others MS will be stored as they were stored in the raw file 2 centroid raw2mzdb exe i lt rawfilename gt o lt outputfilename gt c 1 means you want centroid mode for MS1 others MS will be stored as they were stored in the raw
109. ries of analysis and thus a different response in MS signal for peptides having the same abundance Data may not be used if the difference is too important It is always recommended to do a quality control of the acquisition before considering any computational analysis However there are always biases in any analytic measurement but they can usually be fixed by normalizing the signals Numerous normalization methods have been developed each of them using a different mathematical approach Christin Bischoff et al 2011 Methods are usually split in two categories linear and non linear calculation methods and it has been demonstrated that linear methods can fix most of the biases Callister Barry et al 2006 Three different linear methods have been implemented in Proline by calculating normalization factors as the ratio of the sum of the intensities as the ratio of the median of the intensities or as the ratio of the median of the intensities Sum of the intensities How this factor is calculated For each map sum the intensities of the features The reference map is the median map 3 The normalization factor of a map sum of the intensities of the reference map sum of the intensities of the map NO me Median of the intensities How this factor is calculated For each map calculate the median of the intensities in the map The reference map is the median map 3 The normalization factor of a map median of the intensitie
110. ries with Spectral Count Definition e The peptide spectral count consist in counting the number of spectra which matches the current peptide Thus it s equal to the number of pepitde spectrum matches PSM e Protein basic spectral count BSC is equal to the sum of the peptide spectral count for all peptides which identify the protein e Protein specific spectral count SSC is equal to the sum of the peptide spectral count for specific peptide only A specific peptide is a peptide which does not identify any other protein or more precisely protein in other protein sets in the context of the identification summaries e Protein weigthed spectral count WSC is the Protein specific spectral count sharing weighted spectral count of shared peptide SCoes for P2 2 x 1 3 0 67 SCpe4 for P3 2x 2 3 1 33 ee ee O proteins ha N oan peptides jf E F j1 D ef sa 4 PSMs Ci tZ e BSC 6 gt a P3 WSC 6 43 sorts 7 WSC 2 67 P2 Example calculation of spectral count Specificity and weigth reference The peptide specificity and the spectral count weight could be defined in the context of the Identification Summary Where the spectral count is calculated as shown in previous schema It could also be done using another Identification Summary as reference like using the common parent Identification Summary This allow to consider only identified and validated protein in the merge context
111. rresponding peptide sequences can be retrieved If only one peptide sequence is found for the master feature it will be kept as it is Otherwise the master feature will be cloned in order to have one feature per peptide sequence During this duplication step the daughter features will be distributed on the new master features according to the identified peptide sequences 6 Cross assignment When the master map is created some intensity values could be missing Proline will read the mzDB files to reduce the number of missing values using the expected coordinates m z RT for each missing feature to extract new features These new extractions are added to copies of the daughters and the master maps This gives a new master map with a limited number of missing values 7 Normalizing LC MS maps The comparison of LC MS maps is confronted to another problem which is the variability of the MS signals measured by the instrument This variability can be technical or biological Technical variations between MS signals in two analyses can depend on the injected quantity of material the reproducibility of the instrument configuration and also the software used for the signal processing The observed systematic biases on the intensity measurements between two successive and similar analysis are mainly due to errors in the total amount of injected material in each case or the nanoLC MS system instabilities that can cause variable performances during a se
112. ry e Display Identification Summary additional information Save import and export e Export data e Import Result Files Algorithm and other operation e Validate a Search Result Create a Proline project Command line ProlineAdmin Run the following command line from the ProlineAdmin directory e Windows rum Cid batl Create project o1d lt oOwner 10 gt m lt proj ecu name gt dest lt DEOJeCe depttiption gt e Linux sii run Cmd Sh create proj ect O1d lt Cwner 10 gt m lt project mame gt desc lt project description Note The project s description is optional From graphical tool ProlineAdmin GUI Click on the Create a new project button then select the project s owner from users list and set the project s name You can optionally provide a description for this project T Proline Admin D proline data y SOE fa Menu userl hd Edit Proline configuration 03 oct 201 f p a qui p See user s projects f p a g c p Project name project 1 gt run cmd c 03 oct 200 f p admin g Create a new user Name proje gt run cmd ta First project for user cess Description 03 oct 20g Create a new project f p a g c p WARN Prog 03 oct 20g p a g c B Upgrade databases Refresh Di Register Note Since the project s owner must be provided this functionnality will be disable if Proline is not set up or if no Proline user is reg
113. s of the reference map median of the intensities of the map a Median of ratios This last strategy has been published in 2006 Dieterle Ross et al 2006 and gives the best results It consists of calculating the intensity ratios between two maps to be compared then set the normalization factor as the inverse value of the median of these ratios cf figure 8 The procedure is the following 1 For each map in a map set sum the intensities of the features The reference map is the median map 3 For each feature of the master map ratio intensity of the feature in the reference map intensity of the feature for this map 4 Normalization factor median of these ratios 1000 800 600 400 bo log2 ratio 0 1 0 5 0 0 5 1 Lo Figure 8 Distribution of the ratios transformed in log2 and calculated with the intensities of features observed in two LC MS maps The red line representing the median is slightly off centered The value of the normalization factor is equal to the inverse of this median value The normalization process will refocus the ratio distribution on 0 which is represented by the black arrow Proline makes this normalization process for each match with the reference map and has a normalization factor for each map independently of the choice of the algorithm The normalization factor for the reference map is equal to 1 8 Building a QuantResultSummary Once the master map is normalized
114. s pe6 and pe7 For peptide weight if we consider pe4 for example it will be define as follow e Weight pe4 for P2 1 3 gt P2 has 1 specific peptide for a total of 3 Gf we consider P2 and P3 which are proteins identified by pe4 e Weight pe4 for P3 2 3 gt P3 has 2 specific peptide for a total of 3 The spectral count result will thus be eee Ref Prot BSC SSC WSC Ret Prot BSC SSC WSC oE eee ee ee ee eee ee ee ee B A ee a ee e In Proline Actually spectral count is calculated for a set of hierarchy related Identification Summaries In other words this means that Identification Summaries should have a common parent The list of protein to compare or to considere is created at the parent level as the peptide weigth for spectral count see previous chapter Firstly the peptide spectral count 1s calculated using following rules e Equal to Peptide Spectrum Matches Count if Identification Summaries is a leaf not issued from a merge e Sum of child peptide spectral count in case of identification Summaries merge e Sum of validated child peptide spectral count in case of Search Result merge Validated child PSMs are PSMs which satisfy validation applied to parent Identification Summaries Once peptide spectral count is known for each peptide protein spectral count is calculated using followig rules e Protein BSC sum of peptide spectral count e Protein SSC sum of peptide spectral count for specific
115. scription Rename Clear Delete Start Time The selected data is put in the Trash So it is possible to restore it while the Trash has not been emptied Delete Data by Drag and Drop Select the data you want to delete and drag it to the Trash It is possible to restore data while the Trash has not been emptied ProjectTest B 2 a Identifications E All Imported D Replicate 1 DP F067897 H ij Trash Drag amp Drop Empty the Trash To empty the Trash you have to Right click on it and select the Empty Trash menu fi ProjectTest m A A to Identifications E All Imported 7 Replicate 1 Li Fo67897 i Fo67898 T Replicate2 T Replicate3 of Frec Empty Trash In fact for the moment Search Results are not completely removed you can retrieve them from the All Imported window Delete a Project It is not possible to delete a Project by yourself If you need to do it ask to your IT Administrator Connection Managment Once user is connected see Server Connection it is possible to e Disconnect ts ProlineStudio Beta 2 rcl rs File Window Help Disconnect Change password Exit e Reconnect with a different login Server Connection Server Parameter Server Host http ocalhost 8080 proline User Parameters User newUserName Password eennnene Remember Password e Change password Change Password Old passw
116. sion parameters values e Peak List and Spectrum information file name ms level precursor moz e Search result data o Proteins o Peptide o Spectrum o 2 type of Matches a Peptide Match is a match between a peptide and a spectrum with score fragment matches a Protein Matches is a match between a peptide and a protein Mascot result importation The peptide matches score correspond to Mascot ion score OMSSA result importation i todo The peptide matches score correspond to the negative common logarithm of the E value e Score logl0 E value Content of an Identification Summary e Protein Set e Peptide Set Identification Summary Export When exporting a whole Identification Summary in excel file the following sheets may be generated depending on options e search settings and infos Contains information on project and search settings parameters e import and filters Summary of used parameters during import filtering and validation process e protein sets List of all Protein Set valid or invalidated during Protein Sets Filtering Some columns description o sequences specific sequences number of different peptide sequence identifying the Protein Set specific which does not identify any other valid Protein Set o peptides peptides number of different peptide sequence PTM identifying the Protein Set specific which does not identify any other valid Protein Se
117. st features above threshold to build the master map Two methods are available to filter features the filter can be applied directly on intensity values Intensity method or it can be a proportion of the map median intensity Relative intensity method Relative intensity threshold Intensity threshold this provides the threshold for one or the other filtering method depending on which method you have selected Only features above this threshold will be considered for the master map building process Relative intensity Intensity method this option depends on which filtering method you select If you choose Relative intensity for master feature filter type the only possibility you have is percent so you will remove features which intensities are beyond the relative intensity threshold in percentage of the median intensity If you choose Intensity for master feature filter type you also have only one possibility at the moment of the intensity method basic Features which intensities are beyond the intensity threshold set will be removed and not considered for the master map building process Post processing of LC MS quantitative results This procedure is used to compute ratios of peptide and protein abundances Several filters can also be set to increase the quality of quantitative results Here is the description of the parameters that could be modified by the user Peptide filters e Use only specific peptides if checked peptid
118. st peptides are kept e median for each quantitative channel the median of observed peptides abundances is computed e median profile a matrix of peptide abundance ratios is first computed rows correspond too peptides and columns to quantitative channels The median of these ratios is then computed for each column The relative values are then converted back into absolute values using a scaling factor This factor is computed as the maximum value from the means of TOP3 peptides abundances e normalized median profile matrix of peptide abundance ratios is first computed rows correspond too peptides and columns to quantitative channels This matrix is then normalized and then summarized using the median method described above The obtained median abundances are then adjusted by using a scaling factor This factor is computed as the maximum value from the means of TOP3 peptides abundances Peptide Matches Filtering Peptide Matches identified in search result can be filtered using one or multiple predefined filters describes here after Only validated peptide matches will be considered for further Steps Basic Score Filter All PSMs which score is lower than a given threshold are invalidated Pretty Rank Filter This filtering is performed after having temporarily joined target and decoy PSMs corresponding to the same query only really needed for separated forward reverse database searches Then for each query PSMs from target an
119. t o peptide_matches specific_peptide_matches number of different peptide spectrum matches identifying the Protein Set specific which does not identify any other valid Protein Set e best PSM from protein sets List of best peptide spectrum matches a single PSM per peptide is listed for each Protein Set Some columns description o protein_sets number of Valid Protein Set identified by this PSM o protein_matches number of Protein Match which belong to at least 1 valid Protein Set identified by this PSM o databank_protein_matches number of Protein Match validated or not identified by this PSM This is equivalent to the number of protein in fasta files containing the PSM e protein matches in protein set list of Protein Matches in each Protein Set A same Protein Match could thus appears few times if it belongs to different Protein Sets same column as protein set e all PSMs from protein sets List of all peptide spectrum matches for each Protein Set same column as best PSM from protein sets e statistics Some statistic values for the exported Identification Summary number of Protein Set modified peptides Getting Started a Use case Global Overview GroupA i MzDB Files RAW Files Identification Summary Merge A B File_A dat Sample 1 Filel raw ma2db Identification SummaryA File_B dat File2 raw mzdb identification Summary B aw Laas Sample 2 Sample 2 identA A identA A
120. t on which you will perform validation operations Datasets can be assembled under aggregates or treated as is in Proline Studio only Once you ve created a dataset with one or more files you can launch a validation task on it and then browse its result in what s called an Identifcation Validation Summary How to create a Dataset in Proline Studio in Proline Web Hot to validate a Dataset in Proline Studio in Proline Web Quantitation Quantitation Dataset Experimental Design GroupA Identification Datasets Sample 1 Merged Ident Summary m P Ident Summary A o i Ident Summary B bm Quantitations are built around your Experimental Design You are able to recreate your technical and biological replicas hierarchy in Proline set up your extraction parameters define the ratios of your analysis and then launch the task See how it s done in Proli dio and PostgreSQL optmization PostgreSQL 9 1 x documentation English http www posteresql ore docs 9 1 interactive runtime config resource html RUNTIME CONFIG RESOURCE MEMORY Main configuration file is postgresql conf located in PostgreSQL instance data directory Most usefull tunable parameters are 9 Following recommended memory sizes are given for a server with 16 GiB of physical memory and about 8 GiB dedicated for the PostgreSQL instance
121. terval This time interval corresponds to the window size in which time median will be computed Minimum number of landmarks in window this option is only available for time window smoothing method This allows you to specify the minimum number of landmarks a window must contain to compute a median on it it is not significant to compute a median on less landmarks Sliding window overlap overlap is used to compute the step to move the smoothing window forward to calculate a smoothing point for this new smoothing window Overlap gives the percentage of overlapping between two consecutive windows For example if window size is 200 seconds or landmarks depending on which smoothing method is selected and overlap is 20 the step forward 200 100 20 100 160 seconds or landmarks 1 e the smoothing window will be moved forward by a step of 160 so two successive windows will overlap each other by a step of 40 seconds or landmarks corresponding to 20 of 200 Master map creation This step consists in creating the master map also called consensus map this map resulting from the superimposition of all compared maps m z tolerance when mapping features from 2 different maps of the map set delta m z between features must be lower than the m z tolerance to be considered as the same feature seen on 2 different maps m z tolerance unit the m z tolerance unit can be provided in PPM or Dalton Time tolerance seconds when mapping f
122. the most intense features higher than a given threshold and then consider the other features only if they match a feature with a high intensity in another map This is done in order to avoid to include background noise to the master map cf figure 7 Intensity Intensity Reference map Compared map High intensity features Intensity Low intensity threshold features features features Figure 7 Distribution of the intensities of the maps considered to build the master map The construction is done in 3 steps 1 removing features with a normalized intensity lower than a given threshold 2 matching the most intense features 3 features without matches in at least one map are compared again with the low intensity features put aside in first step 5 Solving conflicts It has been seen that ambiguous features with close m z and retention times can be grouped into clusters Other conflicts are also generated during the creation of the master map due to wrong matches Adding the peptide sequence is the key to solve these conflicts by identifying without ambiguity a feature Proline has access to the list of all identified and validated PSMs as well as the identifier id of each MS MS spectrum related to an identification This means that the link between the scan id and the peptide id is known On the other hand the list of MS MS events simultaneous to the elution window of each feature is known For each of these events the co
123. tion ID E TRASH 1 233 1 2 dentification Test 38 7 A OTEMB080213_01_NodeNumber No Data for this Result Summa ry If Quantitations Search Results J Exported Files You can also right click on the dataset you want to delete and click on the Move to Trash button All deleted datasets are visible in the Trash node in the project tree Create a User You must be logged in as an administrator Click the Start button go to Administration On the first tab User Administration you can create a new user by setting up its name its password and define whether or not the user will have the administration permissions including applications users and service Management Submit the form to create the user You can manage existing users from the users tab Please note that the Proline Web Desktop has its own database and its own users collection However if you configured the Proline Service running inside the Proline Web Desktop it will synchronise the Proline Web Desktop and the Proline Core users each time a user logs in the Proline Web Desktop e the users you create in the Proline Web Desktop Administration panel will be automatically added to the Proline Core database User Data Set database when they sign in on the Proline Web Desktop e the Users registered in the Proline Core Database UDS will be automatically registered in the Proline Web Desktop database
124. tion Result issued from these steps is stored in the identification database Different validation of a Search Result can be performed and a new Identification Summary of this Search Result is created for each validation Merger Search Results Merging several Search Results consist in creating a parent Search Result which will contain all merged PSMs issued from child Search Result For each identified peptide in at least one child a single merged PSM will be created and filled with the best child attributes score missed cleavage etc The best child PSM is the PSM with the higher score Once a parent Search Result is created the same validation operation as the one accessible for new imported Search Result could be done In this case the generated Identification result is not linked to Identification Result associated to child Search Result An other merge algorithm could be used see Merge Identification Results validation Merge Identification Results This merge operation consists in creating a parent Identification Result from few child ones A Search Result corresponding to this parent Identification Result will be generated Concretely the first step of this merge operation consist in creating merged PSMs for all peptides identified and validated in at least one child Identification Result Protein Inference will then be applied to create the parent Identification Result validation Compare Identification Summa
125. to understand main concepts and algorithms used in Proline Proline Admin Create a Proline User Create a Proline Project Proline Studio Creation Deletion Open a session and access to my projects Create a new project e Create a Dataset e Import a Search Result e Delete Data e Connection Management Display e Display Peptides PSM or Proteins of a Search Result e Display PSM Peptides or Protein Sets of an Identification Summary e Display Search Result amp Identification Summary additional information e Display Spectral Counts e Display XIC e Create and Save a User Window e List of Abbreviations e Frame Toolbars Functionalities e Filter tables e Search tables e Graphics Scatter Plot Histogram e Statistical Reports MSDiag Save import and export e Import Mascot OMSSA result file e Export data Algorithm and other operation e Validate a Search Result e Change Typical Protein of a Protein Set e Merge e Data Mixer Quantitation e Spectral Count e XIC e Refine Protein Sets Abundances Proline Web Workflow e Open a session and access to my projects e Register and pair Raw amp MzDB files e Create anew project e Import Result Files e Create an Identification Dataset e Validate a Search Result e Create a Quantitation e Delete Datasets Users management e Create a User Display e Display peptides and or PSM in identification result e Display proteins sets in Identification Summa
126. tools Proline also has its own feature detection algorithms PEAK PICKING SOFTWARE Signal extraction Signal extraction pi OPENMS W haa re o Ae Figure 1 Overview of the LC MS quantification workflow LC MS maps from different sources can be imported in the LCMSdb Once loaded these maps will be treated by several algorithms for data processing and comparison The result of the analysis can be exported into different file formats Analytic workflows have been developed for each quantitative analysis strategies e Label free LC MS quantitation workflow e Isotopic labeling LC MS quantitation workflow mzDB processing Purpose Extracting peptidic signals called features from a file converted into the mzDB format Feature extraction algorithms The FeatureExtractor algorithm is composed of four different extraction strategies e UnsupervisedFeatureExtractor NYT e MS2DrivenFeatureExtractor e PredictedTimeFeatureExtractor e PredictedMzFeatureExtractor NYD The selection of the strategy depends on the PutativeFeatures parameters Details on these different implementations are given in the following sections MS2 driven algorithm This is the main peptide signals extraction algorithm Every MS MS event triggered by the spectrometer corresponds to one or more peptidic signal Each event provides a set of information about the targeted precursor ion the m z ratio assuming it is monoisot
127. uding cai http www matrixscience com cai Importing a Search Result can take some time While the import is not finished the All Imported is shown grayed with an hour glass and you can follow the imports in the Tasks Log Window Menu Window gt Tasks Log to show it ProjectTest a ts Identifications i il Trash id Category aa i mport Identification mascot_data Proline_Tests WP4 F067899 dat Import Identification mascot_data Proline _ is bl ota dat efit a eve B Sowse Server Fie System mascot data Praine Tests WP4 e Server File System mascot _data Proline _Tests AVP 4 vj fe i ees pr Browse Server File System mascot_data Proline_Tests 110 Browse Server File Svstem mascot data To show all the Search Results imported double click on All Imported or right click to popup the contextual menu and select Display List From the All Imported window you can drag and drop one or multiple Search Result to an existing dataset ack Files to Import or TEE eee dat Projects amp amp TasksLog se ProjectTest All Imported se Be Projecttest O id Search Result Name PeakistPath MSISearch F MSIS WP2112 Cab 25 10 12 Col reprosi 172 D Dat aaia 067837 de mnty Replicate 1 I7CR WP2112 Cab 25 10 12 Col reprosi 172 O Data Clair FO67898 de imat T Replicate f a a 19S CR WP2112 Caib 25 10 12 Cal wasl 172 DO Datallal F067899 az imr
128. ued during the validation phase Protein Each individual protein match is scored according to all peptide matches associated with this protein independently of any validation of these peptide matches Currently when e importing Mascot result file the Mascot standard scoring is used sum of peptide matches scores e importing OMSSA result file Protein Set Each individual protein set is scored according to the validated peptide matches belonging to this protein set see inference Scoring schemes Mascot Standard Scoring The score associated to each identified protein or protein set is the sum of the score of all peptide matches identifying this protein or protein set In case of duplicate peptide matches peptide matched by multiple queries only the match with the best score is considered Mascot MudPIT Scoring This scoring scheme is also based on the sum of all non duplicate peptide matches score However the score for each peptide match is not its absolute value but the amount that it is above the threshold the score offset Therefore peptide matches with a score below the threshold do not contribute to the protein score Finally the average of the thresholds used is added to the score For each peptide match the threshold is the homology threshold if it exists otherwise it is the identity threshold The algorithm below illustrates the MudPIT score computation procedure Protein score O For each peptide
129. ule fr proline module seq uniProtSEDbIdentifierRegex to extract protein accession For other fasta file the protein accession will be extract by using string before first blank Installing Proline Studio e Proline Studio application distribution is a zip file that must be extracted on each client computer Installing and configuring Proline Web Desktop e Install Configure and launch the Desktop Installating Proline Web e The Proline Web eXtension PWX is based on the use of MongoDB database engine You need to download it and install it either on the computer which will host the PWX server or any other network accessible computer You will find the installation files on this page http www mongodb org downloads e Download and unzip the PWX archive Configure the Server If you installed your MongoDB database on a different computer than the PWX server you ll need to edit the PWX configuration file e Go to the installation directory of your PWX server e Go to the conf folder and open the application conf file with any text editor like Window default Notepad e Edit the mongodb servers and cache mongodb servers parameters by setting the host name and the port number corresponding to your MongoDB server default MongoDB port is 27017 If MongoDB is installed on the same computer as the PWX server you don t need to configure anything at this time Launch The Server e First please make sure that MongoD
130. up clustering and deconvolution which consists in grouping all the charge states detected for a single molecule 3 LC MS map alignment Feature matching Because chromatographic separation is not completely reproducible LC MS maps must be aligned before being compared The first step of the alignment algorithm is to randomly pick a reference map and then compare every other map to it On each comparison the algorithm will determine all possible matches between detected features considering time and mass windows the default values are respectively 600 seconds and 10 ppm Only landmarks involving unambiguous links between the maps only one feature on each map are kept cf figure 3 Skipped gt landmark ambiguous Reference map Compared map RT RT Kept landmark m z m z Reference map Compared map Figure 3 Matching features with the reference map respecting a mass 10ppm and time tolerance 600s The result of this alignment algorithm can be represented with a scatter plot cf figure 5 Selection of the reference map The algorithm completes this alignment process several times with randomly chosen reference maps Then it sums the absolute values of the distance between each map to an average map cf figure 4 The map with the lowest sum is the closest to the other maps and will be considered as the final reference map from this point Sum of time distances to the i 5 a d E 430
131. when you log in as administrator in the Proline Web Desktop if they were missing Peptides Table The Peptides tab of a validated Identification Dataset or Merged Dataset allows you to browse the peptides of the related Result Summary Each table of the Result Summary data viewer provides a set of Filters for Numerical Text and Boolean data placed on the left of the grid Projects Browser New Project Reload T ShowRSM Selection Settings 804 joseypg Peptide ID Sequence Calculated Ma Best Score Best Rank Proteins Peptide matc 86 MNFKGR 751 3799 3 15 8 171 ADGKIIR 771 4603 14 28 1 Param Min x 213 TLCFLSK 810 4310 763 3 230 LREILLK 883 5855 2 40 5 495 KGWRPR 798 4613 2 69 7 Ja Remove All AND 2 1 1 3 1 1 1 1 4 1 1 1 Peptide Peptide Matches Peptide Match MS Query ID Sequence Experimental m Missed Cleava Fragment mat Is Validated 1 3840 34 ADGKIR 386 74 1 7 Peptide Protein Matches Protein Match ID Protein ID Taxon ID Accession Description Coverage 1 5053 0 0 PYR1_SCHPO sp Q09794 PYR1_SCHPO Protein ura1 OS S 0 A Start __ Projects Browser Quality Strict Peptide Click Actions Clicking on a Peptide will automatically display the peptides matches related to the selected peptide on the Peptide Matches table and the Protein Matches table will display the protein matches related to the selected peptide Double Click actions If you double click on a peptide mat

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