Minimum SNPs version 2043 user manual
Contents
1. bar e Repeat the above process for all loci included in the profile e Click Finish on the menu bar e At this point a new dialog box opens enabling you to locate and open the sequence time file called a strain file e Once you have opened the sequence type file the process is complete and the results will be presented in the results pane The results pane will display the following information o A list of alleles that share the same profile at each selected locus o The indistinguishable STs based on the SNPs that have been included in the profile e Click Clear Report on the results bar to start over OR e Save and Print your Results 6 2 Working backwards using a pre concatenated MLST database downloaded from an MLST web site e Type the SNP positions in the identity check box These must be separated by commas in this instance do NOT put a space next to the comma 17 Select the ST that defines the SNP profile you are interested in using the allele drop down menu Click add The results pane contains the SNP profile of the selected ST and the other STs that share the same profile Click Clear Report on the results bar to start over OR Save and Print your Results 18 7 Tools and Options The allele options page under the tools menu is very useful The functions of these options are listed below 7 1 Number of results Minimum SNPs assembles SNP sets by first f2. button on the task bar e Click Consensus in the task bar e Inthe data box adjacent to the Consensus button type or paste the list of sequence types ST s that you want to identify as a group o The list of ST s must have the format ST x1 ST Xo ST Xn Where x is the MLST sequence type and n the n sequence type in the series Make sure there is a space after every ST and a space after every and no other spaces e Click Not N in the task bar The results pane will display the following information o The loci included in the analysis 14 o The identification constraints see Tools and Options o The results pane is as for the and D modes with the exception that the selected group of sequences is listed and the SNPs are defined in NOT mode i e NOT AGT means C and NOT GC means A or T Click Clear Report on the results bar to start over OR Save and Print your Results 5 3 2 Using a pre concatenated MLST database downloaded from an MLST web site A quirk of the software is that it does not matter which of the D or buttons is selected Click on the consensus button In the data box adjacent to the Consensus button type or paste the list of sequence types that you wish to identify as a group o Important these must be typed EXACTLY as they are listed in the allele drop down menu This is case sensitive and s
3. find and replace function in Microsoft Word to change the allele sequences into the correct format 3 3 Loading Allele Files Loading the Allele files into Minimum SNPs follows the same procedure for all four methods applications As described in Section 3 1 a false locus should be the last allele file to be loaded into Minimum SNPs The last allele will not be included in the Mega Alignment but will act as a bookend or a full stop in a manner of speaking e Open Minimum SNPs e Click File on the menu bar e Select the option Load Allele File from the dropdown menu o Anew window opens which allows you to brows for the location of you allele files e Open the first allele file o The sequences with which the allele files are loaded does not matter at this point However usually load the false allele last for consistency as you will soon see Repeat the steps above until all allele files have been loaded o You can keep track of where you are up to in loading allele files by the title given to the Minimum SNPs window Once you have loaded all the allele files individual alleles can be viewed in task pane A locus is selected buy Click File on the menu bar Select the option Alleles from the dropdown menu From the second dropdown menu select the locus that you would like to use o Notice that the incorrect terminology has been used here The option Alleles on the File dropdown menu sh
4. is the D value analog of the confidence option above This option is even more important because it can take a large number of SNPs to reach a D value of 1 0 with most MLST databases As the calculation time increases exponentially with the number of SNPs it can be impractical in terms of processing time to reach D 1 0 Once again it is prudent and efficient to begin with a low D e g 0 9 and work up from there to see how high it is practical to achieve A search that is limited by D 0 9 will generally only take a few seconds even on very large data sets 7 7 Search Depth We do not use this option very often but it can be useful This limits the size of the SNP set that is returned It apparently over rides the limits set by the Confidence or Simpsons Index options above so e g if the Confidence is set to 99 and the Search Depth to 2 then it may return a SNP set that does not reach 99 Also Somewhat confusingly the search depth is incorrect by 1 so that a search depth of 1 will actually allow the return of a two member SNP set and so forth It is most useful as a time out or infinite loop error avoidance strategy although similar aims can be achieved with judicious use of the Confidence and Simpsons Index options 7 8 Number of loci This sets an upper limit on the number of loci that can be used in assembling mega alignments There is no problem with having it set higher than the number of loci used in particula
5. loci The loci are internal fragments of seven house keeping genes 450 500bp common to all isolates within a species More broadly speaking a locus is position on a chromosome of a gene or other chromosome marker also the DNA at that position 1 5 Allele For each housekeeping gene alleles are the different sequences present within a bacterial species More broadly speaking an allele is any one of a number of alternative forms of the same gene occupying a given locus position on a chromosome 1 6 Allelic profile The alleles at each of the seven loci housekeeping genes in a particular isolate More broadly speaking the allelic profile is the combination of alleles that any one individual possesses 1 7 ST Sequence Type Sequence type is a number given to represent a unique allelic profile gt analogous to allelic profile 1 8 Method There are four basic applications or methods that we have developed for use with Minimum SNPs Below is a brief description of each 1 8 1 Percent Method What set of SNPs should be tested to differentiate a single known sequence type from all other sequence types available on the MLST database 1 8 2 D method What set of SNPs should be tested to differentiate an unknown sequence type from any other sequence type in the MLST database 1 8 3 Not N method What set of SNPs should be tested to identify a defined group or complex of sequence types such that a false negative
6. result cannot occur 1 8 4 Working backwards method Which sequence types available on the MLST database share a defined set of SNP alleles 2 Getting Started Minimum SNPs uses the Java Runtime Environment so you will need this software installed on your computer before you begin You can download the latest version of Java from http www java com en download windows ie jsp If you experience problems using the latest version of Java you may wish to try using an older version Java 2 Runtime Environment SE v1 4 2_01 was used for the creation of this manual There are many versions of Minimum SNPs that were released at various staged in it s evolution Be ware that some older versions have serious bugs that will make some of the applications described in this manual erroneous The version of Minimum SNPs used at the time of writing this manual was MLST2043__Not_N Once you have unzipped the parent file MLST2043_ Not_N you will notice that a new folder has been created called classes There are many files within the classes folder but there is only one executable Jar file called MLST When you open the MLST file make sure that you open with Java javaw The following icon should appear if the file is ready to be opened with Java ZA Mist jar Important note At the time the early versions of Minimum SNPs were written it was not possible to download concatenated databases from MLST web sites As a cons
7. that has been concatenated using the Minimum SNPs on board concatenation facility e Change the mode from D to by clicking the D button on the task bar e Select the ST that you are interested from the dropdown menu adjacent to the word Allele in the top right corner of the task bar e Select percent e Click Identify Allele in the task bar The results pane will display the following information o The loci included in the analysis o The ST that has been queried o The concatenated sequence of that sequence type according to the order in which the loci were joined together added o The identification constraints see Tools and Options o The SNP s returned by the Minimum SNP s analysis and the corresponding confidence of identifying the sequence type queried from all other sequence types in the database using those SNP s e Click Clear Report on the results bar to start over OR e Save and Print your Results 5 1 2 Using a pre concatenated MLST database downloaded from an MLST web site e Select the sequence type of interest in the allele drop down menu 12 e Ensure that the mode has been selected top right hand corner The symbol appears ghosted when it is selected e Click identify allele The results pane will display the following information o The ST that has been queried labelled as an allele o The sequence of that ST o The identification constrain
8. GAACGGGAATT AATTCTCATCCTGATTATCCGAAGGTTGTA GAAAGAAAAAT AAGAGAAGTGACAGGTTTTGAATATACTGTGGCTGAGGATTTGATCGAG GCGACTCAAGAT ACGGGAGCTTATGTACAAATTTCAGGTGTTTTAAAACGTGTTGCAACA AAACTTTCTAAAGTATGT AATGACTTAAGACTTTTAAGCAGTGGTCCAAAATGTGGTCTT AATGAGATTAATCTTCCAAAAATGCAACCAGGTAGTTCTATCATGCCAGGT AAGGTAAAT CCTGTTATTCCTGAAGTAGTTAATCAAGTTTGTTATTTTGTTATTGGAGCAGATGTAACT SPAS ETT TASS ISSAC AAT AC ARE HARTGE TT NTRS MATTOS gt aspa ATGATAGGTGAAGAT AT ACAAAGAGTATTAGAAGCTAGAAAATTGATTTTAGAGATCAAT TTGGGTGGAACTGCTATTGGAACAGGAATTAATTCTCATCCTGATTATCCGAAGGTTGTA GAAAGAAAAAT AAGAGAAGTGACAGGTTTTGAATATACTGTGGCTGAGGATTTAATCGAG GTAACTTTTGCTTGTGAGGGTGGACAATT ACAACTTAATGTTTTTGAACCAGTTGTA gt aspA_3 ATGATAGGTGAAGAT AT ACAAAGAGT ATT AGAAGCT AGAAAATTGATTTTAGAGATCAAT TTGGGTGGAACTGCTATTGGAACAGGAATT AATTCTCATCCTGATTATCCGAAGGTTGTA GAAAGAAAAAT AAGAGAAGTGACAGGTTTTGAATAT ACTGTGGCTGAGGATTTGATCGAG GCGACTCAAGAT ACGGGAGCTTATGTACAAATTTCAGGTGTTTTAAAACGTGTTGCAACA AAS AC URAAG TAT ESS ITT BACCAGTGGTCEALAL TGGTCTT amp Assen cmbpostgrads bamberj 2005 Staph NOT N allele files P fauX_ Notepad File Edit Format view Help bfaux_1 TTATTAATCCAACAAGCT AAATCGAACAGTGACACAACGCCGGCAATGCCATTGGAT ACTTGTGGTGCAATG File Edit View Favorites Tools Help Q x Q B Search gt Folders E File and Folder Tasks C Make a new folder Publish this folder to the Web St File Other Places Staph NOTN My Documents My Computer My Network Places a Hint use the
9. Minimum SNPs Version 2043 User Manual For assistance or additional information contact Phil Giffard at p giffard qut edu au Largely written by John Bamber November 2006 Last updated by Phil Giffard September 2007 1 N N Contents Definitions Definitions of terminology background information references Getting Started System and software requirements how to start the program Creating a mega alignment How to down load and concatenate MLST data Obtaining and loading a pre concatenated MLST database Deriving highly informative sets of SNPs Working backwards method How to go from SNP profile to sequences Tools and options These functions facilitate efficient and effect searches P3 P6 P7 P10 P11 P16 P29 1 Definitions 1 1 Minimum SNPs Minimum SNPs is proprietary software for deriving discriminatory sets of SNPs from DNA sequence alignments as described in Price E P Inman Bamber J Thiruvenkataswamy V Huygens F and Giffard P M 2007 Computer aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants BMC Bioinformatics 8 278 Earlier versions of the software are described in Robertson G A Thiruvenkatswamy V Shilling H Price E P Huygens F Henkens F A and Giffard P M 2004 indentifcation and interrogation of highly informative single nucleotide polymorphism sets defined by bacterial multilocus sequenc
10. al allele sequence alignments with reference to a file of MLST allele profiles This section describes how to download these data ensure that they are in the correct format and assemble a mega alignment i e a concatenated MLST database 3 1 Obtaining and preparing a file of MLST allele profiles Sequence types and allelic profiles T pe y gt aaa Se ce Bl kA z z can be downloaded from the Multi D Locus Sequence Typing website sile I eI http www mist net Follow the i link to Databases and select the database for the organism of interest Depending on the host website allelic profiles may be jo downloaded in tab delimited form comma delimited form or as a csv file The sequence type and allelic profile list must then be converted to a csv file with column headers ST for column A and the loci names for columns B through H The software has a bug which results in the last locus column H not being included in the mega alignment To circumvent this problem simply insert a fake locus into column I Column I then becomes the last column and is not included in the analysis NB The names used to identify the loci must be exactly the same as those used in the allele files wb bh O N wn N e O N 3 on ana ne wN e oN h Remon no Oe W nN on 9 3 3 1 1 10 ee TTA oe oe on ee a a R 3 2 Obtaining and preparing allele sequence
11. alignment files Allele sequences are also downloaded from the MLST web site http www mlst net Once again follow the link to Databases and select the database for the organism of interest Allele sequences can be downloaded in FASTA format form all host websites Some downloaded allele sequences will have paragraph endings f at regular intervals thought each sequence while others will have no paragraph endings within the sequence Either version of the download can be used with Minimum SNPs NB The names used for the loci in FASTA format allele sequences must be exactly the same as those used in the ST file above B arcC_ Notepad File Edit Format View Help parcel ree J TTATTAATCCAACAAGCTAAATCGAACAGTGACACAACGCCGGCAATGCCATTGGATACTTGTGGTGCAA Spree wee erate epee eee eee ete E Or rT ace yee Tere CCE A E Srp RET Sk Aces y Ad STR erence Rng Cac Aa b A A ET AT ENEE ONAA E E ONAT CCA TEON AE EEA TTATTRATCCARCAAGCTAAATCGAACAGTGACACAACGCCGGCAATGCCATTGGATACTTETEGTGCAATE I gt arcc_8 TTATTAATCCAACAAGCTAAATCGAACAGTGACACAACGCCGGCAATGCCATTGGATACTTGTGGTGCAA gt arcc_9 TTATTAATCCAACAAGCTAAATCGAACAGTGACACAACGCCGGCAATGCCATTGGATACTTGTGGTGCAA gt arcc_10 TTATTAATCCAACAAGCTAAATCGAACAGTGACACAACGCCGGCAATGCCATTGGATACTTGTGGTGCAA gt arcc_11 uir yeee E A S e e a A AT ACTER GGT SC Gag P aspA_ Notepad File Edit Format View Help gt paspaAl ATGATAGGTGAAGAT AT ACAAAGAGT ATT AGAAGCT AGAAAATTGATTTTAGAGATCAAT TTGGGTGGAACTGCTATTG
12. by a series of seven integers which correspond to the alleles at the seven house keeping loci In MLST the number of nucleotide differences between alleles is ignored and sequences are given different allele numbers whether they differ at a single nucleotide site or at many sites The rationale is that a single genetic event resulting in a new allele can occur by a point mutation altering only a single nucleotide site or by a recombinational replacement that will often change multiple sites weighting according to the number of nucleotide differences between alleles would imply that the latter allele was more distantly related to the original allele than the former which would be true if all nucleotide changes occurred by mutation but not if the changes occurred by a recombinational replacement Most bacterial species have sufficient variation within house keeping genes to provide many alleles per locus allowing billions of distinct allelic profiles to be distinguished using seven house keeping loci For example an average of 30 alleles per locus allows about 20 billion genotypes to be resolved MLST is based on the well established principles of multilocus enzyme electrophoresis but differs in that it assigns alleles at multiple house keeping loci directly by DNA sequencing rather than indirectly via the electrophoretic mobility of their gene products Extracted from http www mlst net misc further asp on November 1 2006 1 4 Locus
13. cus that you would like to work with In the data box adjacent to Identity Check type the position of the SNP within the selected locus that you would like add to the SNP profile you are characterising o If there is more than one position within that particular locus you must query both at once Separate the SNP positions with a coma Do not use any spaces Example 36 241 243 Click Add in the task bar Click Start in the task bar Click Insert in the task bar 16 e Check that the polymorphs presented in the data box adjacent to the Start button represent the profile that you are characterising If they do not modify the data by replacing the letter for the nucleotide at the appropriate position with the desired polymorph Do not change the format of the data in the box at all e Click Accept in the task bar e Click File on the menu bar e Select the option Alleles from the dropdown menu e From the second dropdown menu select the next locus that you would like to work with e Inthe data box adjacent to Identity Check type the position of the SNP within the second locus that you would like add to the SNP profile you are characterising e Click Add in the task bar e Click Insert in the task bar e Check that the polymorphs presented in the data box adjacent to the Start button represent the profile that you are characterising e Click Accept in the task
14. e typing databases J Med Microbiol 53 35 45 Price E P Thirvenkatswamy V Mickan L Unicomb L Rios R E Huygens F and Giffard P M 2006 Genotyping of Campylobacter jejuni using seven single nucleotide polymorphisms in combination with flaA short variable region sequencing J med Microbiol 55 1061 1070 Additional applications of the software are described in Stephens A J Huygens F Inman Bamber J Price E P Nimmo G R Schooneveldt J Minckhof W and Giffard P M 2006 Methicillin resistant Staphylococcus aureus genotyping using a small set of polymorphisms J Med Microbiol 55 43 51 Price E P Huygens F and Giffard P M 2006 Fingerprinting of Campylobacter jejuni using resolution optimized binary gene targets derived from comparative genome hybridization studies Appl Env Microbiol 72 7793 7803 Huygens F Inman Bamber J Nimmo G R Munckhof W Schooneveldt J Harrison B McMahon J A and Giffard P M 2006 Staphylococcus aureus genotyping using novel real time PCR formats J Clin Microbiol 44 3712 3719 Stephens A J Huygens F and Giffard P M 2007 Systematic derivation of marker sets for Staphylococcal Cassette Chromosome mec typing Antimicrob Agents Chemother 51 2954 2964 The software was developed by researchers at the Queensland University of Technology Brisbane Queensland Australia node of the Cooperative Research Centre for Diagnostics with input
15. equence the software contains its own concatenation facility In this manual a concatenated MLST database is termed a mega alignment Although the concatenation function in Minimum SNPs is completely functional it is somewhat inconvenient and counter intuitive to use with the major issue being that the concatenated data cannot be stored and must be re assembled from the allele sequence and ST profile data every time the software is used Although this only takes a few minutes it is quite annoying In consequence we recommend that analyses be carried out on pre concatenated databases where possible This allows analyses to be carried out using much fewer key strokes and in an inherently simpler fashion The only disadvantage is that while the on board concatenation function keeps track of where each locus starts and stops and defines SNPs in terms of location within MLST loci this obviously cannot be done with pre concatenated data In this case the output SNPs are defined only of terms of their location within the concatenated data This manual is currently structured around using the on board concatenation function However sections that detail how to obtain and use pre concatenated MLST data have been added throughout 3 Creating a Mega Alignment Note if you intend to use pre concatenated MLST data you do not need to create a mega alignment Go to Section 4 Minimum SNPs concatenates MLST data by processing the individu
16. from researchers at the University of Newcastle Newcastle New South Wales Australia Minimum SNPs can only be obtained from www ihbi qut edu au research cells_tissue phil_giffard 1 2 SNP A single nucleotide polymorphism is a variation in the genetic code at a specific point on the DNA In principle SNPs could be bi tri or tetra allelic polymorphisms However tri allelic and tetra allelic SNPs are rare and so SNPs are sometimes simply referred to as bi allelic markers However we have identified two tri allelic SNP that have proved very useful in genotyping Staphylococcus aureus 1 3 MLST Multi Locus Sequence Typing The original MLST web software was developed by Man Suen Chan Oxford University and this version has been developed by David Aanensen Imperial College who is funded by The Wellcome Trust Multilocus sequence typing MLST is an unambiguous procedure for characterising isolates of bacterial species using the sequences of internal fragments of seven house keeping genes Approx 450 500 bp internal fragments of each gene are used as these can be accurately sequenced on both strands using an automated DNA sequencer For each house keeping gene the different sequences present within a bacterial species are assigned as distinct alleles and for each isolate the alleles at each of the seven loci define the allelic profile or sequence type ST Each isolate of a species is therefore unambiguously characterised
17. gth are indeed present in some concatenated databases These must be removed or doctored so they are the correct length 4 2 Loading pre concatentated data into Minimum SNPs e Open Minimum SNPs e Click File on the menu bar e Select the option Load Allele File from the dropdown menu o Anew window opens which allows you to browse for the location of your concatenated data file e Select the file that is the concatenated data in FASTA format The file will then load If the file is large e g thousands of STs the loading process may take 20 seconds or more The concatenated MLST data is now ready for analysis using the D or Not N modes 11 5 Deriving highly informative sets of SNPs Minimum SNPs assembles highly informative sets of SNPs by carrying out an empirical search i e by assessing the informative powers of thousands of different SNP combinations Three different algorithms the D and Not N modes are available for measuring the informative powers of candidate SNP sets For complete details see Robertson et al 2004 Price et al 2006 Price et al 2007 5 1 Mode This mode is used to identify SNP set s diagnostic for a single user specified ST It is called the mode because the informative power is stated as the of STs that are discriminated from the user specified ST by the SNP s To obtain and load data files see Section 3 5 1 1 Using a mega alignment An MLST database
18. inding the most informative SNP and calling that SNP1 and then finding the SNP that is most informative in combination with the SNP1 and calling that SNP2 and so forth If there is a tie i e the same score a draw between one or more SNPs it uses that to define alternative SNP sets In effect each of the tied alternatives seeds new pathways of SNP set assembly The number of results option simply sets an upper limit on the number of SNP sets that are listed in the results Nearly all ties occur late in the process of SNP set assembly so that if the the SNP set is kept small using e g the Confidence option then often only one result will be returned 7 2 Paragraph width Affects the format of the results 7 3 Inclusions and Exclusions This option is extremely useful It allows the user to tell the program to ignore SNPs exclusions or to include SNPs in the output set irrespective of resolving power inclusions This allows SNP sets to be easily adapted and optimised For example a SNP that is technically difficult to interrogate can easily be replaced by the next most informative SNP without the necessity for redesigning the whole SNP set It is important to note that when a SNP is forced to be included in the set using the include function its informative power is used in the derivation of other SNPs to be added to the set Included SNPs can therefore be used to seed SNP sets The include function
19. ion constraints see Tools and Options o The SNP s returned by the Minimum SNP s analysis and the corresponding Simpson s index of diversity associated when the MLST database is assessed with the resulting SNPs e Click Clear Report on the results bar to start over OR 13 e Save and Print your Results 5 2 2 Using a pre concatenated MLST database downloaded from an MLST web site e Ensure that the D mode has been selected top right hand corner The symbol appears ghosted when it is selected e Click identify allele Note although it is possible to select an allele actually an ST in the drop down menu this has no effect on the results because the D algorithm does not identify SNPs with reference to any particular ST The results pane will display the following information o The identification constraints see Tools and Options o The SNP s returned by the Minimum SNP s analysis and the corresponding Simpson s index of diversity associated when the MLST database is assessed with the resulting SNPs e Click Clear Report on the results bar to start over OR e Save and Print your Results 5 3 Not N Method Note the key strokes for this method are very different from the and D modes 5 3 1 Using a mega alignment An MLST database that has been concatenated using the Minimum SNPs on board concatenation facility e Change the mode form D to by clicking the D
20. is also a very easy way of determining the resolving power of SNP sets that have been identified by Minimum SNPs independent methods 7 4 Time out Seconds This also can be very important although primarily for the trouble is causes rather than the benefits In essence if it is set too low then the program will terminate before finishing its calculation and will return a result of no results found It usually makes sense to set it to a high value in particular if you are doing D mode searches on large databases These searches can take up to several hours especially on older computers although more usually it is a few minutes at most and not N searches should only take a few seconds 7 5 Confidence 1 100 This option is very important It is the maximum resolving power that is aimed for in and not N searches It is important to have this set at an appropriate value This is because Minimum SNPs does not have good error handling functions If this value is set higher than can ever be reached and this can happen if the not N mode is used or there are identical sequences in the input file then the program will never finish its calculation and 19 will simply proceed until it times out and returns no result An efficient and low stress approach to doing searches is to start with the confidence set low and work up E g start with 95 for searches and 50 for not N searches 7 6 Simpsons Index 0 1 This
21. ould really read Loci You will notice that each allele of the selected locus can be viewed by selecting from the drop down menu adjacent to the word Allele in the task bar 3 4 Assembling Alleles and Loading ST File Once all the alleles have been loaded into Minimum SNPs they need to be assembled into a Mega Alignment This is done by sequentially joining the loci together followed by loading the sequence type file which of which alleles from each locus to include in the sequence of a particular sequence type It is important to note that the loci do not need to be joined in any particular order except for the final locus which is a false locus acting as a bookend Change the mode form to D by clicking the D button on the task bar Click File on the menu bar Select the option Alleles from the dropdown menu From the second dropdown menu select the locus that you would to add fists o usually work from top to bottom as they are listed in the drop down menu the order in which they were loaded On the task bar click Add On the task bar click Start On the task bar click Accept Click File on the menu bar Select the option Alleles from the dropdown menu From the second dropdown menu select the locus that you would to add next Click Add Click Accept e Repeat the above 5 steps until all loci have been joined added including the fal
22. paces must be included if they are within the name In addition the gt symbol that denotes sequence names in the FASTA format must also be included The sequence names must be separated by commas and there MUST be a space after each comma If you are doing many Not N analyses it is helpful to use an input file in which the sequence names in the Fasta format are very short e g just gt x where x is the ST number You can then type the sequence as e g gt 5 gt 28 gt 79 gt 766 in the consensus box Click the Not N button The results pane is as for the and D modes with the exception that the selected group of sequences is listed and the SNPs are defined in NOT mode i e NOT AGT means C and NOT GC means A or T Helpful hint It is common for Not N analyses to fail to yield highly resolving sets of SNPs such sets may simply not exist Therefore it is important to not set the confidence level under the tools drop down menu allele options menu item too high If it is too high the program will simply provide no results It is prudent to start with the confidence at about 50 and work up from there 15 6 Working Backwards Method Working backwards is calculating the STs defined by a particular user defined SNP profile The methods for doing this in Minimum SNPs are a little counter intuitive as is explained below 1 The software canno
23. r having it set to 7 the number of loci in MLST schemes causes no problem in using pre concatenated MLST datasets in the form of single alignments Therefore it is better to simply set it to 7 and then leave it alone 20
24. se locus o Once again you can keep track of where you are up to in joining loci together adding loci by the title given to the Minimum SNPs window e On the task bar click Finish e At this point a new dialog box opens enabling you to locate and open the sequence time file called a strain file e Once you have opened the sequence type file the word Mega alignment will appear in the title if the process has been successfully completed If you do not see the word Mega Alignment then there has been an error in either the data within your allele or ST files or in the sequence of loading and adding alleles in Minimum SNPs This mega alignment is now ready for analysis using the D or Not N modes 10 4 Obtaining and loading a pre concatenated MLST database Most MLST sites now provide the option of downloading concatenated data The early versions of Minimum SNPs were designed to analyse data from individual MLST loci As a consequence of this Minimum SNPs can deal with a pre concatenated MLST database as if it were in effect a single giant MLST locus and each sequence type is an allele This approach is very easy and straightforward 4 1 Downloading a pre concatenated MLST database e Download and save the concatenated MLST data in FASTA format In order for the program to run all the sequences must be same length and composed only of G s A s T s and C s We have found that sequences of the incorrect len
25. t work backwards using a mega alignment that has been constructed using the on board concatenation function However it can work backwards using the separate locus specific alignments that are used to construct mega alignments Although this is fully functional it is not very convenient In particular it requires that the software be re started and the sequence alignments reloaded Working backwards using a pre concatenated MLST database i e a single alignment is extremely quick and easy The ease of working backwards is a major argument in favour of using pre concatenated data This mode does not operate by the user actually inputting the SNP profile Rather it operates as follows if the program is given one or more SNPs and a particular ST it will determine the SNP profile defined by that ST and also determine which STs have the same SNP profile 6 1 Working backwards if you are using separate locus specific sequence alignments and an ST allele profiles file i e you usually work using the on board mega alignment concatenation function If you have already created a Mega Alignment you will need to close Minimum SNPs and start over as working backwards does not make use of a Mega Alignment Load allele files Change the mode form D to by clicking the D button on the task bar Click File on the menu bar Select the option Alleles from the dropdown menu From the second dropdown menu select a lo
26. ts see Tools and Options o The SNP s returned by the Minimum SNP s analysis and the corresponding confidence of identifying the sequence type queried from all other sequence types in the database using those SNP s e Click Clear Report on the results bar to start over OR e Save and Print your Results 5 2 D Method This is used to identify SNP sets that are optimised towards discriminating all STs from all STs This is accomplished by assessing the informative power of candidate SNP s by calculating the Simpsons Index of Diversity D with respect to the input sequence alignment In this context Dis the probability that any two sequences selected at random from the alignment without replacement will be discriminated by the SNP set under test To obtain and load data files see Section 3 5 2 1 Using a mega alignment An MLST database that has been concatenated using the Minimum SNPs on board concatenation facility e Make sure that the mode selected in the task bar is D The symbol appears ghosted when it is selected e Click Identify Allele in the task bar You may need to wait some time at this point Note although it is possible to select an ST in the drop down menu this has no effect on the results because the D algorithm does not identify SNPs with reference to any particular ST The results pane will display the following information o The loci included in the analysis o The identificat
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