Blast2GO Plugin User Manual
Contents
1. e Annotation e Annotation progress success This chart shows the annotation status of the data set i e how many sequences have or have not a BLAST result how many sequences obtained a GO mapping and how many sequences could be annotated successfully or stayed without annotations e Annotation distribution This chart informs about the number of GO terms assigned per sequence e GO term distribution A chart for each GO category shows the most frequent GO terms within a dataset without taking into account the GO hierarchy GO level distribution This chart shows the distribution of GO levels for each GO category This chart helps to analyse whether a set of annotations is more general low GO levels or more specific higher GO levels e Sequence length distribution This chart shows the correlation between length of the sequences and the number of assigned annotations e InterProScan Statistics This chart shows the effect of adding the GO terms retrieved though the InterProScan results e Annex This chart shows the performance of the Annex annotation augmentation step It shows the number of GO terms which were confirmed replaced or removed through this method Copyright 2013 BioBam Bioinformatics S L 14 A Blast2GO Workflow All major Blast2GO plugin functions are workflowable and the corresponding input and output formats are described in table 4 1 This allows us to create an annotation pipeline with only a2. Copyright 2013 BioBam Bioinformatics S L 4 e Filter Filter or hide sequences e Delete Delete all selected sequences 2 BLAST Import Blast Import Blast via the general Workbench import function Please go to Menu Import Standard Import and select the file type Blast2GO Project via Blast XML result xml For further instructions please see Import using the import dialog in the CLC bio Work bench Help 3 Mapping Mapping is the process of retrieving GO terms associated to the hits obtained after a BLAST search To run mapping select one or various data sets which contain blasted sequences and execute the mapping function When a BLAST result is successfully mapped to one or several GO terms these will come up at the GOs column of the Main Sequence Table Assigned GOs to hits can be reviewed in the BLAST Results Browser Successfully mapped sequences will turn green Blast2GO 8 performs different mapping steps to link all BLAST hits to the functional information stored in the Gene Ontology database Therefore Blast2GO R uses different public resources provided by the NCBI PIR and GO to link the different protein IDs names symbols GIs UniProts etc to the information stored in the Gene Ontology database the GO database contains several million functionally annotated gene products for hundreds of different species All annotations are associated to and Evidence Code which provides information about the
3. GO terms to nucleotide or protein sequences However not only functional labels but also a meaningful description for novel sequences is desired A common approach is to directly transfer the Best BLAST hit description to the novel sequence It is frequent that best hit descriptions are of low informative text such as unknown putative or hypothetical while descriptions of other Blast hits of the same sequence do contain informative keywords For this reason a text mining functionality has been included in Blast2GO It analyses a set of sequence descriptions of a given BLAST result The feature is called the BLAST Description Annotator BDA Depending on the frequency of occurrence and the information content the most suitable description is selected out of the collection of words In this way this simple approach avoids sequence descriptions like for example hypothetical putative or unknown protein in the case that a more informative and representative description is available These descriptions are only of exploratory nature and do not have the same weight of evidence as the functional labels Validate Annotation This function validates the annotation result and removes redundant GOs from the dataset It assures that only the most specific annotations for a given sequence are saved In this way this function prevents that two or more GO terms lying on the same GO branch are assigned to the same sequence The Gen
4. ion binding NodeScore 21 00 calcium ion binding NodeScore 20 00 transcription factor activity NodeScore 20 00 sa rotein tyrosine poen copper ion bindi pisso pies serine threonine kinase NodeScore 14 00 NodeScore 13 00 activity NodeScore 20 28 heme binding NodeScore 22 00 FILTERED AND THINNED GRAPH c filtered Graph 2 Figure 3 3 The molecular functions of 1000 sequences visualized in 3 different ways The first eraph is unfiltered the second graph shows the functional information after having applied a GoSlim reduction and the third graph is filtered and thinned according to the number of sequences belonging to each GO term and the node score All GO terms with less than 10 sequences tip nodes and all intermediate terms with a node score smaller than 12 with a 0 4 were removed Copyright 2013 BioBam Bioinformatics S L 11 Number of sequences with length x E value distribution 25 r pop m 1 qn d T A nn a i La 1 1 0 100 200 300 400 500 600 700 800 25 50 75 100 125 150 175 length E value 1e X a b BEGUENTe nudis distribution Data Distribution FEM Sequences 600 0 500 1 000 1 500 200 Without Bla ane Without Blast Hits I 300 With Blast Results EN 200 100 With Mappi i A E IN Fpositives alignment length o e NEEE c d Mapping database sources Evidence code distribution for GOs Total GOs
5. of the earlier described method of simply counting the number of different sequences assigned to each GO term The o parameter allows this behaviour to be further adjusted A value of zero means no propagation of information and can be increased by rising o score g M gp ga oa 6 92 3 1 ga desc g where e desc g represents all the descendant terms for a given GO term g Copyright 2013 BioBam Bioinformatics S L 9 e dist g g is the number of edges between the GO term g and the GO term ga e g is an element of the GO where GO is the overall set of all GO terms e gp g is the number of gene products assigned to a given GO term g Graph Term Filtering Combined graphs can become extremely large and difficult to navigate when the number of visualized sequences is high Additionally the relevant information in these cases is frequently concentrated in a relatively small subset of terms We have introduced graph pruning functions to simplify DAG structures to display only the most relevant information In the case of the Combined Graph function a cutoff on the number of sequences or the node score value can be set to filter out GO terms In this case the size of a graph is reduced without loosing the important information i e hiding tip and intermediate low informative nodes This approach of graph filtering and trimming is based on a combination of different scoring schemes On the one hand graph filteri
6. quality of this functional assignment 1 BLAST result accessions are used to retrieve gene names or Symbols making use of two mapping files provided by NCBI Identified gene names are than searched in the species specific entries of the GO database 2 BLAST result GI identifiers are used to retrieve UniProt IDs making use of a mapping file from PIR including PSD UniProt Swiss Prot TrEMBL RefSeq GenPept and PDB 3 BLAST result accessions are searched directly in the GO database Statistics Three evaluation charts are available to summarize the mapping results The DB resources of mapping chart shows from which database annotations has been obtained and the Evidence Code distribution for hits and sequences indicated how EC associate in the obtained GO pool Note that in most cases IEA electronic annotation are overwhelmed in the mapping results However the contribution of this and other type of annotation to the finally assigned annotations to the query set can be modulated at the annotation step Reset Mapping Removes already obtained results for a given dataset 4 Annotation This is the process of selecting GO terms from the GO pool obtained by the Mapping step and assigning them to the query sequences GO annotation is carried out by applying an annotation rule AR on the found ontology terms The rule seeks to find the most specific annotations with a certain level of reliability This process is adjusta
7. sequences 0 10 000 20 000 Sequences UniProtKB JEN 0 2 500 5 000 7 500 TAIR J _ _ _ GR protein i EA NW MGI un i w RGD 4 o iss i uU UniProt 4 Y i n WB 4 o NDE 2 SGD 4 m ZFIN 4 9 Rca rm GeneDB Spombe 4 c a RefSe IDA GeneDB Tbrucei gt IBA SGN y L TIGR CMR lI IGC e Annotation distribution GO level distribution g0 z 400 2 SPIB dece LASA id z 200 icons E ste 100 J 9 38 ER 00 85 88 88 a EE A e Oe Em m 01234556 7 8 9 101112131415 12 34587 8 8101112131415 01234567 8 91011121314151617 12345587 8 9 1011121314151817 Total Annotations 5620 Mean Level 5 168 Std g h Figure 3 4 A collection of different Blast2GO Charts Copyright 2013 BioBam Bioinformatics S L 12 Direct GO Count MF InterProScan results 5eqs yy 00 0 22 30 fo 100 y ATP binding a 5 protein binding i E 400 structur IS D zinc ion binding i w 300 electron carri _ iT E i amp DNA binding pur binding Am iron ion binding prote MS 0 RNA binding y without IPS with IPS with GOs a b Species distribution BLAST Hits O 200 1 000 1 500 2 000 2 300 3 000 Vitis vinifera Lk Populus trichocarpa q UU Arabidopsis thaliana UU Orza sativa IS
8. terms per branch that fulfil a user specified annotation weight criteria i e sequence abundance or node scores are shown In this way the GO DAG can be cut locally at different levels to provide an optimal view of the dataset s most relevant terms 9 3 Statistics The Statistics wizard allows to select and generate all available charts in one run Satistical charts are available to provide direct feedback about data composition Charts such as mean sequence length involved species distribution BLAST e value distribution or the standard deviation of GO level annotation distribution allow the visualisation of intermediate and final result summaries These charts are especially helpful to validate the results of each analysis step and to re adjust or determine the parameters of subsequent processing In this interactive manner the annotation process can be adjusted to specific data set and user requirements List of all available quantitative statistical charts in Blast2GO e BLAST e E value distribution This chart plots the distribution of E values for all selected BLAST hits It is useful to evaluate the success of the alignment for a given sequence database and help to adjust the Evalue cutoff in the annotation step e Sequence similarity distribution This chart displays the distribution of all calculated sequence similarities percentages shows the overall performance of the alignments and helps to adjust the annot
9. the number of sequences annotated at different amounts of GO terms Reset Annotation Removes already obtained results for a given dataset 5 InterProScan The functionality of InterPro annotations in Blast2GO 8 allows to retrieve domain motif information in a sequence wise manner The processed sequence have to contain a valid sequence string which is not the case when they were just imported through a blast result file IPRscan results can be viewed through the Single Sequence Menu Merge InterProScan In this step the obtained GO terms have to be transferred to the sequences and merged with already existent GO terms Reset InterProScan Removes already obtained results for a given dataset 6 GO Slim GO Slim is a reduced version of the Gene Ontology that contains a selected number of relevant GO terms The GO Slim algorithm generates a GO Slim mapping for the available annotations and permits in this way the projection of certain specific terms into more generic ones i e GO Slim summarizes a set of GO annotations from e g a whole genome microarray analysis to a simpler more general functional schema Different GO Slims are available which are adapted to specific organisms Blast2GO R supports Copyright 2013 BioBam Bioinformatics S L f the following GO Slim mappings General Plant PIR Yeast GOA GO Association TAIR Candida and Pombe GO Slim is often used before a GO Combined Graph is generated GoSlim p
10. Blast2GO Plugin User Manual For CLC bio Genomics Workbench and Main Workbench Version 1 Feb 2013 e Dlast2go BioBam Bioinformatics S L Valencia Spain Contents Introduction E Quick Start Blast2GO Plugin Manual 1 Blast2G0O Pl ginj a sos a a a zoo PSR RRR Sew Be Re Eee See 1 1 Blast2GO Plugin Toolbox functions oa ao a aa 1 2 The Blast2GO sequence Table 1 3 Blast2GO Sequence Table Side Panel DO 39 9 TREE NMEMYEMESPRMM VERMES ee ee MINIS 3 Mapp h kane Peo mom EOROROE ER wo PARR ERE AAA eS AE E E E E E E r oa aaa E E E E ee OET E E E aa ees cc 8 Miscellaneous oaoa a a a eee eae eee ee eee ss ee eee eee ee ee 9 1 Create Combined Graphs 2 a a a e 9 2 Create Pie nor o eos Rom ox roS oo ee AA 9 3 ss ae ata bo ee ace ewe hheanee eee Peewee ee E N IN cc dduoooco i 1ctcvcc BR KBR gx mL A Blast2GO Workflow E On E QO Copyright 2013 BioBam Bioinformatics S L Introduction Support pluginsupport blast2go0 com Website Blast2GO Conesa et al 2005 is a methodology for the functional annotation and analysis of gene or protein sequences The method uses local sequence alignments BLAST to find similar sequences potential homologs for one or several input sequences The program extracts all GO terms associated to each of the obtained hits and returns an evaluated GO annotation for the query sequence s En
11. Desc Annotation J validate Annotations LY S Remove 1 Level Annotations sii Run ANNEX BE Create Annotation Table Create Blast2GO Example Dataset ES Analysis Select 23 Create Combined Graph O Unselect Exact Search 3 Browse File gt amp Create Pie Chart lf Statistics Apply a Toolbox area b Sequence editor side panel Figure 3 2 User Interface The Blast2GO Toolbox and the Main Sequence Side Panel Copyright 2013 BioBam Bioinformatics S L 411 possibility and strength of abstraction When GO weight is set to 0 no abstraction is done Finally the AR selects the lowest term per branch that lies over a user defined threshold DT AT and the AR terms are defined as given in Figure 1 To better understand how the annotation score works the following reasoning can be done When EC weight is set to 1 for all ECs no EC influence and GO weight equals zero no abstraction then the annotation score equals the maximum similarity value of the hits that have that GO term and the sequence will be annotated with that GO term if that score is above the given threshold provided The situation when EC weights are lower than 1 means that higher similarities are required to reach the threshold If the GO weight is different to 0 this means that the possibility is enabled that a parent node will reach the threshold while its various children nodes would not The annotation rule provides a general fr
12. Project to convert your Blast results into a Blast2GO Project 4 Perform Gene Ontoloy Mapping Go to Toolbox Mapping Mapping to start the mapping Mapped sequences will turn green Once Mapping is completed visualize your results at Mapping Mapping Statistics 5 Annotation Go to Toolbox Annotation Annotation to run the annotation step Leave the default parameters for the annotation rule as well as the Evicence Codes Annotated sequences will turn blue 6 Generate Statistic Charts Once the annotation process is finished we can generate all the different statistics charts from Toolbox Miscellaneous Statistics 7 Modify Annotations To modify the annotations click on one of the sequences form the Blast2GO sequence table with the left mouse button and select Change Annotation and Description To change the extent of annotations we can add implicit terms via Annex Toolbox Miscellaneous Run Annex To reduce the amount of functional information and to summarize the functional content of a dataset run a GO Slim reduction Toolbox GO Slim GO Slim 8 InterProScan To complement the Blast based annotations with domain based annotations run an Inter ProScan Search Go to Toolbox InterProScan This step is recommended to improve the annotation outcome Once InterProScan results are retrieved use Merge InterProScan to add the GO terms obtained through motifs domains to the current ex
13. Ricinus communis EE Zea mays q IN Glycine max q EM Sorghum bicolor Em Physcomitrella patens l edicago truncatula q mE Picea sitchensis unknown 4E un Nicotiana tabacum I wv Solanum tuberosum 4 EE T Gossypium hirsutum D Lycopersicon esculentum a Solanum lycopersicum un Malus x Pisum sativum Elaeis guineensis Citrus sinensis Capsicum annuum Brassica napus Fragaria x Triticum aestivum Chlamydomonas reinhardtii Micromonas sp Hevea brasiliensis Branchiostoma floridae others EILLLLLLLLLLLIT D O A Figure 3 5 A collection of different Blast2GO Charts Copyright 2013 BioBam Bioinformatics S L 13 e Top Blast Species distribution This chart gives the species distribution of the Top BLAST HITs e HSP HIT coverage This chart shows a distribution of percentages The percentages represent the coverage between the HSPs and its corresponding HITs This chart helps to get an understanding of the effect of this annotation parameter e Mapping e Evidence Code distribution This chart shows the distribution of GO evidence codes for the functional terms obtained dur ing the mapping step It gives an idea about how many annotations derive from automatic computational annotations or manually curated ones e DB source of mapping This chart gives the distribution of the number of annotations GO terms retrieved from the different source databases like e g UniProt PDB TAIR etc
14. amework for annotation The actual way annotation occurs depends on how the different parameters at the AS are set 1 E Value Hit Filter This value can be understood as a pre filter only GO terms obtained from hits with a greater e value than given will be used for annotation and or shown in a generated graph default 1 0E 6 2 Annotation Cut Off threshold The annotation rule selects the lowest term per branch that lies over this threshold default 55 3 GO Weight This is the weight given to the contribution of mapped children terms to the anno tation of a parent term default 5 4 Hsp Hit Coverage CutOff Sets the minimum needed coverage between a Hit and his HSP For example a value of 80 would mean that the aligned HSP must cover at least 80 of the longitude of its Hit Only annotations from Hit fulfilling this criterion will be considered for annotation transference 5 EC Weight Note that in case influence by evidence codes is not wanted you can set them all at 1 Alternatively when you want to exclude GO annotations of a certain EC for example IEAs you can set this EC weight at 0 Successful annotation for each query sequence will result in a color change for that sequence from light green to blue at the Main Sequence Table and only the annotated GOs will remain in the GO IDs column An overview of the extent and intensity of the annotation can be obtained from the Annotation Distribution Chart which shows
15. ation score in the annotation step e Species distribution This chart gives a listing of the different species to which most sequences were aligned during the BLAST step Copyright 2013 BioBam Bioinformatics S L 10 _ _ _ 9 gt apera T z LAA A e Es tEn E e Se HO Ee T d E TEE ae ae ee a de xz eee cox E ma zar c a Ni i c2 RE 9 o mw re e M GE ww A oo eo mon E zresa si Tow oe eae ee sheers TS a Unfiltered Graph molecular_function y NodeScore 576 59 nutrient reservoir protein tag activity ity NodeScore 1 00 NodeScore 4 00 NodeScore 5 40 molecular transducer activi transmembrane substrate specific transporter activity transporter activity NodeScore 1 30 NodeScore 1 30 chromatin binding NodeScore 5 00 NodeScore 1 80 NodeScore 1 30 z substrate specific hydrolase activity hydrolase activity T cytoskeletal protein passive transmembrane s 2 H receptor activity receptor binding nels SR transmembrane acting on ester acting on acid NodeScore 5 00 NodeScore 1 00 4 P Y transporter activity bonds anhydrides NodeScore 4 08 NodeScore 0 39 NodeScore 2 16 hydrolase activity acting on acid anhydrides in phosphoric ester hydro
16. ble in specificity and stringency For each candidate GO an annotation score AS is computed The AS is composed of two additive terms The first direct term D T represents the highest hit similarity of this GO weighted by a factor corre sponding to its EC The second term AT of the AS provides the possibility of abstraction This is defined as annotation to a parent node when several child nodes are present in the GO candidate collection This term multi plies the number of total GOs unified at the node by a user defined GO weight factor that controls the Copyright 2013 BioBam Bioinformatics S L 5 e Tr 4 Show GO Names GO IDs GO Category Color InterPro Acc GO IDs Only Selected Sequences Selection Select All Unselect All Invert Selection Delete Selection Blast2G0 E Blast Select by State l Blast Statistics Unblasted iy Reset Blast ka Mapping y Mapping lf Mapping Statistics Mapped e CMM Annotated a Annotation Manually Annotated ls Annotation Statistics GO Slim ff Reset Annotation UID E InterProScan select Dy InterProScan Name Seq ID Merge InterProScan Description let Inter Pro Scan Statistics Function GO Term iy Reset InterProScan E7 GO Slim GO ID he GO Slim LJ include more specific Functions iy Reset GOSlim li Manage Projects 7 E Combine Datasets _ Case Sensitive Convert Data to Blast2GO Project Eg Miscellaneous Search ao4 Blast
17. e Ontology true path rule assures that all the terms lying on the branch or route from a term up to the root top level must always be true for a given gene product Therefore any term is considered as redundant and is removed if a child term coexists for the same sequence This function can be run independently however Blast2GO 8 applies this method automatically always after a modification is made to an existing annotation such as merging GO terms from InterProScan search after Annex augmentation or upon manual curation Remove 1 Level Annotations This function removes for each sequence the three main root or top level GO terms molecular function biological process and cellular component if present since they do not provide any relevant information Copyright 2013 BioBam Bioinformatics S L 8 Create Annotation Table This function allows to create an CLC bio Annotation Table containing the Gene Ontology terms gen erated with Blast2GO ANNEX Annex Myhre et al 2006 developed by the Norwegian University of Science and Technology is essentially a set of relationships between the three GO categories Basically this approach uses uni vocal relationships between GO terms from the different GO Categories to add implicit annotation Annex consists of over 6000 manually reviewed relations between molecular function terms involved in biological processes and molecular function terms acting in cellular compo
18. erforms a graph pruning based on a manually defined subset of more general GO terms in order to summarize the information in a graph The result is that a DAG of thousands of nodes can be summarized or slimmed to a few dozen key terms which makes the graph navigable and easy to interpret However the GoSlim method has several characteristics that are not always appropriate The manually defined subsets are context dependent e g different definitions for different species the level degree of abstraction is static and information at more specific levels is blurred 7 Manage Projects Combine Datasets This function allows to combine an already existing Blast2GO B project with another dat or annot file In the case of dat files only those sequences will be added to the existing data set which sequence names do not already exist In case of the annot file annotation information will be added and merged i e if a sequence with a given sequence name already exists in the data set the new annotations will be added to this sequences and a validation check is performed see section B Convert Data to Blast2GO Project This function allows to convert various CLC bio data types to a Blast2GO project Supported CLC bio data types are e Nucleotide Sequence s e Protein Sequence s e DLAST Result 8 Miscellaneous Blast Description Annotation The primary goal of Blast2GO is to assign functional labels in form of
19. few mouse clicks Let s say we have a set of sequences that contain blast results which we want to map and annotate Af terwards we also want to create some statistics to get an idea if the result is satisfactory or not One way to achieve this is by executing the mentioned algorithms and functions one by one The just described way of proceeding has one big disadvantage the different steps have to be started all separately one after another This can be undesirable if we have a very big data set and want to analyze our data set e g over the weekend Another scenario would be to re run the same steps several times but with different parameters e g being more or less restrictive in the annotation part The workflow tool allows us to automate at least parts of this process In the following section it is described how to create a simple annotation pipeline using the workflow functionality of the Workbench Please keep in mind that the described steps to create a workflows in general are the same for any kind of workflow and are therefore also described in the CIC bio Workbench manual However the Blast2GO 8 plugin has several characteristics that are important to know and which will be described here 1 First of all we need to create a new workflow Go to Workflows New Workflow 2 Now we can add the desired functions with right click Add Element Blast2GO We add Convert Data to Blast2GO Project Mapping Annotation Mappin
20. flow we have to verify this ourselves and check that all steps are connected in the right order Figure 4 2 shows such a case where the mapping is placed behind the annotation Running this workflow will result in a mapped project without annotations This is because the annotation step needs the information from the mapping However we will not receive any error messages or similar because of the above mentioned reason Copyright 2013 BioBam Bioinformatics S L 15 Sequence List or Multi Blast Convert Data to Blast2GO Project Blast2GO Project Blast2GO Project with blast or Multi Blast f Mapping Blast2GO Project mapped Blast2GO Project mapped Blast2GO Project mapped les Mapping Statistics 5j Annotation Blast2GO Chart Blast2GO Project annotated Blast2GO Project annotated 11 Blast2GO Project Blast2GO Chart mapped Statistics Blast2GO Chart Blast2GO Chart Annotation 1 Figure 4 1 Example of a correctly configured complete annotation workflow Sequence List or Multi Blast Convert Data to Blast2GO Project Blast2G0 Project Blast2GO Project mapped 5 Annotation Blast2GO Project annotated Blast2GO Project with blast or Multi Blast h Mapping Blast2GO Project mapped Blast2GO Project mapped Figure 4 2 Example of a wrongly configured workflow Copyright 2013 BioBam Bioinformatics S L 16 Table 4 1 Detailed list of workflowable plug
21. g Statistics and Statis tics 3 The selected functions now appear in the workflow area we can arrange them to graphically form the pipeline shown in figure 4 1 4 Now we connect all the available outputs with the logical proceeding inputs Apart from that all functions that create a result that you want to save to disk have to be connected to a so called workflow output To achieve this we right click on the desired functions outputs and select Use as Workflow Output We must not forget to connect the workflow input to the Convert Data to Blast2GO Project which will be our entrance point of the pipeline 5 The next step would be to configure a few parameters Configurable functions are indicated by a little notepad symbol To set the parameters of a function we double click on it to show a wizard similar to the ordinary one We can activate the Data Distribution chart in both statistic steps With this we can examine the success rate of the mapping step while the annotation step is still running 6 After configuring the functions as desired we save the workflow to be able to execute it It is important to understand that a Blast2GO Project has no attribute which indicates the status of a project e g project is mapped or annotated The workbench is therefore not able to verify if the processed project is annotated mapped or has only blast results Therefore when ever we need to choose input data or connect algorithms in the work
22. hy Conesa et al 2005 Conesa A G tz S Garc a G mez J M Terol J Tal n M and Robles M 2005 Blast2go a universal tool for annotation visualization and analysis in functional genomics research Bioinformatics 21 18 3674 3676 Gotz et al 2008 Gotz S Garcia Gomez J M Terol J Williams T D Nagaraj S H Nueda M J Robles M Talon M Dopazo J and Conesa A 2008 High throughput functional anno tation and data mining with the blast2go suite Nucl Acids Res pages gkn1764 Copyright 2013 BioBam Bioinformatics S L 19
23. in features Possible Input 1 Convert Data to Sequence Data Multi Blast2GO Project Blast2GO Project Blast Blast2GO Project 1 Blast2GO Project Blast2GO Project 2 Blast2GO Project GO Slim Blast2GO Project 3 Blast2GO Project 5 InterProScan Blast2GO Project with Blast2GO Project Sequence Data 6 Merge InterProScan Blast2GO Project 5 Blast2GO Project Run ANNEX Blast2GO Project 3 Blast2GO Project S8 Statistics Blast2GO Project Blast2GO Project 9 Create Combined Graph Blast2GO Project 3 6 7 Blast2GO Combined Graph 10 Create Pie Chart Blast2GO Combined Blast2GO Pie Chart Graph 9 Copyright 2013 BioBam Bioinformatics S L 17 Please Cite e A Conesa S G tz J M Garcia Gomez J Terol M Talon and M Robles Blast2GO a universal tool for annotation visualization and analysis in functional genomics research Bioinformatics Vol 21 September 2005 pp 3674 3676 e A Conesa and S Gotz Blast2GO A Comprehensive Suite for Functional Analysis in Plant Genomics International Journal of Plant Genomics Vol 2008 2008 pp 1 13 e S Gotz et al High throughput functional annotation and data mining with the Blast2GO suite Nucleic Acids Research Vol 36 June 2008 pp 3420 3435 e S Gotz et al B2G FAR a species centered GO annotation repository Bioinformatics Vol 27 7 2011 pp 919 924 Copyright 2013 BioBam Bioinformatics S L 18 Bibliograp
24. isting annotations Note If we already have a set of InterProScan results in XML format we can add them to the existing Blast2GO project from the main menu File gt Import InterProScan XMLs Copyright 2013 BioBam Bioinformatics S L 2 9 Export Results Once the annotation process has concluded several options exist to export the results via the Workbench Export function e annot file The annot file is the standard format to export GO annotations It is a tab separated text file each row contains one GO term e dat file The standard Blast2GO project file This file can also be opened with the standalone Blast2GO application e Sequence Table A tab separated text file containing all the information given in the Blast2GO sequence table e GAF 2 0 A tab separated text file of the funtional information in the Gene Ontology annotation file format The content of this format can also be viewed within the Workbench via the Create Annotation Table function from the toolbox Copyright 2013 BioBam Bioinformatics S L 3 Blast2GO Plugin Manual 1 Blast2GO Plugin 1 1 1 2 1 3 Blast2GO Plugin Toolbox functions BLAST Contains functions for performing BLAST searches and resetting results Gene Ontology Mapping of Blast results This function fetches GO terms associated to hit sequences obtained by BLAST Functional Annotation Includes different functions to obtain and modulate GO computing Goslim view Enzyme Code anno
25. lase activity NodeScore 1 80 actin binding channel activity NodeScore 3 00 NodeScore 2 16 nuclease activity NodeScore 5 00 phosphorus containing anhydrides NodeScore 0 65 ion transmembrane transporter activity lis a isa isa NodeScore 3 60 substrate specific channel activity NodeScore 3 60 pyrophosphatase activity NodeScore 1 08 phosphatase at NodeScore 3 00 ion channel phosphoprotein nucleoside t activity phosphatase activity riphosphatase activity NodeScore 6 00 NodeScore 5 00 NodeScore 1 80 motor activity NodeScore 3 00 GOSLIM GRAPH b Filtered Graph 1 isa isa Ca a a a terms endopeptidase inhibitor Y activity transporter acti NodeScore 23 2 NodeScore 14 00 7 transcription regulator isa isa sa 1 term isa activity isa isa isa 1 term ls a NodeScore 14 16 nucleotide hydrolase activity NodeScore 26 08 isa isa 1 term 1 term a 1 term 1 term kinase activity L NodeScore 24 40 phosphotransferase activity alcohol group as acceptor NodeScore 17 63 purine ribonucleotide adeny nucleotide unfolded protein 3 terms binding binding binding NodeScore 16 32 NodeScore 16 40 NodeScore 16 00 nucleoside t riphosphatase activity NodeScore 14 95 peroxidase activity 1 term 1 term isa isajisa GTP binding NodeScore 22 00 magnesium
26. nents Annex based GO term augmentation can be run on any annotation loaded in Bast2GO Generally between 10 and 15 extra annotation is achieved and around 30 of GO term confirmations are obtained through the Annex data set For more details visit the Annex Project at The Annex dataset connects molecular functions with terms from the biological process and cellular component GO categories Create Blast2GO Example Dataset This functions allows to add several small example data set to the Navigation Area in the Workbench Each file contains just 10 sequences which allows to easily explore the different possibilities of the plugin 9 Analysis 9 1 Create Combined Graphs Visualization is a helpful component in the process of interpreting results from high throughput exper iments and can be indispensable when working with large data sets Within the GO the natural visualization format is the Direct Acyclic Graph of a group of annotated sequences In the DAG each node represents a GO term Arcs represent the relationships between the biological concepts A prob lem when visualising GO functional information of genomic data sets is that these graphs can become extremely large and difficult to navigate when the number of represented sequences is high Combined Graphs One of the functions of Blast2GO is the ability to display the annotation result of one or several sequences in the same GO graph Within Blast2GO 8B these gra
27. ng can be based on the number of sequences assigned to each node and on the other hand a graph can be thinned out by removing intermediate nodes that are below a given cutoff The latter approach allows a certain level of details to be maintained while drastically reducing the size of the graph by removing unimportant intermediate graph elements In this way any large GO graph can be reduced by abundance and information content instead of simply cutting through the Gene Ontology at a certain hierarchical level or by the use of GoSlim definitions In Figure the molecular functions of 1000 sequences are visualized in 3 different ways The first graph is unfiltered the second graph shows the functional information after having applied a GoSlim reduction The third graph is filtered and thinned according to the number of sequences belonging to each GO term and the node score All GO terms with less than 10 sequences were removed tip nodes and all the nodes with a node score smaller than 12 applying an a of 0 4 were removed intermediate nodes This strategy allows the removal of terms that are less significant to a particular data set while at the same time it maintains frequently present terms at lower levels of specificity 9 2 Create Pie Chart Blast2GO offers pie charts as summary representations of annotation results Single GO level pie charts as well as multi level pie function is provided In the latter only the lowest GO
28. phs are called Combined Graphs The function generates joined GO DAGs to create overviews of the functional context of groups of annotations and sequences Combined Graph nodes are highlighted through a colour scale proportional to their number of sequences annotated to a given term This confluence score from now on denoted node Score takes into account the number of sequences converging at one GO term and at the same time penalizes by the distance to the term where each sequence was actually annotated Assigned sequences and scores can be displayed at the terms level Node Score The node score is calculated for each GO term in the DAG and takes into account the topology of the ontology and the number of sequences belonging i e annotated to a given node i e GO term The score is the sum of sequences directly or indirectly associated to a given GO term weighted by the distance of the term to the term of direct annotation i e the GO term the sequence is originally annotated to This weighting is achieved by multiplying the sequence number by a factor o 0 00 to the power of the distance between the term and the term of direct annotation see Equation for a mathematical expression In this way the node score is accumulative and the information of lower level GO terms is considered but the influence of more distant information i e annotations is suppressed decreased depending on the value of a This compensates for the drawback
29. tation with KEGG maps and InterPro annotation InterProScan Domain Searches GO Slim Reduction Analysis This tab hosts different options for the analysis of the available functional annota tion Includes graphical exploration through the Combined Graph Display and performing statistical analysis of GO distributions for groups of sequences e Combined Graphs This tab offers different descriptive statistics charts for the results of BLAST mapping and annotation e Pie Charts This tab offers different descriptive statistics charts for the results of BLAST mapping and annotation e Statistics Charts This tab offers different descriptive statistics charts for the results of BLAST mapping and annotation Various data import and export formats The Blast2GO sequence Table Colors Different colors indicate the status of each sequence Context menu Several options available for a single sequences are available via the right click context menu Mapped Manual Annot Annotated GoSlim Figure 3 1 Different colour codes indicating the status of the sequences Blast2GO Sequence Table Side Panel Select Allows to make sequence selections based on the sequences status colors sequence names functions GO terms or IDs or sequence descriptions View Switch the main table view from GO IDs to term show only selected sequences show hide GOs of InterProScan results and color highlight the different GO categories
30. zyme codes are obtained by mapping from equivalent GOs while Inter Pro motifs can directly be queried at the InterProScan web service A basic annotation process with Blast2GO consists of 3 steps blasting mapping and annotation These steps will be de scribed in this manual including further explanations and information on additional functions Gotz et al 2008 Copyright 2013 BioBam Bioinformatics S L 1 Quick Start This section gives a quick survey on a typical Blast2GO usage Detailed descriptions of the different steps and possibilities of this plugin are given in the remaining sections of this manual 1 Load data To start an annotation proccess load a Fasta sequence file Menu Import Standard Import File Type Blast2GO Blast Result Fasta File You can also add an example dataset to your Navigation Area from Edit Preferences gt Gerneral gt Blast2GO Create Blast2GO Example Dataset This dataset contains 10 sequences as plain sequences 2 Blast your sequences Please see BLAST at NCBI in the workbench Help Note If we already have a set of blasted sequences we can use the mport function from the main menu to create a new Blast2GO project I we want to add Blast results in XML format to an already existing project we will have to use the function File Import Blast Result XML from the main menu 3 Convert Blast to Blast2GO Project Go to Toolbox Manage Project Convert Data to Blast2GO
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