Blast2GO PRO Plug-in User Manual
Contents
1. Annotations e Figure 3 6 Different types of charts open next to each other in the workbench Copyright 2013 BioBam Bioinformatics S L 14 10 Enrichment Analysis Fisher s Exact Test Blast2GO offers the possibility of direct statistical analysis on gene function information A common analysis is the statistical assessment of GO term enrichment in a group of interesting genes when com pared to a reference group i e to asses the functional differences between two sets of functional annota tions e g GO function of two groups of genes This anaylsis is typically performed by a Fisher s Exact Test in combination with a robust False Discovery Rate FDR correction for multiple testing Fisher s exact test is a statistical significance test used in the analysis of contingency tables Although in practice it is employed when sample sizes are small it is valid for all sample sizes It is named after its inventor R A Fisher The false discovery rate FDR control is a statistical method used in multiple hypothesis testing to correct for multiple comparisons In a list of statistically significant findings FDR is used to control the expected proportion of incorrectly rejected null hypotheses false discoveries Here a Benjamini Hochberg correction is used The result is a list of statisticall2. Introduction Support pluginsupportOblast2g0 com Website http www blast2go com 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 Enzyme 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 G tz et al 2008 m o Im a F IS o lo o 3 S D a S o e cm o x ls El 3 m uw D o A 8 ur a o ile Edit vi D aa a el al a GAS I a S New sse Import i Undo ted P sisie Workspace ES 1 Plug ins Download Workflows Show Save Export Graphics Print Rede Cut Copy Paste Delete Marcia ne 100seqs mappe x 100seqs annot x Y fa tas Ja nr sequence na s n e 7 Eg CLC Data ES US E Blast2G0 Example Data Show i 100seqs in 100segs blastResult xml 67 9 ea GO Names GOAT Multi BLAST 100 sequences d v GO C
3. 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 gkn176 Copyright 2013 BioBam Bioinformatics S L 21
4. Bioinformatics S L Include more specific Functions 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 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 Import 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 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 gt Mapping Statistics 5 Annotation Go to Toolbox Annotation Annotation to run the anno
5. 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 the number of sequences annotated at different amounts of GO terms Reset Annotation Removes already obtained results for a given data set 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 Copyright 2013 BioBam Bioinformatics S L i Reset InterProScan Removes already obtained results for a given
6. 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 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 performs 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 External Datasets This function allows to combine an already existing Blast2GO B project with another dat or annot file In the case of dat files o
7. 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 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 components 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 possibil
8. similarities percentages shows the overall performance of the alignments and helps to adjust the annotation score in the annotation step Species distribution This chart gives a listing of the different species to which most sequences were aligned during the BLAST step Top Blast Species distribution This chart gives the species distribution of the Top BLAST HITs 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 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 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 e Annotation 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 Annotation distribution This chart informs about the number of GO terms assigned per sequence GO ter
9. 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 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 dy am de xz eee cox E ma ee zar c a Ni i c2 RE 9 o mw re e M GE ww A oo eo mon E zresa Pr 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 hydrolase activit
10. Blast2GO PRO Plug in User Manual CLC bio Genomics Workbench and Main Workbench Version 1 0 2 May 2013 e Dlast2go BioBam Bioinformatics S L Valencia Spain Contents Introduction 1 Quick Start 2 Blast2GO PRO Plug in Manual 4 1 Blasi2 GO PRO PISA seais c e da de aote e dd i ve td BS ee 4 1 1 Blast2GO PRO Plug in Toolbox functions 4 i2 The Blast2GO sequence Table 4 1 3 Blast2GO Sequence Table Side Panel ls 4 2 PELS D eue Tits et en eee Oe ce eh ek ees eur ed doc eee 5 3 NIGDDHIS 4 La E 404 45 a BE al do ip So V M Pee Se Eee RUE S 5 4 AMO TA LION EE atea fete ws a Os St ge a de ds Na N T 5 Kea PO a spend ets a A ae a UAE we OO DSS ew ee 7 6 GOR hk hy a cade Bete te Sek Me eH Hi ec rama a a he ee i 8 T Manage Projects ne SE EA A 9 wx RS 8 8 NWisc llancous 2 5 uem RAS e SG 8 9 DL AP ib a aras So Sethe Sl aho oro de o We ake e Se deeds ch chose HB 9 9 1 Create Combined Graphs 5 9 9 2 Create Pies Cart saae Sek X x box ARS ete 10 9 3 o Bua a Astin a eed ide E 10 10 Enrichment Analysis Fisher s Exact Test 15 10 1 Runa Fisher s Exact Test 2 20 00 00 eee ee ee 15 102 Ae Gets ees at A a ea Te cee eet wi A 15 DU nese Base ate eek nase ea O E Pe aaa a Set Soo S 16 10 4 Reduce to most specific terms 16 A Blast2GO Workflow 17 Please Cite 20 Copyright 2013 BioBam Bioinformatics S L
11. L 9 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 3 1 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 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 dis da seore g 3 ape eee 3 1 Ja desc g where e desc g represents all the descendant terms for a given GO term g dist g ga 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 gp g is the nu
12. T 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 Copyright 2013 BioBam Bioinformatics S L 8 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 Gene 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
13. a patens qa edicago truncatula EEE Picea sitchensis mmm unknown 1E un Nicotiana tabacum 1I wv Solanum tuberosum 4 EE i T Gossypium hirsutum 4E l D Lycopersicon esculentum 41 a Solanum lycopersicum 18 vi Malus x 4H Pisum sativum 4m l Elaeis guineensis 1H Citrus sinensis 1H Capsicum annuum 1H Brassica napus 70 Fragaria x 41 Triticum aestivum 1H l Chlamydomonas reinhardtii 4H Micromonas sp 1H Hevea brasiliensis 70 Branchiostoma floridae 4i others mE c Figure 3 5 A collection of different Blast2GO Charts ly Data Distribu X T4 GO Distributi X Data Distribution El GO Distribution by Level 2 Sequences 50 60 Seqs 0 10 20 30 40 70 80 90 100 0 5 10 15 20 25 30 35 40 45 50 55 60 65 Without Blast Results response to stimulus biological adhesion 1 E3l f i B E d cellular process sss n cell killing i 6 Pod i olic process Tm UN Without Blast Hits metab With Blast Results developmental process With Mapping Results Annotated Sequences Total Sequences Eja y ly GO level dist x GO level distribution E 35 30 25 20 15 5 5 ui TH 0 L 2 E 4 5 6 7 8 9 10 1 1 12 13 14 15 lv Eja y ls cb y
14. ame function GO terms or IDs description enzyme code or InterPro ID The selection type exact search whole word important for IDs has to match and case sensitivity can be chosen A search criteria can be provided via a search field Alternatively a list of sequence names or GO functions can be loaded via a text file Finally we have to decide if the search result has do be added or removed form the actual selection select or un select the sequences which match the criteria The search can be started by clicking the apply button 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 Theref
15. 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 R 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 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 Statistical 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
16. ategory Color 100seqs mapped dat InterPro Acc GO IDs 100seqs goslim dat Y se PRUPE ppaol 90 75 7 lose ames Codes a Blast2GO Basic WF 1813mg Only Selected Sequences N 4 Ih IL lectin domain y Selection Qr zenter search term J containing Select All receptor kinase Unselect All e Blast260 miraculin like Ej Blast protein 2 EC2 4 18 lg Blast Statistics Reset Blast BERI 9 63 Mapping anthranilate Mapping transferase le Mapping Statistics E 40s ribosomal Reset Mapping protein s6 like bs Annotation Lg Annotation Statistics _ fj Reset Annotation 9 B3 InterProScan thioredoxin chloroplastic l ike InterProS Merge InterProS ly InterPros t fj Reset InterProScan ke GO Slim Q GO Slim fj Reset GOSlim BE Ana Create Pie Chart 4 il m Processes Toolbox Favorites EC 1 2 1 36 IEC 1 2 1 3 F Invert Selection LLLAA 4 Delete Selection Select by State v Unblasted vj Mapped v Annotated v Manually Annotated y GO Slim Select by Name Seq ID Description Function GO Term GOAD InterProScan ID Enzyme Code C Exact Search O Case Sensitive Figure 1 1 Main Sequence Table of the Blast2GO PRO Plugin Copyright 2013 BioBam
17. e most specific information is reported lg Fisher s Exac x Differential GO term Distribution Sequences 3 0 Fisher s Exact Test Te oe t testSe went afta tl txt e Ontolo pedis hey are enriched in a te oe D when compared to e group using Fisher act T rd ultiple le Testing Correction of FDR Benjamini ai pt aa erg er ee E GO 0010055 oom G0 0000119 oxida m A m 2 wo 2 a E 2 a a a 9 y 2 0 m a a z 8 ES e 3 3 T 2 T a 3 3 a v E E 3 5 5 EI 2 3 D D 2 E 2 E s 8 E E b S gt e o T o un E 3 o E Los in N D a z z N un p gt over GO 0006119 over cobalamin EL Figure 3 7 Different types of Fisher s Exact Test results Copyright 2013 BioBam Bioinformatics S L 16 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 a 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 al
18. ed 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 Show Allows to change the visualization of several columns of the sequence table It is possible to switch between GO IDs or GO names show or hide the GO categories of each GO term show InterproScan Accession or the corresponding GO IDs choose between Enzyme codes or names br The option allows to show only selected sequences which can be helpful in combination with the select functions see below Copyright 2013 BioBam Bioinformatics S L 4 e Selection Allows to select un select invert and delete a given selection e Select by State Allows to make a selections based on the sequence status colours e Select by Allows to select sequences based on their n
19. er and under represented Gene Ontology functions in the test set compared to a reference set A two tailed test means therefore to test for over and under representation at the same time Note The correction for multiple testing FDR is higher in a two tailed test and therefore it is less likely to detect significant results since the number of performed test is doubled e Remove Double IDs This options allows you to automatically remove all sequences gene ids which are present in the test set and in the reference set at the same time By default double common IDs are only removed from the reference set Copyright 2013 BioBam Bioinformatics S L 15 10 3 Results Table Blast2GO offers several options to view the results of an Enrichment Analysis The table format shows a list of all the terms which add been included in the analyse With the side panel we can filter the results and can only visualize e g statistically significant results with a FDR p value smaller than 0 05 Graph The same results can also be visualized in form of a Enriched GO Graph The Enriched Graph shows the Gene Ontology graph of the significant terms with a node coloring which is proportional to the significance value p value This type of graphical representations helps to understand the biological context of the functional differences and to find pseudo redundancies in the parent child relationships of significant GO term A node filter va
20. g 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 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 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 Sequence similarity distribution This chart displays the distribution of all calculated sequence
21. gorithms 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 gt Add Element Blast2GO We add Convert Data to Blast2GO Project Mapping Annotation Mapping 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 functi
22. ia 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 a Blast2GO Basi X Add Element Validation successful gt Run Create Installer Blast2GO Project with blast or Multi Blast t Mapping Blast2GO Project mapped z Blast2GO Project ML Blast2GO Project mapped a Annotation Blast2GO Project mapped Blast2GO Project annotated en i Mapping Statistics Blast2GO Chart Mapping Chart 1 Blast2GO Project with Sequence Data Blast2GO Project annotated 1 InterProScan Blast2GO Project with InterProScan Blast2GO Project annotated lv A oS Statisti Blast2GO Chart ee Annotation Chart 1 Blast2GO Project with InterProScan f Merge InterProScan LA Blast2GO Project annotated IPS merged Blast2GO Project with InterProScan 1 Blast2GO Project wi
23. ities 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 R these graphs 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 Copyright 2013 BioBam Bioinformatics S
24. lue can be set for the p value or adjusted FDR p value In this way intermediate GO terms are not shown in the graph which reduced the overall size of the graph and graphs can be thinned out deleting these terms A node filter value determines the p value for the lowest nodes to be included in the graph GO Terms with a value higher than the given filter are not shown To perform an Enriched GO Graph a Fisher s Exact Test result is necessary Bar Chart An Enrichment Bar Chart shows for each significant GO term the amount percentage of sequences annotated with this term The Y axis shows significantly enriched GO terms and the X axis gives the relative frequency of each term Red bars correspond to the sequences of the test set and blue bars correspond to the reference or background dataset e g a whole genome To perform an Enrichment Bar Chart a Fisher s Exact Test result is necessary 10 4 Reduce to most specific terms This function allows to reduce the size of the result set of over represented GO terms useful in case of a very large list of enriched GO terms In many cases reported enriched functions have a parent child relationship and therefore these terms represent the same functional concept but at different levels of specificity In case of large result sets it can be convenient to filter the results by removing parent terms of already existing statistically significant child GO terms In this way only a reduced list of th
25. m 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 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 12 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 SA 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 sequences 0 10 000 20 000 Sequences UniPro
26. mber 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 filtering 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 3 3 the molecular functions of 1000 sequences
27. nly 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 8 Combine Blast2GO Projects This function allows to combine Blast2GO Projects which are already imported into the workbench 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 BLAST Result 8 Miscellaneous Blast Description Annotation The primary goal of Blast2GO is to assign functional labels in form of 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 BLAS
28. o 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 framework 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
29. oScan ID Enzyme Code _ Exact Search Case Sensitive Search LY 3 Browse File gt Select 2 Unselect ey 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 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 adjustable 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 DT 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 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 T
30. ons 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 workflow 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 anno
31. ore Blast2GO 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 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 Gl 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 Map
32. ping Removes already obtained results for a given dataset Copyright 2013 BioBam Bioinformatics S L 5 bg Blast2GO Egr Blast la Blast Statistics iy Reset Blast Ec Mapping ly Mapping les Mapping Statistics ff Reset Mapping k Annotation 4 Annotation lw Annotation Statistics T Reset Annotation E InterProScan InterProScan f Merge InterProScan lef InterProScan Statistics TM Reset InterProScan E GO Slim y GO Slim y Reset GOSlim Bg Analysis 2 Create Combined Graph e Create Pie Chart la Statistics Eg Enrichment Analysis ky Fisher s Exact Test J Fisher s Exact Test Y Reduce to Most Specific Terms 4 Create Enriched Graph lw Create Bar Chart lj Manage Projects Y Combine Datasets 3L Combine Blast2GO Projects Convert Data to Blast2GO Project kw Miscellaneous aca Blast Desc Annotation J Validate Annotations P Remove 1 Level Annotations da Run ANNEX EE Create Annotation Table create Blast2GO Example Dataset a Toolbox area T T E a E E SE A Show GO Names GO IDs GO Category Color InterPro Acc GO IDs Enzyme Names Codes Only Selected Sequences Selection Select All Unselect All Invert Selection Delete Selection Select by State Unblasted Mapped Annotated Manually Annotated GO Slim Select by Name Seq ID Q Description O Function GO Term GO ID J include more specific Functions InterPr
33. t with Blast2GO Project Sequence Data 6 Merge InterProScan Blast2GO Project 5 Blast2GO Project Run ANNEX Blast2GO Project 3 Blast2GO Project 8 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 19 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 20 Bibliography 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
34. tKB 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 1l L TIGR CMR IGC 4 e Annotation distribution GO level distribution g0 A z 400 2 SPIB dece LASA id z 200 icons E ste 100 J 9 38 ER 00 85 88 88 a EE Da 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 13 Direct GO Count MF InterProScan results 5eqs yy 800 0 25 30 fo 100 T ATP binding MEM BNN protein binding IE 400 structur D zinc ion binding w 300 electron carri _ iT i pam amp DNA binding M gan binding A E iron ion binding REN 3 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 2 500 3 000 Vitis vinifera q EE Populus trichocarpa A Arabidopsis thaliana EE i Oryza sativa IN Ricinus communis AS Zea mays q IN Glycine max q EE Sorghum bicolor Em Physcomitrell
35. tation 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 existing 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 v
36. tation 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 17 Sequence List or Multi Blast Convert Data to Blast2GO Project Blast2G0 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 18 Table 4 1 Detailed list of workflowable plugin 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 Projec
37. th InterProScan BA Statistics Final Blast2GO Project 1 Blast2GO Project Blast2GO Chart l Statistics Blast2GO Chart InterProScan Chart 1 Data Distribution Chart 1 E Cb y Figure 2 1 The work flow from the example data shows a similar scenario as described above The work flow accepts as input a Blast2GO project generated from via a Blast XML file import or a Multi Blast CLC Object It proceeds than with mapping annotation InterporScan merges the annotations obtained through Blast and Domain searches and generates several charts on the way The Blast2GO Project is saved after each step Copyright 2013 BioBam Bioinformatics S L 3 Blast2GO PRO Plug in Manual 1 Blast2GO PRO Plug in 1 1 1 2 1 3 Blast2GO PRO Plug in 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 annotation 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 Combin
38. uences genes included in the statistical test e Step 2 Select one or more Test Sets from the navigation area The selected datasets will be combined to one Please note that the given IDs have to match the sequence names of the Blast2GO Project selected in the first step It is allowed to select Blast2GO Projects or sequence gene ID lists in plain text format In the case of the plain text format please make sure to have only one ID per line An example can be found in the Blast2GO example datasets e Step3 Select one or more Reference Sets from the navigation area This is optional and the whole set selected in the first step will be used otherwise The selected datasets will be combined to one Please note that the given IDs have to match the sequence names of the Blast2GO Project selected in the first step It is allowed to select Blast2GO Projects or sequence gene ID lists in plain text format In the case of the plain text format please make sure to have only one ID per line An example can be found in the Blast2GO example datasets A e Step 4 Configure Fisher s Exact Test Parameters e Two Tailed In statistical significance testing a one tailed test or two tailed test are alternative ways of computing the statistical significance of a data set in terms of a test statistic depending on whether only one direction is considered extreme and unlikely or both directions are considered extreme This translates to ov
39. y 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 ion bindin
40. y significant Gene Ontology terms ranked by their adjusted p values Results can be viewed in several different ways like tabular format directly visualized on the Gene Ontology Grapf or as a bar chart always colouring statistically significant terms in red over represented and green under represented 10 1 Runa Fisher s Exact Test To perform the test we need to have a Blast2GO Project or various which contains the functional information of all seuquenes genes to be included in the statistical test In a second and third step we will select subsets of the first selection The the second step the test set is selected This can be done by choosing a Blast2GO Project already loaded in the workbench or via a text file which contains the coresponding sequence names of gene ids of the test set one each line In the third step we can than define a reference set This step is optional and if no reference is selected the dataset in the first step minus the sub set selected in the seconf step is choosen as a background or refenrece dataset The calculation of the p values for all functions can take several minutes depending on the size of the dataset and network connection speed Once the This table lists the adjusted p values of the Fisher s Exact Test for each GO term 10 2 Parameter e Step 1 Select one or more Blast2GO Projects Select one or more Blast2GO Projects which together contains the functional information of all seq
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