FIVA (Function Information Viewer and Analyzer) aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software assists in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes.

D. W. J. Bosman

E.-J. Blom

Hosack

J. B. T. M. Roerdink

L. Tijsma

O. P. Kuipers

R. Breitling

R. Silvis

S. A. F. T. van Hijum

Scheer

English

FIVA (Function Information Viewer and Analyzer) aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software assists in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes

Blom, E.J.

Bosman, D.W.

van Hijum, S.A F T

Breitling, R.

Tijsma, L.

Silvis, R.

Roerdink, J.B.T.M.

Kuipers, O.P.

NARCIS 

FIVA: Functional Information Viewer and Analyzer extracting biological knowledge from transcriptome data of prokaryotes

FIVA (Function Information Viewer and Analyzer) aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software assists in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes.</p

ARTS repository - University of Groningen

   University of GroningenFIVABlom, E.J.; Bosman, D.W.; van Hijum, S.A F T; Breitling, R.; Tijsma, L.; Silvis, R.; Roerdink,J.B.T.M.; Kuipers, O.P.Published in:Bioinformatics.DOI:10.1093/bioinformatics/btl658IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2007Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Blom, E. J., Bosman, D. W., van Hijum, S. A. F. T., Breitling, R., Tijsma, L., Silvis, R., Roerdink, J. B. T. M.,& Kuipers, O. P. (2007). FIVA: Functional Information Viewer and Analyzer extracting biological knowledgefrom transcriptome data of prokaryotes. Bioinformatics., 23(9), 1161-1163.https://doi.org/10.1093/bioinformatics/btl658CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 06-06-2022Utilization of multiple functional modules in FIVA  An analysis on the overlap between functional annotations was performed (see interpretation in http://bioinformatics.biol.rug.nl/standalone/fiva/data/informationAnalysis.pdf ). This analysis shows that the Gene Ontology module is the most informative annotation type for our test organism Bacillus subtilis. However, we also demonstrate that adding other annotations provide unique information on top of the GO information. The first information analysis was focused towards the available information content from annotation modules and their overlap. This analysis investigates the results from all annotation modules of a FIVA analysis on two transcriptome datasets (CodY and Mannitol/sorbitol dataset from http://bioinformatics.biol.rug.nl/standalone/fiva/suppMaterials.php).  Several other programs overlap with FIVA in terms of functionality and data sources employed (Khatri, P. et al. (2005). The Gene Ontology annotation module is used most often in these software packages, so this analysis will attempt to demonstrate that combining multiple functional modules into one tool is clearly beneficial to using one or a few modules.   Analysis on datasets  Two functional categories (urea cycle & competence) were found to be significantly overrepresented by functional annotations other than GO (metabolic pathways & interactions) for the CodY dataset. For the carbon sources dataset, five categories were found by the metabolic pathways module which were missed by GO. Below in Table 1A and Table 1B the missed categories by GO are described, together with the genes which were found to be involved in these categories. The genes from the functional categories were checked for relevant GO annotations. Table 1C describes the missed categories by GO and occurrences of relevant functional GO annotations. This table is not based on the genes of our model organism. This table was constructed to identify relevant GO occurrences which are present in the global GO database and describe the missing categories but are missing in the GO annotation for our organism. Furthermore, we checked for perfect GO hits (i.e. GO terms which match the missed category name perfect) but also for GO hits that are involved in the process described by the missed relevant category. We find that in most cases missed categories are available in the GO annotation but not sufficiently annotated. However, in some cases there is no appropriate GO annotation present.            Table 1A. Analysis of functional categories found by functional annotations other than GO (CodY dataset). This table describes some functional categories that were found overrepresented by the metabolic pathways module (column 1) and the gene involved in these categories (column 2). Relevant GO annotations are displayed in column 3 with their description in column 4. The number of genes that are linked to these GO annotations are indicated in column 5. For example; the metabolic pathway module analysis found an overrepresented category called Urea Cycle in which six genes were linked. Two GO annotations are available that could indicate the similar process. Only three genes are available in this GO annotation, of which two differentially expressed (ureA and ureB).  Original  functional category missed by GO Genes involved in functional category missed by GO Available relevant GO annotation Relevant GO description Total nr of genes with relevant GO      ureA: GO:0009039  Urea Cycle  (from the metabolic pathway annotation) argC, argJ, proH, rocD, urea, ureB ureB: GO:0009039 urease activity 3 genes comEA: GO:0030420  establishment of competence for transformation 2 genes Competence (from the regulatory interactions annotation) comC,comFA,comGA comK,nucA,recA, ydbK,yneA,yvyF,ywpHcomEA,smf smf: GO:0009294 DNA-mediated transformation 1 gene  Although some of the genes contain a relevant GO annotation (Table 1A), it is highly unlikely that they would have been identified in a significant overrepresented GO category due to the small size of the functional GO category. Without the metabolic pathway and regulatory interaction information, the two functional categories described in Table 1 would have been missed.             Table 1B. Analysis of functional categories found by functional annotations other than GO (Mannitol/sorbitol dataset).  For explanation on this table, see Table 1A.  Original  functional category missed by GO (all from the metabolic pathways annotation) Genes involved in functional category missed by GO Available relevant GO annotation Relevant GO description present in our model organism. Total nr of genes with relevant GO Valine, leucine and isoleucine degradation dhaS , mmgA , scoA , scoB , yngE , yngF , yngG , ysiB , ysnE , yusK , yusL , ywnH    Fructose and mannose metabolism levD , levE , levF , levG , mtlA , mtlD mtlD:  GO:0008926 mannitol-1-phosphate 5-dehydrogenase activity 1 gene Fatty Acid biosynthesis adhB , dhaS , mmgA , yngF , yngI , ysiB , yusK , yusL yusL: GO:0006631 fatty acid metabolism 3 genes Butanoate metabolism acoB , dhaS , mmgA , mmgB , scoA , scoB , yjmD , yngF , yngG , ysiB , ysnE , yusL , ywnH    Benzoate degradation mmgA , mmgB , yjmD , yngF , ysiB , ysnE , yusL , ywnH      From Table 1B it is clear that quite a number of functional categories that were found using the metabolic pathway annotation module were not detected by the GO annotation. The size of the relevant functional GO categories that were found for the carbon sources dataset would have prevented them to be identified as significantly overrepresented.          Table 1C. Analysis of functional categories found by functional annotations other than GO compared to available GO terms from the global GO database. This table summarizes the functional categories that were missed by GO. Perfect and second best hits are displayed in column two and three. Original  functional category missed by GO Available perfect hit from the GO annotation. Available hit (not perfect) from the GO annotation  Urea Cycle   urea cycle GO:0000050  Competence  n.a. competence pheromone activity GO:0030413 establishment of competence for transformation GO:0030420 regulation of establishment of competence for transformation GO:0045304 Valine, leucine and isoleucine degradation n.a. isoleucine metabolism GO:0006549 isoleucine catabolism GO:0006550 valine metabolism GO:0006573  valine catabolism GO:0006574    leucine catabolism GO:0006552 leucine metabolism GO:0006551 Fructose and mannose metabolism n.a. fructose metabolism GO:0006000 fructose catabolism GO:0006001 mannose metabolism GO:0006013   mannose catabolism GO:0019309 Fatty Acid biosynthesis fatty acid biosynthesis GO:0006633 fatty acid metabolism GO:0006631 fatty acid catabolism GO:0009062 Butanoate metabolism n.a. n.a. Benzoate degradation n.a. benzoate metabolism GO:0018874 From the above table it is evident that a relevant GO category is available for two of the seven missed categories. For the remaining five, one category was not described at all by the GO database (Benzoate degradation) and four were only described by other, more detailed, GO categories.  Concluding, in most cases of categories missed by the GO module the specific Bacillus subtilis annotations were missing. In particular, one category identified by the metabolic pathways module would have been missed completely by GO. Furthermore, the question remains whether the four categories, not described by a perfect hit, would have been found at all by GO. The found GO categories could easily be too detailed (no general GO category is available for these categories), and therefore missing the general picture. A solution could consist of adding more general nodes to the Gene Ontology graph. Until then, we conclude that combining multiple functional modules is clearly beneficial to using one module. 

FIVA:Functional Information Viewer and Analyzer extracting biological knowledge from transcriptome data of prokaryotes

FIVA (Function Information Viewer and Analyzer) aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software assists in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes. © 2007 The Author(s)

Blom, Evert Jan

Bosman, Dinne W J

van Hijum, Sacha A F T

Breitling, Rainer

Tijsma, Lars

Silvis, Remko

Roerdink, Jos B T M

Kuipers, Oscar P.

The University of Manchester - Institutional Repository

University of Groningen

   University of GroningenFIVABlom, E.J.; Bosman, D.W.; van Hijum, S.A F T; Breitling, R.; Tijsma, L.; Silvis, R.; Roerdink,J.B.T.M.; Kuipers, O.P.Published in:Bioinformatics.DOI:10.1093/bioinformatics/btl658IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2007Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Blom, E. J., Bosman, D. W., van Hijum, S. A. F. T., Breitling, R., Tijsma, L., Silvis, R., ... Kuipers, O. P.(2007). FIVA: Functional Information Viewer and Analyzer extracting biological knowledge fromtranscriptome data of prokaryotes. Bioinformatics., 23(9), 1161-1163.https://doi.org/10.1093/bioinformatics/btl658CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 12-11-2019Vol. 23 no. 9 2007, pages 1161–1163BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btl658Gene expressionFIVA: Functional Information Viewer and Analyzer extractingbiological knowledge from transcriptome data of prokaryotesEvert-Jan Blom1, Dinne W. J. Bosman2, Sacha A. F. T. van Hijum1, Rainer Breitling3,Lars Tijsma2, Remko Silvis1, Jos B. T. M. Roerdink2 and Oscar P. Kuipers1,1Molecular Genetics, Groningen Biomolecular Sciences, 2Institute for Mathematics and Computing Science and3Groningen Bioinformatics Centre, University of Groningen, PO Box 800, 9700 AV, Groningen, The NetherlandsReceived on October 19, 2006; revised on November 24, 2006; accepted on December 19, 2006Advance Access publication January 19, 2007Associate Editor: Dmitrij FrishmanABSTRACTSummary: FIVA (Function Information Viewer and Analyzer) aidsresearchers in the prokaryotic community to quickly identify relevantbiological processes following transcriptome analysis. Our softwareassists in functional profiling of large sets of genes and generatesa comprehensive overview of affected biological processes.Availability: http://bioinformatics.biol.rug.nl/standalone/fiva/Contact: o.p.kuipers@rug.nlSupplementary information: http://bioinformatics.biol.rug.nl/standalone/fiva/suppMaterials.php1 INTRODUCTIONGenome-wide expression profiles describing various cellularstates are obtained by use of DNA microarrays. Followingstatistical analysis of the raw gene expression values, data-driven methods such as unsupervised clustering allow groupingof genes based on their (temporal) expression patterns. Genesinvolved in similar cellular processes are expected to have ahigh probability of exhibiting similar expression patterns.Analysis and interpretation of these clusters is time-consumingand error-prone. Various applications have been developed tofunctionally profile differentially expressed genes from DNA-microarray experiments.Several of these, as reviewed by Khatri et al. (2005), overlapwith our application in terms of functionality and datasources employed. Many of these focus on higher organismsand therefore lack support for prokaryote gene identifiers.A number of applications support rarely used (Uniprot,GI accession) identifiers (Hosack et al., 2003) or only identifiersfor a limited set of organisms (Scheer et al., 2006). Moreover,with few exceptions, these software products use gene ontologyas their exclusive data source. In addition, the laborious taskof preprocessing the list containing differentially expressedgenes must be performed by a researcher. A stand-aloneapplication that focuses on prokaryotes is therefore essentialfor the fast-growing community of microbiologists making useof a plethora of (confidential) microbial genome sequences.We have developed FIVA (Functional Information Viewerand Analyzer). It uses several sources of biological annotationsto create an extensive functional profile based on geneexpression data. Furthermore, FIVA is capable of processinggroups of genes assembled by other criteria (e.g. functionalgrouping of genes which are not available in current annotationmodules). The significance of each biological process iscalculated to distinguish between significant and spuriousoccurrences.2 PROGRAM OVERVIEW2.1 InputThe input data for FIVA consists of transcriptome data andgenome annotation files (e.g. EMBL or Genbank), supplemen-ted with annotation information. FIVA supports a broadvariety of prokaryotic gene identifiers from the expressiondatasets, including locus tags and standard gene names (furtherdetails available in Supplementary Materials). Each annotationmodule uses functional information from one of the followingsources to classify the groups of genes and determine anysignificantly over-represented categories. (i) Gene ontology(ii) Metabolic pathways (iii) COG classes (iv) Regulatoryinteractions (v) UniProt keywords (vi) InterPro (vii) User-defined functional categories.2.2 ProcessingThe analysis in FIVA first involves the partitioning of the geneexpression data into up- and down-regulated fractions. Testingdifferent settings to partition the data is not a trivial task.FIVA offers the ability to automatically detect the optimalsettings for each individual experiment based on the number ofover-represented functional categories. In addition to thispartitioning method which is based on thresholds applied toa single experiment, the iGA algorithm (Breitling et al., 2004) isimplemented. This algorithm optimizes the parameters for eachfunctional category, which greatly improves the sensitivity ofthe analysis and increases the number of affected biologicalprocesses that can be reliably detected. Furthermore, theanalysis of gene expression data can also be applied onuser-defined gene lists.*To whom correspondence should be addressed. 2007 The Author(s)This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.A Fisher exact test is used to calculate P-values for eachcluster. This P-value describes the probability of observing aspecific enrichment of genes from a functional category ina cluster by chance. The number of false positives, due to thelarge number of statistical tests performed, are controlledby four multiple testing corrections (Benjamini/HochBerg,Bonferroni Step-down, Bonferroni and Benjamini Yekutieli).These are implemented to adjust the raw P-values (seeSupplementary Website).2.3 OutputFor each of the classification modules, a graphical representa-tion of the over-represented categories is generated. A previewis created from these results, from which a selection of theresults can be made (Fig. 1). In order to conveniently comparebiological phenomena occurring in different experiments,multiple experiments can be loaded simultaneously and aredisplayed as columns. Clickable links are present for eachcategory in the individual graphical map, providing detailedinformation for each cluster that contains an enrichment ofgenes from this category. Furthermore, FIVA uses the KEGGAPI (http://www.genome.jp/kegg/soap/) to communicate withthe KEGG database to color pathways based on the genedistribution in the clusters.2.4 Implementation and availabilityFIVA was programmed as a stand-alone application in Javausing the Eclipse (http://www.eclipse.org/) framework andruns on all Java-supporting operating systems (Mac OS,MS Windows, UNIX and Linux). The graphical output canalso be viewed by all web browsers that are able to processscalable vector graphics (SVG) or, to ensure portabilityFig. 1. Graphical output of a single annotation module. Genes from two DNA-microarray datasets (gluc: growth on glucitol compared to growth onglucose, man: growth on mannitol compared to glucose) were partitioned into up- and down-regulated clusters. The size of each cluster is displayedin blue underneath the cluster name. Numbers in each rectangle represent absolute values of occurrences. The significance of occurrences is visualizedin a colour gradient which is displayed at the bottom of the plot. The description of each category is placed at the right. S: annotations that aresignificant after multiple testing correction. Multiple testing correction results are visualized using five different symbols to distinguish between theindividual corrections. The number of symbols placed in each rectangle corresponds to the number of multiple testing corrections after which theannotation is found significant.E.-J.Blom et al.1162of the results, portable network graphics. More informationon the functionality of FIVA, as well as the results ofseveral test cases, can be found under the SupplementaryMaterials.3 CONCLUSIONA full information analysis was performed to assess theoverlap between the annotation modules (see SupplementaryWebsite). The gene ontology module is the most informativeannotation type for our test organism Bacillus subtilis andcovers a large portion of the information present in the othertypes. However, the utilization of multiple modules yieldsrelevant areas which are not shared by any of the othermodules. For our test cases, several relevant categories wereidentified by the metabolic pathways modules but were missedby the GO module (see Supplementary Website for moreinformation on this analysis). We conclude that combiningmultiple annotation sources into one tool is advantageouscompared to using only one or a few sources. The combinationof various complementary annotation sources, togetherwith the dynamic visualization and elaborate statisticalanalysis, allows a richer and more objective explorationof prokaryote expression data than any other available toolprovides.ACKNOWLEDGEMENTSThis study was fully supported by a grant from TheNetherlands Organization for Scientific Research and industrialpartners in the NWO-BMI project number 050.50.206 onComputational Genomics of Prokaryotes and by Center IOPGenomics. Work performed by SvH was supported by grantQLK3-CT-2001-01473 under the EU programme ‘Quality oflife and management of living resources: The cell factory’.We thank J.W.Veening for useful suggestions on experimentalprocedures and G. te Meerman for expert advice on thestatistical analysis. Funding to pay the Open Access chargeswas provided by the Molecular Genetics department of theUniversity of Groningen.Conflict of Interest: none declared.REFERENCESBreitling,R. et al. (2004) Iterative group analysis (iGA): a simple tool to enhancesensitivity and facilitate interpretation of microarray experiments. BMCBioinformatics, 5, 34.Hosack,D.A. et al. (2003) Identifying biological themes within lists of genes withease. Genome Biol., 4, R70.Khatri,P. et al. (2005) Ontological analysis of gene expression data: current tools,limitations, and open problems. Bioinformatics, 21, 3587–3595.Scheer,M. et al. (2006) JProGO: a novel tool for the functional interpretationof prokaryotic microarray data using Gene Ontology information.Nucleic Acids Res., 34, W510–515.FIVA: Functional Information Viewer and Analyzer1163

Blom, Evert-Jan,

Bosman, Dinne W.J.,

Hijum, Sacha A.F.T. van,

Breitling, Rainer,

Tijsma, Lars,

Silvis, Remko,

Roerdink, Jos B.T.M.,

Kuipers, Oscar P.,

University of Groningen Digital Archive

Dissertations of the University of Groningen

   University of GroningenFIVABlom, E.J.; Bosman, D.W.; van Hijum, S.A F T; Breitling, R.; Tijsma, L.; Silvis, R.; Roerdink,J.B.T.M.; Kuipers, O.P.Published in:Bioinformatics.DOI:10.1093/bioinformatics/btl658IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2007Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Blom, E. J., Bosman, D. W., van Hijum, S. A. F. T., Breitling, R., Tijsma, L., Silvis, R., Roerdink, J. B. T. M.,& Kuipers, O. P. (2007). FIVA: Functional Information Viewer and Analyzer extracting biological knowledgefrom transcriptome data of prokaryotes. Bioinformatics., 23(9), 1161-1163.https://doi.org/10.1093/bioinformatics/btl658CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 12-09-2023Utilization of multiple functional modules in FIVA  An analysis on the overlap between functional annotations was performed (see interpretation in http://bioinformatics.biol.rug.nl/standalone/fiva/data/informationAnalysis.pdf ). This analysis shows that the Gene Ontology module is the most informative annotation type for our test organism Bacillus subtilis. However, we also demonstrate that adding other annotations provide unique information on top of the GO information. The first information analysis was focused towards the available information content from annotation modules and their overlap. This analysis investigates the results from all annotation modules of a FIVA analysis on two transcriptome datasets (CodY and Mannitol/sorbitol dataset from http://bioinformatics.biol.rug.nl/standalone/fiva/suppMaterials.php).  Several other programs overlap with FIVA in terms of functionality and data sources employed (Khatri, P. et al. (2005). The Gene Ontology annotation module is used most often in these software packages, so this analysis will attempt to demonstrate that combining multiple functional modules into one tool is clearly beneficial to using one or a few modules.   Analysis on datasets  Two functional categories (urea cycle & competence) were found to be significantly overrepresented by functional annotations other than GO (metabolic pathways & interactions) for the CodY dataset. For the carbon sources dataset, five categories were found by the metabolic pathways module which were missed by GO. Below in Table 1A and Table 1B the missed categories by GO are described, together with the genes which were found to be involved in these categories. The genes from the functional categories were checked for relevant GO annotations. Table 1C describes the missed categories by GO and occurrences of relevant functional GO annotations. This table is not based on the genes of our model organism. This table was constructed to identify relevant GO occurrences which are present in the global GO database and describe the missing categories but are missing in the GO annotation for our organism. Furthermore, we checked for perfect GO hits (i.e. GO terms which match the missed category name perfect) but also for GO hits that are involved in the process described by the missed relevant category. We find that in most cases missed categories are available in the GO annotation but not sufficiently annotated. However, in some cases there is no appropriate GO annotation present.            Table 1A. Analysis of functional categories found by functional annotations other than GO (CodY dataset). This table describes some functional categories that were found overrepresented by the metabolic pathways module (column 1) and the gene involved in these categories (column 2). Relevant GO annotations are displayed in column 3 with their description in column 4. The number of genes that are linked to these GO annotations are indicated in column 5. For example; the metabolic pathway module analysis found an overrepresented category called Urea Cycle in which six genes were linked. Two GO annotations are available that could indicate the similar process. Only three genes are available in this GO annotation, of which two differentially expressed (ureA and ureB).  Original  functional category missed by GO Genes involved in functional category missed by GO Available relevant GO annotation Relevant GO description Total nr of genes with relevant GO      ureA: GO:0009039  Urea Cycle  (from the metabolic pathway annotation) argC, argJ, proH, rocD, urea, ureB ureB: GO:0009039 urease activity 3 genes comEA: GO:0030420  establishment of competence for transformation 2 genes Competence (from the regulatory interactions annotation) comC,comFA,comGA comK,nucA,recA, ydbK,yneA,yvyF,ywpHcomEA,smf smf: GO:0009294 DNA-mediated transformation 1 gene  Although some of the genes contain a relevant GO annotation (Table 1A), it is highly unlikely that they would have been identified in a significant overrepresented GO category due to the small size of the functional GO category. Without the metabolic pathway and regulatory interaction information, the two functional categories described in Table 1 would have been missed.             Table 1B. Analysis of functional categories found by functional annotations other than GO (Mannitol/sorbitol dataset).  For explanation on this table, see Table 1A.  Original  functional category missed by GO (all from the metabolic pathways annotation) Genes involved in functional category missed by GO Available relevant GO annotation Relevant GO description present in our model organism. Total nr of genes with relevant GO Valine, leucine and isoleucine degradation dhaS , mmgA , scoA , scoB , yngE , yngF , yngG , ysiB , ysnE , yusK , yusL , ywnH    Fructose and mannose metabolism levD , levE , levF , levG , mtlA , mtlD mtlD:  GO:0008926 mannitol-1-phosphate 5-dehydrogenase activity 1 gene Fatty Acid biosynthesis adhB , dhaS , mmgA , yngF , yngI , ysiB , yusK , yusL yusL: GO:0006631 fatty acid metabolism 3 genes Butanoate metabolism acoB , dhaS , mmgA , mmgB , scoA , scoB , yjmD , yngF , yngG , ysiB , ysnE , yusL , ywnH    Benzoate degradation mmgA , mmgB , yjmD , yngF , ysiB , ysnE , yusL , ywnH      From Table 1B it is clear that quite a number of functional categories that were found using the metabolic pathway annotation module were not detected by the GO annotation. The size of the relevant functional GO categories that were found for the carbon sources dataset would have prevented them to be identified as significantly overrepresented.          Table 1C. Analysis of functional categories found by functional annotations other than GO compared to available GO terms from the global GO database. This table summarizes the functional categories that were missed by GO. Perfect and second best hits are displayed in column two and three. Original  functional category missed by GO Available perfect hit from the GO annotation. Available hit (not perfect) from the GO annotation  Urea Cycle   urea cycle GO:0000050  Competence  n.a. competence pheromone activity GO:0030413 establishment of competence for transformation GO:0030420 regulation of establishment of competence for transformation GO:0045304 Valine, leucine and isoleucine degradation n.a. isoleucine metabolism GO:0006549 isoleucine catabolism GO:0006550 valine metabolism GO:0006573  valine catabolism GO:0006574    leucine catabolism GO:0006552 leucine metabolism GO:0006551 Fructose and mannose metabolism n.a. fructose metabolism GO:0006000 fructose catabolism GO:0006001 mannose metabolism GO:0006013   mannose catabolism GO:0019309 Fatty Acid biosynthesis fatty acid biosynthesis GO:0006633 fatty acid metabolism GO:0006631 fatty acid catabolism GO:0009062 Butanoate metabolism n.a. n.a. Benzoate degradation n.a. benzoate metabolism GO:0018874 From the above table it is evident that a relevant GO category is available for two of the seven missed categories. For the remaining five, one category was not described at all by the GO database (Benzoate degradation) and four were only described by other, more detailed, GO categories.  Concluding, in most cases of categories missed by the GO module the specific Bacillus subtilis annotations were missing. In particular, one category identified by the metabolic pathways module would have been missed completely by GO. Furthermore, the question remains whether the four categories, not described by a perfect hit, would have been found at all by GO. The found GO categories could easily be too detailed (no general GO category is available for these categories), and therefore missing the general picture. A solution could consist of adding more general nodes to the Gene Ontology graph. Until then, we conclude that combining multiple functional modules is clearly beneficial to using one module. 

Proceedings - University of Groningen

   University of GroningenFIVABlom, E.J.; Bosman, D.W.; van Hijum, S.A F T; Breitling, R.; Tijsma, L.; Silvis, R.; Roerdink,J.B.T.M.; Kuipers, O.P.Published in:Bioinformatics.DOI:10.1093/bioinformatics/btl658IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2007Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Blom, E. J., Bosman, D. W., van Hijum, S. A. F. T., Breitling, R., Tijsma, L., Silvis, R., Roerdink, J. B. T. M.,& Kuipers, O. P. (2007). FIVA: Functional Information Viewer and Analyzer extracting biological knowledgefrom transcriptome data of prokaryotes. Bioinformatics., 23(9), 1161-1163.https://doi.org/10.1093/bioinformatics/btl658CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 02-06-2022Utilization of multiple functional modules in FIVA  An analysis on the overlap between functional annotations was performed (see interpretation in http://bioinformatics.biol.rug.nl/standalone/fiva/data/informationAnalysis.pdf ). This analysis shows that the Gene Ontology module is the most informative annotation type for our test organism Bacillus subtilis. However, we also demonstrate that adding other annotations provide unique information on top of the GO information. The first information analysis was focused towards the available information content from annotation modules and their overlap. This analysis investigates the results from all annotation modules of a FIVA analysis on two transcriptome datasets (CodY and Mannitol/sorbitol dataset from http://bioinformatics.biol.rug.nl/standalone/fiva/suppMaterials.php).  Several other programs overlap with FIVA in terms of functionality and data sources employed (Khatri, P. et al. (2005). The Gene Ontology annotation module is used most often in these software packages, so this analysis will attempt to demonstrate that combining multiple functional modules into one tool is clearly beneficial to using one or a few modules.   Analysis on datasets  Two functional categories (urea cycle & competence) were found to be significantly overrepresented by functional annotations other than GO (metabolic pathways & interactions) for the CodY dataset. For the carbon sources dataset, five categories were found by the metabolic pathways module which were missed by GO. Below in Table 1A and Table 1B the missed categories by GO are described, together with the genes which were found to be involved in these categories. The genes from the functional categories were checked for relevant GO annotations. Table 1C describes the missed categories by GO and occurrences of relevant functional GO annotations. This table is not based on the genes of our model organism. This table was constructed to identify relevant GO occurrences which are present in the global GO database and describe the missing categories but are missing in the GO annotation for our organism. Furthermore, we checked for perfect GO hits (i.e. GO terms which match the missed category name perfect) but also for GO hits that are involved in the process described by the missed relevant category. We find that in most cases missed categories are available in the GO annotation but not sufficiently annotated. However, in some cases there is no appropriate GO annotation present.            Table 1A. Analysis of functional categories found by functional annotations other than GO (CodY dataset). This table describes some functional categories that were found overrepresented by the metabolic pathways module (column 1) and the gene involved in these categories (column 2). Relevant GO annotations are displayed in column 3 with their description in column 4. The number of genes that are linked to these GO annotations are indicated in column 5. For example; the metabolic pathway module analysis found an overrepresented category called Urea Cycle in which six genes were linked. Two GO annotations are available that could indicate the similar process. Only three genes are available in this GO annotation, of which two differentially expressed (ureA and ureB).  Original  functional category missed by GO Genes involved in functional category missed by GO Available relevant GO annotation Relevant GO description Total nr of genes with relevant GO      ureA: GO:0009039  Urea Cycle  (from the metabolic pathway annotation) argC, argJ, proH, rocD, urea, ureB ureB: GO:0009039 urease activity 3 genes comEA: GO:0030420  establishment of competence for transformation 2 genes Competence (from the regulatory interactions annotation) comC,comFA,comGA comK,nucA,recA, ydbK,yneA,yvyF,ywpHcomEA,smf smf: GO:0009294 DNA-mediated transformation 1 gene  Although some of the genes contain a relevant GO annotation (Table 1A), it is highly unlikely that they would have been identified in a significant overrepresented GO category due to the small size of the functional GO category. Without the metabolic pathway and regulatory interaction information, the two functional categories described in Table 1 would have been missed.             Table 1B. Analysis of functional categories found by functional annotations other than GO (Mannitol/sorbitol dataset).  For explanation on this table, see Table 1A.  Original  functional category missed by GO (all from the metabolic pathways annotation) Genes involved in functional category missed by GO Available relevant GO annotation Relevant GO description present in our model organism. Total nr of genes with relevant GO Valine, leucine and isoleucine degradation dhaS , mmgA , scoA , scoB , yngE , yngF , yngG , ysiB , ysnE , yusK , yusL , ywnH    Fructose and mannose metabolism levD , levE , levF , levG , mtlA , mtlD mtlD:  GO:0008926 mannitol-1-phosphate 5-dehydrogenase activity 1 gene Fatty Acid biosynthesis adhB , dhaS , mmgA , yngF , yngI , ysiB , yusK , yusL yusL: GO:0006631 fatty acid metabolism 3 genes Butanoate metabolism acoB , dhaS , mmgA , mmgB , scoA , scoB , yjmD , yngF , yngG , ysiB , ysnE , yusL , ywnH    Benzoate degradation mmgA , mmgB , yjmD , yngF , ysiB , ysnE , yusL , ywnH      From Table 1B it is clear that quite a number of functional categories that were found using the metabolic pathway annotation module were not detected by the GO annotation. The size of the relevant functional GO categories that were found for the carbon sources dataset would have prevented them to be identified as significantly overrepresented.          Table 1C. Analysis of functional categories found by functional annotations other than GO compared to available GO terms from the global GO database. This table summarizes the functional categories that were missed by GO. Perfect and second best hits are displayed in column two and three. Original  functional category missed by GO Available perfect hit from the GO annotation. Available hit (not perfect) from the GO annotation  Urea Cycle   urea cycle GO:0000050  Competence  n.a. competence pheromone activity GO:0030413 establishment of competence for transformation GO:0030420 regulation of establishment of competence for transformation GO:0045304 Valine, leucine and isoleucine degradation n.a. isoleucine metabolism GO:0006549 isoleucine catabolism GO:0006550 valine metabolism GO:0006573  valine catabolism GO:0006574    leucine catabolism GO:0006552 leucine metabolism GO:0006551 Fructose and mannose metabolism n.a. fructose metabolism GO:0006000 fructose catabolism GO:0006001 mannose metabolism GO:0006013   mannose catabolism GO:0019309 Fatty Acid biosynthesis fatty acid biosynthesis GO:0006633 fatty acid metabolism GO:0006631 fatty acid catabolism GO:0009062 Butanoate metabolism n.a. n.a. Benzoate degradation n.a. benzoate metabolism GO:0018874 From the above table it is evident that a relevant GO category is available for two of the seven missed categories. For the remaining five, one category was not described at all by the GO database (Benzoate degradation) and four were only described by other, more detailed, GO categories.  Concluding, in most cases of categories missed by the GO module the specific Bacillus subtilis annotations were missing. In particular, one category identified by the metabolic pathways module would have been missed completely by GO. Furthermore, the question remains whether the four categories, not described by a perfect hit, would have been found at all by GO. The found GO categories could easily be too detailed (no general GO category is available for these categories), and therefore missing the general picture. A solution could consist of adding more general nodes to the Gene Ontology graph. Until then, we conclude that combining multiple functional modules is clearly beneficial to using one module. 

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FIVA:Functional Information Viewer and Analyzer extracting biological knowledge from transcriptome data of prokaryotes

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