This presentation is designed to show the features of four ‘third-party’GO analysis tools. These tools and others listed on http://www.geneontology.org/GO.tools.shtml#micro can be used in proteomics studies to view GO terms associated with a list of proteins obtained from high-throughput experiments and their statistical significancecompared with a reference set of proteins.*

Each presentation was prepared by the developers of the tools, using for the analysis a list of human cardiovascular-related protein accessions (or in the case of Blast2GO, the equivalent bovine protein sequences).

*All of these tools have been created outside of the GO Consortium. The articles authors do not intend to recommend any tool, merely demonstrate how GO analysis of proteome sets could be performed using some of these tools. We advise researchers to try several different tools to find one which suits their needs.

IntroductionIntroduction

Blast2GO Slide 4

FatiGO Slide 13

Onto-Express Slide 20

Ontologizer Slide 27

ContentsContents

Accession list I Slide 35

Accession list II Slide 36

Functional Annotation: First, the BLAST step to obtain the homologue sequences for the query sequences. Second, the actual GO annotation by applying the Blast2GO method which, basically, transfers the most confident and appropriate GO annotations to the novel sequences. Statistical charts help here to understand and interpret the annotation results.

Visualization: This step allows the users to get an overall idea of the assigned GO annotations of the sequence dataset making use of GO's graph structure.

Conesa, A., Götz, S., García-Gómez, J.M., Terol, J., Talón, M. & Robles, M. (2005). Blast2GO: A universal tool for annotation, visualization and

analysis in functional genomics research. Bioinformatics 21: 3674-3676

Blast2GO in BabelomicsBlast2GO in Babelomics

Bioinformatics DepartmentCentro de Investigación Príncipe Felipe (CIPF)

babelomics@cipf.es

http://babelomics.bioinfo.cipf.es

2. GO terms are selected from this original pool to extract the most reliable annotationOnce all this information is gathered, an annotation score is computed for each {GO,Query Sequence} pair. Only the most specific GO term within a branch of the GO is assigned to the query sequence, and this assignment is dependent on the 'annotation score', the threshold for which is preset by the user. The annotation score is computed as:

Annotation score{GO, Seq} = (max.sim * ECw) + (#GO-1 * GOw)Annotation score{GO, Seq} = (max.sim * ECw) + (#GO-1 * GOw)

where: max.sim: is the maximal value of similarity between the query and hit sequences that have the given GO annotation ECw: is the weight given to the Evidence Code of the original annotation. Blast2GO has defined values for these weights, which can also be modified by the user. In general, ECw = 1 for experimental evidence codes and ECw < 1 for non-experimental evidence codes. #GO: is the number of annotated children terms GOw: is the weight given to the contribution of annotated children term to a given term

Annotation is the process of assigning functional categories to gene or gene products. In Blast2GO this assignment is performed for each sequence based on the information available for the homologous sequences retrieved by BLAST. Blast2GO annotation proceeds through a 2 step strategy:

Functional Annotation with Blast2GO

1. All GO terms for the BLAST hit sequences are collectedFor the first step, BLAST results are parsed and the identifiers of the BLAST hits are found and used to query the Gene Ontology database to recover associated functional terms. Also the evidence code of each particular annotation is recovered. The evidence codes indicate how the functional assignment in the Gene Ontology database has been obtained.

Upload your sequence file in FASTA format, choose the appropriate BLAST parameters and database (blastp for protein sequences) and press RUN

The homology search is the first and most time consuming step when attempting to transfer functional information from similar sequences to uncharacterized sequence data. This simple tool gives you the option to perform high-throughput BLAST searches against several protein databases, keep processes running until they are finished monitoring its actual status and saving the generated alignments as XML file. These XML-files can than be used as input data for the Blast2GO annotation method.

In this tab you can see the actual status of your job and for big datasets come back later to retrieve the results.

The BLAST Step (1/2)

Open the results with this link

Save your results as an XML file.

The BLAST Step (2/2)

Evidence code weights can be set to in/decrease the influence of different kinds of annotation

evidence e.g. automatically generated source annotation

Annotation rule parameters: e-Value cut-off as minimal quality criteria annotation rule cut-off (coverage vs. exactness) GO-Weight (more general vs. more specific terms) define a minimal alignment length allowed for function transfer

Upload and parse your BLAST results in NCBI's

XML format applying several filters

The Annotation Step

Start the annotation assignment

The Blast2GO web tool generates a multitude of statistical charts to understand the underlying dataset and to better interpret the generated

annotation results

A chart showing the e-value distribution of the BLAST results

A chart showing from which source databases the transferred GO terms were originally coming from

The result table to browse and export the generated annotations

review

browse

export

A chart showing the most frequent GO terms throughout the dataset

A chart showing the success of the annotations process giving the number of successfully ‘BLASTed’, GO-mapped and annotated sequences

A chart showing how many GO terms were assigned to how many sequences

A chart showing the distribution of the different evidence codes throughout the GO terms per sequence

A chart showing the number of sequences annotated at a certain GO level and category

A chart showing the distribution of BLAST sequence similarities

A chart showing the distribution of the different evidence codes throughout the GO terms per BLAST hit

A chart showing the distribution of the different species from which the BLAST hits originate

Open and save the results in a tabular format for further use in the GO-Graph-Viewer or as download data in Blast2GO project format for direct import into Blast2GO

Saving and exporting results

Blast2GO annotations are exported in a tabular format: SeqId<tab>GOterm<tab>SeqDesc

Browse the generated annotations in the result table

Start the interactive graph visualization tool with Java Web Start

Visualization: The GO-Graph-ViewerThe DAG viewer tool generates joined Gene Ontology graphs (DAGs) to create overviews of the functional context of groups of sequences. Interactive graph visualization allows the navigation of large and unwieldy graphs often generated when trying to biologically explore large sets of sequence annotations. Zoom and graph navigation is provided through the DAG viewer Java Web Start tool.

Define graph filtering parameters for more dense and informative graphs

Save parts of your graphs in high resolution images to better communicate your results

Upload your Blast2GO generated annotations

FatiGOFunctional enrichment analysis

Al-Shahrour, F., et al. (2005), Babelomics: a suite of web-tools for functional annotation and analysis of group of genes in high-throughput experiments, Nucleic Acids Research, 33, W460-W464

Al-Shahrour, F., et al. (2004), FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes, Bioinformatics, 20, 578-580

Bioinformatics DepartmentCentro de Investigación Príncipe Felipe (CIPF)

http://www.fatigo.orghttp://www.babelomics.org

babelomics@cipf.es

Select your organism *Several types of identifier are acceptable,

such as UniProtKB, Ensembl IDs, HGNC symbols, RefSeq, Entrez Gene etc.

Enter your list or file of genes/proteins*

In this example, list #1 is a list of BHF-UCLannotated cardiovascular-related proteins (see Slide 35) and list #2 is the “Rest of genome”

Click options to filter the database

(optional)

Select the database(s) you

want to query

Filter Tool

Babelomics allows for sub-selection of gene annotations, in which gene modules are based, in order to test hypotheses in a more focused and sensitive manner. Removing from the analysis modules whose testing is unnecessary and superfluous increases the power of the tests in the multiple-testing adjustment step.

Use the level of the DAG and the evidence code as filtering criteria

Select subsets of annotations based on keywords and on the

size of the gene module

Results of GO analysis

Level 3 is less-granular terms. Level 9 is more-granular terms.

The number of annotated proteins per GO level is displayed

FatiGO returns a list of GO terms which are over-represented in the list of interest, in this case the BHF-UCL list. For Biological Process terms at level 3 of the ontology, the terms that are over-represented in the BHF-UCL list include muscle contraction, cell cycle and anatomical structure development.

Low p-value = more significant

The proteins from your query set that are annotated to each GO

term are listed

Best p-value

FatiGO shows terms deeper in the ontology, at level 6, which are over-represented in the BHF-UCL list (but not necessarily significantly – compare p-values) such as regulation of progression through cell cycle, heart development and cholesterol absorption. These are all processes you would expect cardiovascular-related proteins to be involved in.

The DAG viewer tool allows visualization of the significant GO terms as a GO graph.The GO term names are displayed together with the annotation score.

GO-Graph-Viewer Tool

You can upload your FatiGO results to the interactive graph visualization tool

Onto-Express Features at a Glance

Purvesh Khatri (purvesh@cs.wayne.edu)Sorin Draghici (sod@cs.wayne.edu)

Intelligent Systems and Bioinformatics LabDepartment of Computer Science

Wayne State University

http://vortex.cs.wayne.edu/projects.htm#Onto-Express

Input interface

Select organism

Select type of IDs in input file

Choose from more than 300 microarrays.

If an array of choice is not

available, use your own reference.

Choose a statistical distribution from:1. hypergeometric2. binomial3. chi-square

Choose a correction for multiple hypotheses from:1. Bonferroni, 2. FDR,3. Holm, 4. Sidak

Supported input types are GenBank accession numbers, UniGene cluster IDs, Entrez Gene IDs, gene symbols, Affymetrix probe IDs, any of the IDs used in GO database.

Results – Flat view

Results – tree view

• Choose a level to expand the GO tree and click “Expand” button.

• Only the GO terms with at least one input gene are displayed in the tree.

Results – chromosome view

• Chromosome information is supported for human, mouse and rat. It displays number of genes on each chromosome and their positions.

• Clicking on “NCBI Genome view” links out to NCBI Mapviewer.

Results – single gene view

Selecting “show in gene view” in the tree view displays the annotations for the selected gene in the GO hierarchy in the single gene view.

References

• Purvesh Khatri, Sorin Draghici, G. Charles Ostermeier, Stephen A. Krawetz. Profiling Gene Expression Using Onto-Express. Genomics, 79(2):266-270, February 2002.

• Sorin Draghici, Purvesh Khatri, Rui P. Martins, G. Charles Ostermeier and Stephen A.Krawetz. Global functional profiling of gene expression. Genomics 81(2):98-104, February 2003.

• Purvesh Khatri and Sorin Draghici. Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics, 21(18):3587-95, September 2005.

• http://vortex.cs.wayne.edu/projects.htm.

Ontologizer

http://compbio.charite.de/ontologizer/

Institute for Medical GeneticsCharité Universitätsmedizin Berlin

Ontologizer Open Source Team

located at

Robinson P.N., Wollstein A., Böhme U., Beattie B. Ontologizing gene-expression microarray data: characterizing clusters with Gene Ontology. Bioinformatics. 2004 Apr 12;20(6):979-81.

Grossman S., Bauer S., Robinson P.N., Vingron M. Improved detection of overrepresentation of Gene Ontology annotations with parent child analysis. Bioinformatics. 2007 Nov 15;23(22):3024-31.

Ontologizer – Setting up a Project

• Ontology, defines the GO structure

• Annotations, map genes to GO terms

There are several predefined entries for various settings…

…or you may specify the fields manually.

Inputs:

The induced graph of these terms can be displayed.

Annotated identifiers are highlighted on the fly.

Ontologizer – Editing Sets of Identifiers

Mouse hovering reveals direct annotations.

No annotation for this one

Of interest here are two lists of identifiers – study and population.*

*In this example the study list is a list of BHF-UCL annotated cardiovascular-related proteins (see Slide 35) and the population list is a random list of human UniProtKB accessions.

Choose analysis method; parent-child takes account of the ontology structure, term-for-term treats

each term independently.

Ontologizer – Overview

But multiple projects may reside in the workspace.

A list of terms is displayed. The shading indicates significance – darker shading is more

significant.

Click on a term to display its position in the

ontology, definition and the proteins annotated to

it and its parents.

Ontologizer – Results

The term highlighted in the table will also be highlighted red in the graph.

Yellow = Molecular Function

Pink = Cellular Component

Green = Biological Process

Ontologizer – Graphical View of Results

Ontologizer – What Else?

• Can be easily invoked from the Web.

• Input files can be located remotely.

• Several procedures of multiple testing correction are supported.

• Results can be filtered and stored in a tabular as well as in a graphical fashion.

• A command line version is available.

AcknowledgmentsThe authors wish to thank the developers of the tools for preparing

these presentations as follows;

• FatiGO Fatima Al-Shahrour

• Blast2GO Stefan Götz

• Ontologizer Sebastian Bauer and Peter Robinson

• Onto-Express Sorin Draghici and Purvesh Khatri

List of human UniProtKB accessions used in FatiGO, Onto-Expressand Ontologizer analyses

O00273

O60543

O75955

O95477

P00519

P01127

P01137

P01375

P01584

P02647

P02649

P02652

P02655

P02656

P04114

P04180

P05231

P05976

P06727

P06741

P06858

P07203

P08590

P09493

P09958

P10253

P10636

P10916

P11597

P11802

P12643

P12829

P12830

P13501

P16519

P17947

P18510

P22301

P24385

P25098

P25103

P29120

P30279

P30281

P34947

P35226

P36897

P37173

P38936

P40337

P42684

P42771

P42772

P42773

P45379

P45844

P46527

P49918

P50150

P55273

P55290

P61812

P84022

Q00534

Q00872

Q01449

Q13485

Q14114

Q14896

Q15796

Q16665

Q5JRA6

Q6PGN9

Q6Q788

Q86Y82

Q8N726

Q8TBM5

Q92673

Q96AB3

Q96N67

Q9BQE4

Q9H172

Q9H1R3

Q9H221

Q9H222

Q9HC96

Q9UKX2

Q9UNQ0

Q9UPY8

Q9Y5C1

Q9Y623

List of bovine UniProtKB accessions used in Blast2GO analysis

A0JNJ5

A1A3Z1

A4FUX1

A4FUZ9

A4IFM7

A5PJI9

A5PKM2

A6QLS3

A6QP89

A7MBB9

O46680

O77482

O97919

P00435

P05363

P09428

P11151

P13789

P15497

P18341

P19034

P19035

P21146

P21214

P26892

P43249

P43480

P81644

P85100

Q03247

Q06599

Q08DE0

Q0P5D3

Q0VC16

Q0VC37

Q0VD56

Q1HE26

Q1RMM7

Q1W668

Q24JY8

Q28193

Q29RJ9

Q29RV0

Q2KI22

Q2KI76

Q2KIW4

Q2KJB3

Q2KJD8

Q2KJD8

Q2TBI0

Q32KX0

Q32KX7

Q32KY4

Q32PJ1

Q32PJ2

Q3B7N0

Q3MHH5

Q3SYR3

Q3SZE5

Q3SZE5

Q4GZT4

Q4TTZ1

Q4ZJV8

Q4ZJV9

Q58D48

Q5E9I5

Q5KR49

Q6R8F2

Q9BE40

Q9BE41

Q9GLR0

Q9GLR1

Q9MYM4

Q9XTA5