Blast2GO

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High throughput functional annotation and
analysis with the Blast2GO suite
Ana Conesa Bioinformatics Department Centro de
Investigaciones Prínicpe Felipe aconesa_at_cipf.es
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Credits
Blast2GO Development
Biomedical Informatics UPV, Valencia Juan Miguel
Gómez Montserrat Robles
Bioinformatics Department CIPF, Valencia
Bioinformatics Department CIPF, Valencia Ana
Conesa Stefan Goetz
Centro de Genómica IVIA, Valencia Javier
Terol Manuel Talón
Blast2GO special thanks to ANNEX Simen Myhre,
Henrik Tveit (MTNU)? GOSSIP Nils Blüthgen
(MicroDiscovery GmbH)? ZVTM Emmanuel Pietriga
(INRIA)? goslim.tair.obo Suparna Mundodi (TAIR)?
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Motivation
Numerous EST/genome projects
Large amounts of NEW sequence data
Functional Genomics Studies
Need of Functional Annotation
Which kind of tool?
Easy to set up run Versatil
Universal High-throughput interactive Combine
annotation function analysis
www.blast2go.org
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Gene Ontology based annotation
Molecular Function Biological Process Cellular
Component
more general
more specific
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Concepts of automatic annotation
Similarity between Sequences
Consistency of assigned annotation
Precision vs. recall
Resolution Level in GO hierarchy
Selection of recovered annotation data
Quality of existence annotation
B2G Annotation Rule
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Blast2GO Annotation Rule
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Main functions within Blast2GO
GO Second Layer
GO-Slim
Manual Curation
Validation
Annotation (GO,IPR,EC)?
Statistics
InterProScan
Graph Visualization
KEGG maps
Additional Features
Enrichment
Pipeline
Batch Mode
costumDB
localB2GDB
Compare
GeneIDs
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Blast2GO Schema
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Blast2GO use
Species Citrus, nicotiana, maize, soybean,
tomato, grape Streptococcus, Trichoderma,
Schistosoma, Cyanobacteria European
Flounder,pig, flidder crab, rat, honneybee,
human Metagenome projects
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Where to find Blast2GO
Web http//www.blast2go.orghttp//blast2go.bio
info.cipf.es http//www.geneontology.orghttp//gr
oups.google.com/group/Blast2GO
More infoBioinformatics 2005 21
3674-3676 Blast2GO tutorial http//www.blast2go.o
rg
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Blast2GO Guided Tour
Ana Conesa Bioinformatics Department Centro de
Investigaciones Prínicpe Felipe aconesa_at_cipf.es
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Start Blast2GO
www.blast2go.org

• Desktop application
• Java webstart technology
• Internet connection

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Load Sequences
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Run BLAST search
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BLAST results
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Blast Distribution Charts
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Exercise 1

• Launch Blast2GO
• Open FASTA file (unizip examples.zip)?
• Browse number of sequences and sequence length
• Unselect all sequences
• Select 5 sequences
• Run Blast against NCBI nr (change parameters if
  desidered)?

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Exercise 2

• Open blast_example.dat
• Examine Distribution charts

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Mapping
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Resources of mapping

• GO mapping resources
• Full Gene Ontology DB
• NCBI Flat Files gene2accession (4 079 414
  entries) gene_info (1 635 614 entries)
• PIR - Non-Redundant Reference Protein Database
  including PSD, UniProt, Swiss-Prot, TrEMBL,
  RefSeq, GenPept y PDB

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Annotation
GO
DAG Validation Annex
GOSlim
EC/KEGG
InterPro
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Gene Ontology annotation
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Annotation Charts
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Exercise 3

• Select 10 first sequences
• Run Mapping and Annotation
• Select non annotated sequences and re-annotate
  with milder parameters
• Lo annotation_example.dat file
• Visualize Results on Mapping/Annotation Charts

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Sequence menu
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Modulation of annotation
Extend annotation by the GO Second Layer
Molecular Function
acts in
is involved in
Biological Process
Cellular Component
Myhre et al, Bioinformatics 2006
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Exercise 4

• Browse BlastResults to (select one sequence and
  use sequence menu)
• Draw annotation graph
• View Annotations
• Edit/change annotation
• Select a few sequences to run GoSlim
• Run Annex

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Enzyme annotation and Kegg Maps
GO ? Enzyme Codes ? KEGG maps
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InterproScan
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Exercise 5

• Select a few sequences to run InterProScan
• Change terms view GO ID/term, IPS/GO
• Merge IPS results with Blast Annotations
• Load annot_interpro_annex_example.dat
• Export GO Annotations
• Export IPS Annotations
• Save Project and Sequence Table

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Visualization
GO Graph Visualization as tool to explore
data Interactive and zoomable graphs Color
graphs highlighting areas of interest
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Visualization Pies
Level and Multilevel Charts
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Exercise 6

• Select some sequences using select by names
  function (use test.example.txt)
• Create a GO Combined Graph
• Create Pies at level 4 and Multilevel Pie at
  score 3
• Play with filters to simplify the graph (set
  score filter to 3)
• Export GO Graph data as table and visualize

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Functional analysis with B2G
Enrichment Analysis (Fisher)?
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Exercise 7

• Run Enrichment Analysis using test and reference
  set files
• Create Bar Chart
• Create Enriched Graph and modulate number of
  nodes
• Export results