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GO based data analysis. Iowa State Workshop 11 June 2009. All tools and materials from this workshop are available online at the AgBase database Educational Resources link. For continuing support and assistance please contact: agbase@cse.msstate.edu.

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go based data analysis

GO based data analysis

Iowa State Workshop

11 June 2009

slide2

All tools and materials from this workshop are available online at the AgBase database Educational Resources link.

  • For continuing support and assistance please contact:

agbase@cse.msstate.edu

This workshop is supported by USDA CSREES grant number MISV-329140.

agbase protein annotation process

GOanna

GOSlimViewer

AgBase protein annotation process

Protein identifiers or Fasta format

GORetriever

Annotated Proteins

Proteins with no annotations

hypothesis generating
Hypothesis generating
  • Gene Ontology enrichment analysis

GO terms that are statistically (Fisher’s exact test) over or underrepresented in a set of genes

  • Annotation Clustering

groupsimilar annotations based on the hypothesis that they should have similar gene members 

slide5

Some resources

  • DAVID: http://david.abcc.ncifcrf.gov/
  • GOStat: http://gostat.wehi.edu.au/
  • EasyGO: http://bioinformatics.cau.edu.cn/easygo/
  • AmiGO http://amigo.geneontology.org/cgi-bin/amigo/term_enrichment(does not use IEA)
  • Onto-Express & OE2GOhttp://vortex.cs.wayne.edu/projects.htm
  • GOEAST http://omicslab.genetics.ac.cn/GOEAST
  • http://www.geneontology.org/GO.tools.shtml
  • Comparison of enrichment analysis tools : Nucleic Acids Research, 2009, Vol. 37, No. 1 1–13

(Tool_Comparison_09.pdf)

DAVID and EasyGO analysis included DAVID&EasyGo.ppt

slide9

http://vortex.cs.wayne.edu/ontoexpress

Onto-Express analysis instructions are

Available in onto-express.ppt

comparison
Comparison
  • Onto-Express , EasyGO, GOstat and DAVID
  • Test set: 60 randomly selected chicken genes
  • Used AgBase GO annotations as baseline annotations

Vandenberg et al (BMC Bioinformatics, in review)

networks pathways

Networks & Pathways

Iowa State Workshop

11 June 2009

multiple data analysis platforms
Multiple data analysis platforms

Proteomics

LIST

Transcriptomics

ESTs

our original aim understand biological phenomena
Our original aim….…understand biological phenomena….
  • Bits and pieces of information
  • Do not have the full picture
  • How do we get back to BIOLOGY in this digital information landscape?
what do we know about biological systems

Francis Crick, 1958

What do we know about biological systems ….
  • biological systems are dynamic, not static
  • how molecules interact is key to understanding complex systems
types of interactions
Types of interactions
  • protein (enzyme) – metabolite (ligand)
      • metabolic pathways
  • protein – protein
      • cell signaling pathways, protein complexes
  • protein – gene
      • genetic networks
slide19

STRING Database

Sod1

Mus musculus

http://string.embl.de/

slide21

Database/URL/FTP

DIP http://dip.doe-mbi.ucla.edu

BIND http://bind.ca

MPact/MIPS http://mips.gsf.de/services/ppi

STRING http://string.embl.de

MINT http://mint.bio.uniroma2.it/mint

IntAct http://www.ebi.ac.uk/intact

BioGRID http://www.thebiogrid.org

HPRD http://www.hprd.org

ProtCom http://www.ces.clemson.edu/compbio/ProtCom

3did, Interprets http://gatealoy.pcb.ub.es/3did/

Pibase, Modbase http://alto.compbio.ucsf.edu/pibase

CBM ftp://ftp.ncbi.nlm.nih.gov/pub/cbm

SCOPPI http://www.scoppi.org/

iPfam http://www.sanger.ac.uk/Software/Pfam/iPfam

InterDom http://interdom.lit.org.sg

DIMA http://mips.gsf.de/genre/proj/dima/index.html

Prolinks http://prolinks.doe-mbi.ucla.edu/cgibin/functionator/pronav/

Predictome http://predictome.bu.edu/

PLoS Computational Biology March 2007, Volume 3 e42

pathways networks
Pathways & Networks
  • A network is a collection of interactions
  • Pathways are a subset of networks

Network of interacting proteins that carry out biological functions such as metabolism and signal transduction

  • All pathways are networks of interactions
  • NOT ALL NETWORKS ARE PATHWAYS
biological networks
Biological Networks
  • Networks often represented as graphs
  • Nodes represent proteins or genes that code for proteins
  • Edges represent the functional links between nodes (ex regulation)
  • Small changes in graph’s topology/architecture can result in the emergence of novel properties
slide24

Yeast Protein-Protein Interaction Map

Nature411, 2001,

H. Jeong, et al

slide25

Some resources

KEGG http://www.genome.jp/kegg/pathway.html/

BioCyc http://www.biocyc.org/

Reactome http://www.reactome.org/

GenMAPP http://www.genmapp.org/

BioCarta http://www.biocarta.com/

Pathguide – the pathway resource list

http://www.pathguide.org/

slide27

Pathguide

Statistics

Gallus gallus is missing

systems biology workflow
Systems Biology Workflow

Nanduri & McCarthy CAB reviews, 2008

systems biology workflow1
Systems Biology Workflow

For a given species of interest

what type of data is available???

retrieval of interaction datasets
Retrieval of interaction datasets
  • Evaluate PPI resources such as Predictome

Prolinks for existence of species of interest

  • If unavailable, find orthologous proteins in

related species that have interactions!

i have interactions what next
I have interactions what next?
  • Evaluate the quality of interactions i.e. type of method used for identification….what exactly are these methods?
i have interactions what next1
I have interactions what next?
  • Evaluate the quality of interactions i.e. type of method used for identification….what exactly are these methods?

STRING Database

slide35

PPI Identification

Computational

Experimental

Phylogenetic profile

Yeast two hybrid

Yeast two hybrid (Y2H)

Gene Cluster

TAP assays

TAP assays

Sequence coevolution

Gene Coexpression

Rosetta stone method

Protein arrays

Text mining

PLoS Computational Biology March 2007, Volume 3 e42

slide36

PPI database comparisons

Proteins: Structure, Function and Bioinformatics 63:490-500 2006

i have interactions what next2
I have interactions what next?
  • Evaluate the quality of interactions i.e. type of method used for identification….what exactly are these methods?
  • Visualize these interactions as a network and analyze…

what are the available tools?