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Canadian Bioinformatics Workshops

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Title: Canadian Bioinformatics Workshops


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Canadian Bioinformatics Workshops
  • www.bioinformatics.ca

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Module Title of Module
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Module 5Gene Function Prediction
  • Quaid Morris
  • Pathway and Network Analysis of omics Data
  • May 2-3, 2011

http//morrislab.med.utoronto.ca
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Outline
  • Concepts in gene function prediction
  • Guilt-by-association
  • Gene recommender systems
  • Gene function prediction use cases
  • Functional interaction networks
  • Scoring interactions by guilt-by-association
  • GeneMANIA STRING
  • GeneMANIA demo
  • STRING demo

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Using genome-wide data in the lab
CHiP-chip regulation data
Protein-protein interaction data
Genetic interaction data
?!?
Microarray expression data
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Genomics revolution, the bad news
Genomics datasets are
  • noisy,
  • redundant,
  • incomplete,
  • mysterious,
  • massive

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Google cant do biology
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Google cant do biology
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Guilt-by-association principle
Microarray expression data
Co-expression network
Conditions
Genes
Eisen et al (PNAS 1998)
Fraser AG, Marcotte EM - A probabilistic view of
gene function - Nat Genet. 2004 Jun36(6)559-64
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Two types of functional prediction
  • Give me more genes like these,
  • e.g. find more genes in the Wnt signaling
    pathway, find more kinases, find more members of
    a protein complex
  • What does my gene do?
  • Goal determine a genes function based on who it
    interacts with guilt-by-association.

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Give me more genes like these
Input
Network and profile data
Output
from GeneMANIA
Gene recommender system
Query list
CDC48 CPR3 MCA1 TDH2
e.g., GeneMANIA, STRING, bioPIXIE (not updated)
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What does my gene do?, Solution 1
Input
Network and profile data
Output
Gene recommender system then enrichment analysis
Query list
CDC48
e.g., GeneMANIA, bioPIXIE
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What does my gene do?, Solution 2
CDC48
Input
Network and profile data
Supervised learning of a classifier
Classifier
from FuncBase
(e.g. Support Vector Machine, Naïve Bayes, Neural
networks, Random Forests)
Gene annotations, e.g. Gene Ontology
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Comparing solutions
  • Supervised learning
  • Needs gene sets for training, typically training
    is time-consuming and is done off-line but
    classifier is very fast
  • So, fast but inflexible
  • Gene recommender systems
  • Typically most computation is done online (except
    for offline calculation of composite functional
    interaction network, see next slide), so
    updating is easier and can use arbitrary gene
    sets
  • So, a little slower but much more flexible

Note can solve give me more genes like these
with supervised learning as well, so long as gene
set is predefined
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Composite functional interaction/linkage/associati
on networks
CHiP-chip regulation data
Protein-protein interaction data
Genetic interaction data
Composite functional association network
Microarray expression data
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Pre-computed functional interaction networks
Pre-combine networks e.g. by simple addition or
Naïve Bayes
Pavlidis et al, 2002, Marcotte et al,
1999 bioPIXIE
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Composite networks One size doesnt fit all
  • Gene function could be a/the
  • Biological process,
  • Biochemical/molecular function,
  • Subcellular/Cellular localization,
  • Regulatory targets,
  • Temporal expression pattern,
  • Phenotypic effect of deletion.

Some networks may be better for some types of
gene function than others
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Solution Query-specific weights
Pavlidis et al, 2002, Lanckriet et al,
2004 Mostafavi et al, 2008
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Two rules for network weighting
  • Relevance
  • The network should be relevant to predicting the
    function of interest
  • Test Are the genes in the query list more often
    connected to one another than to other genes?
  • Redundancy
  • The network should not be redundant with other
    datasets particularly a problem for
    co-expression
  • Test Do the two networks share many interactions
  • Caveat Shared interactions also provide more
    confidence that the interaction is real.

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Scoring nodes by guilt-by-association
Query list positive examples
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Scoring nodes by guilt-by-association
Query list positive examples
Score
high
low
Two main algorithms
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Node scoring algorithm details
  • Direct neighbour node score depends on
  • Strength of links to positive examples
  • of positive neighbors
  • Label propagation node score depends on
  • Strength of links and of positive direct
    neighbors
  • of shared neighbors with positive examples
  • modular structure of network

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Label propagation example
Before
After
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Three parts of GeneMANIA
  • A large, automatically updated collection of
    interactions networks.
  • A query algorithm to find genes and networks that
    are functionally associated to your query gene
    list.
  • An interactive, client-side network browser with
    extensive link-outs

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GeneMANIA data sources
Legend
Network types
minor curation
major curation
Co-expression
  • Gene ID mappings from Ensembl and Ensembl Plant
  • Network/gene descriptors from Entrez-Gene and
    Pubmed
  • Gene annotations from Gene Ontology, GOA, and
    model org. databases

Co-localization
Pathways
Physical interactions
Genetic interactions
Shared domains
Predicted interactions
Other
MGI Chemogenomics
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Gene identifiers
  • All unique identifiers within the selected
    organism e.g.
  • Entrez-Gene ID
  • Gene symbol
  • Ensembl ID
  • Uniprot (primary)
  • also, some synonyms organism-specific names
  • We use Ensembl database for gene mappings (but we
    mirror it once / 3 months, so sometimes we are
    out of date)

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Current status
  • Six organisms
  • Human, Mouse, yeast, worm, fly, A Thaliana, Rat
    coming soon
  • 1,250 networks (about 50 co-expression, 35
    physical interaction)
  • Web network browser

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Cytoscape plugin
http//www.genemania.org/plugin/
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QueryRunner
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http//cytoscapeweb.cytoscape.org/
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STRING http//string-db.org/
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STRING results
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STRING results
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GeneMANIA vs STRING
  • STRING (2003-present)
  • Large organism converge
  • Protein focused
  • Uses eight pre-computed networks
  • Heavy use of phylogeny to infer functional
    interactions, also contains text mining derived
    interactions
  • Uses direct interaction to score nodes
  • Link weights are Prob of functional interaction
  • GeneMANIA webserver (2010-present)
  • Covers 6 (not 7) major model organisms (but can
    add more with plugin)
  • Gene focused
  • Thousands of networks, weights are not
    pre-computed, can upload your own network
  • Relies heavily on functional genomic data so has
    genetic interactions, phenotypic info, chemical
    interactions
  • Allows enrichment analysis
  • Uses label propagation to score nodes

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Meaning of GeneMANIA link weights
Simple intuition Sum of link weights to
neighbors in each data source is 100
Weight 50
Weight 25
Precise definition Weight 100 x 1/sqrt( of
neighbours of node 1) x 1/sqrt( of neighbours of
node 2)
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GeneMANIA future directions
  • Rat (1-3 weeks), next is probably E. Coli
  • Non-coding genes (miRNAs!!!!)
  • Regulatory networks (ChIP, RNA-protein,
    miRNA-mRNAs)
  • More phenotypic information (OMIM, etc)
  • Orthology mapping for inferring interologs

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GeneMANIA URLs
  • Main site (stable but still fun)
  • http//www.genemania.org
  • Beta site (new and edgy but possibly unreliable)
  • http//beta.genemania.org

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  • We are on a Coffee Break Networking Session
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