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