Annotating Gene List From Literature

1
Annotating Gene List From Literature

• Xin He
• Department of Computer Science
• UIUC

2
Motivation

• Biologists often need to understand the
  commonalities of a list of genes (e.g. whether
  they are involved in the same pathway).
• These genes typically come from clustering
  results in microarray expression
• Given a list of gene names, is there any
  automatic way to find the common themes from
  literature articles?

3
Related Work

• The most popular way is based on the analysis of
  GO terms associated with genes.
• Method each gene is associated with a set of GO
  terms. Find the GO terms that are overrepresented
  in the input list
• Hypergeometric test p-value of a GO term

N total number of genes M total number of genes
annotated with this term n number of genes in
the list k number of genes in the list annotated
with this term
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Problems with GO-based Approach

• GO cannot cover all the important concepts in the
  literature. E.g. GO has relatively low coverage
  for behavior terms (compared with specialized
  behavior ontology)
• The associations of genes and concepts change
  very rapidly. E.g. new functions of known genes
  are constantly found..

5
Text-based Gene List Annotation

• Hypothesis testing approach
• find terms that are overrepresented for each
  gene Poisson distribution
• find common terms across the gene list
  hypergeometric distribution
• Comparative text mining approach find the common
  themes in multiple collections (one for each gene)

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Comparative Text Mining

• For each gene, find a collection of articles that
  discuss this gene
• Each article in a collection is a mixture of two
  distributions a theme common to all collections
  and a collection-specific theme
• Parameter estimation in the mixture model the
  standard EM algorithm

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Results Pelle System

• Pelle system in Drosophila Saptzle, Toll, Pelle,
  Tube, Cacus, Dorsal
• Among the top-50 words signaling, pathway,
  receptor, embryo, ventral, dorsoventral,
  patterning, embryonic

8
Results MET cluster

• MET cluster from yeast cell-cycle data MET28,
  MET14, MET16, MET10, MET2, MUP1
• Among the top-50 words amino, met25, sulphite

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Problems and Plan

• Many common words (such as stop words) in the
  top-list, not properly normalized
• Use the entire Medline corpus as background not
  working
• Hypothesis testing approach as alternative
• Single words not very suggestive
• Phrase extraction as the postprocessing step