Functional annotation and identification of candidate disease genes by computational analysis of normal tissue gene expression data.

ABSTRACT:

Background

High-throughput gene expression data can predict gene function through the "guilt by association" principle: coexpressed genes are likely to be functionally associated.

Methodology/principal findings

We analyzed publicly available expression data on normal human tissues. The analysis is based on the integration of data obtained with two experimental platforms (microarrays and SAGE) and of various measures of dissimilarity between expression profiles. The building blocks of the procedure are the Ranked Coexpression Groups (RCG), small sets of tightly coexpressed genes which are analyzed in terms of functional annotation. Functionally characterized RCGs are selected by means of the majority rule and used to predict new functional annotations. Functionally characteri

SUBMITTER: Miozzi L 

PROVIDER: S-EPMC2409962 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature