Brief. Bioinformatics - GO-function: deriving biologically relevant functions from statistically significant functions.

Tópicos

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Resumo

In high-throughput studies of diseases, terms enriched with disease-related genes based on Gene Ontology (GO) are routinely found. However, most current algorithms used to find significant GO terms cannot handle the redundancy that results from the dependencies of GO terms. Simply based on some numerical considerations, current algorithms developed for reducing this redundancy may produce results that do not account for biologically interesting cases. In this article, we present several rules used to design a tool called GO-function for extracting biologically relevant terms from statistically significant GO terms for a disease. Using one gene expression profile for colorectal cancer, we compared GO-function with four algorithms designed to treat redundancy. Then, we validated results obtained in this data set by GO-function using another data set for colorectal cancer. Our analysis showed that GO-function can identify disease-related terms that are more statistically and biologically meaningful than those found by the other four algorithms.

Resumo Limpo

highthroughput studi diseas term enrich diseaserel gene base gene ontolog go routin found howev current algorithm use find signific go term handl redund result depend go term simpli base numer consider current algorithm develop reduc redund may produc result account biolog interest case articl present sever rule use design tool call gofunct extract biolog relev term statist signific go term diseas use one gene express profil colorect cancer compar gofunct four algorithm design treat redund valid result obtain data set gofunct use anoth data set colorect cancer analysi show gofunct can identifi diseaserel term statist biolog meaning found four algorithm

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