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Open Access Highly Accessed Methodology article

TF-finder: A software package for identifying transcription factors involved in biological processes using microarray data and existing knowledge base

Xiaoqi Cui1, Tong Wang2, Huann-Sheng Chen13, Victor Busov23 and Hairong Wei23*

Author Affiliations

1 Department of Mathematical Science, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA

2 School of Forest Resources and Environmental Science, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA

3 Biotechnology Research Center, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA

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BMC Bioinformatics 2010, 11:425  doi:10.1186/1471-2105-11-425

Published: 12 August 2010

Abstract

Background

Identification of transcription factors (TFs) involved in a biological process is the first step towards a better understanding of the underlying regulatory mechanisms. However, due to the involvement of a large number of genes and complicated interactions in a gene regulatory network (GRN), identification of the TFs involved in a biology process remains to be very challenging. In reality, the recognition of TFs for a given a biological process can be further complicated by the fact that most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation except for small conserved domains. This poses a significant challenge for identification of the exact TFs involved or ranking the importance of a set of TFs to a process of interest. Therefore, new methods for recognizing novel TFs are desperately needed. Although a plethora of methods have been developed to infer regulatory genes using microarray data, it is still rare to find the methods that use existing knowledge base in particular the validated genes known to be involved in a process to bait/guide discovery of novel TFs. Such methods can replace the sometimes-arbitrary process of selection of candidate genes for experimental validation and significantly advance our knowledge and understanding of the regulation of a process.

Results

We developed an automated software package called TF-finder for recognizing TFs involved in a biological process using microarray data and existing knowledge base. TF-finder contains two components, adaptive sparse canonical correlation analysis (ASCCA) and enrichment test, for TF recognition. ASCCA uses positive target genes to bait TFS from gene expression data while enrichment test examines the presence of positive TFs in the outcomes from ASCCA. Using microarray data from salt and water stress experiments, we showed TF-finder is very efficient in recognizing many important TFs involved in salt and drought tolerance as evidenced by the rediscovery of those TFs that have been experimentally validated. The efficiency of TF-finder in recognizing novel TFs was further confirmed by a thorough comparison with a method called Intersection of Coexpression (ICE).

Conclusions

TF-finder can be successfully used to infer novel TFs involved a biological process of interest using publicly available gene expression data and known positive genes from existing knowledge bases. The package for TF-finder includes an R script for ASCCA, a Perl controller, and several Perl scripts for parsing intermediate outputs. The package is available upon request (hairong@mtu.edu). The R code for standalone ASCCA is also available.