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Open Access Highly Accessed Methodology article

Principal component analysis for predicting transcription-factor binding motifs from array-derived data

Yunlong Liu12, Matthew P Vincenti45 and Hiroki Yokota123*

Author Affiliations

1 Department of Biomedical Engineering, Indiana University – Purdue University Indianapolis, Indianapolis, IN 46202, USA.

2 Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.

3 Department of Anatomy and Cell Biology, Indiana University – Purdue University Indianapolis, Indianapolis, IN 46202, USA.

4 Department of Veteran's Affairs, White River Jct, VT 05009, USA.

5 Department of Medicine, Dartmouth Medical School, Hanover, NH 03755, USA.

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BMC Bioinformatics 2005, 6:276  doi:10.1186/1471-2105-6-276

Published: 18 November 2005

Abstract

Background

The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs). In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD) is a powerful method to derive primary components of a given matrix. Applying SVD to a promoter matrix defined from regulatory DNA sequences, we derived a novel method to predict the critical set of TFBMs.

Results

The promoter matrix was defined to establish a quantitative relationship between the IL-1-driven mRNA alteration and genomic DNA sequences of the IL-1 responsive genes. The matrix was decomposed with SVD, and the effects of 8 potential TFBMs (5'-CAGGC-3', 5'-CGCCC-3', 5'-CCGCC-3', 5'-ATGGG-3', 5'-GGGAA-3', 5'-CGTCC-3', 5'-AAAGG-3', and 5'-ACCCA-3') were predicted from a pool of 512 random DNA sequences. The prediction included matches to the core binding motifs of biologically known TFBMs such as AP2, SP1, EGR1, KROX, GC-BOX, ABI4, ETF, E2F, SRF, STAT, IK-1, PPARγ, STAF, ROAZ, and NFκB, and their significance was evaluated numerically using Monte Carlo simulation and genetic algorithm.

Conclusion

The described SVD-based prediction is an analytical method to provide a set of potential TFBMs involved in transcriptional regulation. The results would be useful to evaluate analytically a contribution of individual DNA sequences.