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

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 IL1-driven mRNA alteration and genom...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Yunlong Liu, Matthew P. Vincenti, Hiroki Yokota
• Keywords: BMCBI 2005, Critical Set, Dna Sequences, Genomic Dna