Refining motifs by improving information content scores using neighborhood profile search

Abstract

The main goal of the motif finding problem is to detect novel, over-represented unknown signals in a set of sequences (e.g. transcription factor binding sites in a genome). The most widely used algorithms for finding motifs obtain a generative probabilistic representation of these overrepresented signals and try to discover profiles that maximize the information content score. Although these profiles form a very powerful representation of the signals, the major difficulty arises from the fact that the best motif corresponds to the global maximum of a non-convex continuous function. Popular algorithms like Expectation Maximization (EM) and Gibbs sampling tend to be very sensitive to the initial guesses and are known to converge to the nearest local maximum very quickly. In order to improve the quality of the results, EM is used with multiple random starts or any other powerful stochastic global methods that might yield promising initial guesses (like projection algorithms). Global methods do not necessarily give initial guesses in the convergence region of the best local maximum but rather suggest that a promising solution is in the neighborhood region. In this paper, we introduce a novel optimization framework that searches the neighborhood regions of the initial alignment in a systematic manner to explore the multiple local optimal solutions. This effective search is achieved by transforming the original optimization problem into its corresponding dynamical system and estimating the practical stability boundary of the local maximum. Our results show that the popularly used EM algorithm often converges to suboptimal solutions which can be significantly improved by the proposed neighborhood profile search. Based on experiments using both synthetic and real datasets, our method demonstrates significant improvements in the information content scores of the probabilistic models. The proposed method also gives the flexibility in using different local solvers and global methods depending on their suitability for some specific datasets.

An ensemble learning approach to reverse-engineering transcriptional regulatory networks from time-series gene expression data

“…A major challenge in computational biology is to reveal the cis -regulatory logics of gene expression through analysis of high-throughput genomic data, for example, genomic sequences and gene expression data. A common practice is to first identify putatively co-regulated genes by clustering gene expression patterns [ 1 - 3 ], and then search for common motifs from the promoter sequences of the genes in the same cluster [ 4 - 7 ]. Such enriched motifs, if identified, are often believed to be the binding motifs of a common transcription factor (TF).…”

Data Mining in Bioinformatics (BIOKDD)

“…In the paper Refining Motifs by Improving Information Content Scores using Neighborhood Profile Search , Chandan K. Reddy, Yao-Chung Weng and Hsiao-Dong Chiang [4], show how one can refine the profile motifs discovered via Expectation Maximization and Gibbs Sampling based methods. They search the neighborhood regions of the initial alignments to obtain locally optimal solutions, which improve the information content of the discovered profiles.…”