Finding Motifs with Insufficient Number of Strong Binding Sites

Motif finding in DNA sequences based on skipping nonconserved positions in background Markov chains

One strategy to identify transcription factor binding sites is through motif finding in upstream DNA sequences of potentially co-regulated genes. Despite extensive efforts, none of the existing algorithms perform very well. We consider a string representation that allows arbitrary ignored positions within the nonconserved portion of single motifs, and use O(2(l)) Markov chains to model the background distributions of motifs of length l while skipping these positions within each Markov chain. By focusing initially on positions that have fixed nucleotides to define core occurrences, we develop an algorithm to identify motifs of moderate lengths. We compare the performance of our algorithm to other motif finding algorithms on a few benchmark data sets, and show that significant improvement in accuracy can be obtained when the sites are sufficiently conserved within a given sample, while comparable performance is obtained when the site conservation rate is low. A software program (PosMotif ) and detailed results are available online at http://faculty.cse.tamu.edu/shsze/posmotif.

W-AlignACE: an improved Gibbs sampling algorithm based on more accurate position weight matrices learned from sequence and gene expression/ChIP-chip data

MOTIVATION

Position weight matrices (PWMs) are widely used to depict the DNA binding preferences of transcription factors (TFs) in computational molecular biology and regulatory genomics. Thus, learning an accurate PWM to characterize the binding sites of a specific TF is a fundamental problem that plays an important role in modeling regulatory motifs and also in discovering the regulatory targets of TFs.

RESULTS

We study the question of how to learn a more accurate PWM from both binding sequences and gene expression (or ChIP-chip) data, and propose to find a PWM such that the likelihood of simultaneously observing both binding sequences and their associated gene expression (or ChIP-chip) data is maximised. To solve the above maximum likelihood problem, a sequence weighting scheme is thus introduced based on the observation that binding sites inducing drastic fold changes in mRNA expression (or showing strong binding ratios in ChIP experiments) are likely to represent a true motif. We have incorporated this new learning approach into the popular motif finding program AlignACE. The modified program, called W-AlignACE, is compared with three other programs (AlignACE, MDscan and MotifRegressor) on a variety of datasets, including simulated data, mRNA expression and ChIP-chip data. These tests demonstrate that W-AlignACE is an effective tool for discovering TF binding motifs from gene expression (or ChIP-chip) data and, in particular, has the ability to find very weak motifs like DIG1 and GAL4.

AVAILABILITY

http://www.ntu.edu.sg/home/ChenXin/Gibbs

DNA motif representation with nucleotide dependency

The problem of discovering novel motifs of binding sites is important to the understanding of gene regulatory networks. Motifs are generally represented by matrices (position weight matrix (PWM) or position specific scoring matrix (PSSM) or strings. However, these representations cannot model biological binding sites well because they fail to capture nucleotide interdependence. It has been pointed out by many researchers that the nucleotides of the DNA binding site cannot be treated independently, e.g. the binding sites of zinc finger in proteins. In this paper, a new representation called Scored Position Specific Pattern (SPSP), which is a generalization of the matrix and string representations, is introduced which takes into consideration the dependent occurrences of neighboring nucleotides. Even though the problem of discovering the optimal motif in SPSP representation is proved to be NP-hard, we introduce a heuristic algorithm called SPSP-Finder, which can effectively find optimal motifs in most simulated cases and some real cases for which existing popular motif finding software, such as Weeder, MEME and AlignACE, fail.