MotEvo: Integrated bayesian probabilistic methods for inferring regulatory sites and motifs on multiple alignments of DNA sequences

63Citations
Citations of this article
150Readers
Mendeley users who have this article in their library.

Abstract

Motivation: Probabilistic approaches for inferring transcription factor binding sites (TFBSs) and regulatory motifs from DNA sequences have been developed for over two decades. Previous work has shown that prediction accuracy can be significantly improved by incorporating features such as the competition of multiple transcription factors (TFs) for binding to nearby sites, the tendency of TFBSs for co-regulated TFs to cluster and form cis-regulatory modules and explicit evolutionary modeling of conservation of TFBSs across orthologous sequences. However, currently available tools only incorporate some of these features, and significant methodological hurdles hampered their synthesis into a single consistent probabilistic framework. Results: We present MotEvo, a integrated suite of Bayesian probabilistic methods for the prediction of TFBSs and inference of regulatory motifs from multiple alignments of phylogenetically related DNA sequences, which incorporates all features just mentioned. In addition, MotEvo incorporates a novel model for detecting unknown functional elements that are under evolutionary constraint, and a new robust model for treating gain and loss of TFBSs along a phylogeny. Rigorous benchmarking tests on ChIP-seq datasets show that MotEvo's novel features significantly improve the accuracy of TFBS prediction, motif inference and enhancer prediction. © The Author 2011. Published by Oxford University Press. All rights reserved.

References Powered by Scopus

Evolutionary trees from DNA sequences: A maximum likelihood approach

12234Citations
N/AReaders
Get full text

Model-based analysis of ChIP-Seq (MACS)

11622Citations
N/AReaders
Get full text

T-coffee: A novel method for fast and accurate multiple sequence alignment

5844Citations
N/AReaders
Get full text

Cited by Powered by Scopus

An integrated expression atlas of miRNAs and their promoters in human and mouse

424Citations
N/AReaders
Get full text

Opossum-3: Advanced analysis of regulatory motif over-representation across genes or chip-seq datasets

242Citations
N/AReaders
Get full text

ISMARA: automated modeling of genomic signals as a democracy of regulatory motifs

224Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Arnold, P., Erb, I., Pachkov, M., Molina, N., & Van Nimwegen, E. (2012). MotEvo: Integrated bayesian probabilistic methods for inferring regulatory sites and motifs on multiple alignments of DNA sequences. Bioinformatics, 28(4), 487–494. https://doi.org/10.1093/bioinformatics/btr695

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 74

60%

Researcher 36

29%

Professor / Associate Prof. 12

10%

Lecturer / Post doc 1

1%

Readers' Discipline

Tooltip

Agricultural and Biological Sciences 61

53%

Biochemistry, Genetics and Molecular Bi... 28

24%

Computer Science 21

18%

Medicine and Dentistry 6

5%

Save time finding and organizing research with Mendeley

Sign up for free