BayesMD: flexible biological modeling for motif discovery

PairMotif+: a fast and effective algorithm for de novo motif discovery in DNA sequences

The planted (l, d) motif search is one of the most widely studied problems in bioinformatics, which plays an important role in the identification of transcription factor binding sites in DNA sequences. However, it is still a challenging task to identify highly degenerate motifs, since current algorithms either output the exact results with a high computational cost or accomplish the computation in a short time but very often fall into a local optimum. In order to make a better trade-off between accuracy and efficiency, we propose a new pattern-driven algorithm, named PairMotif+. At first, some pairs of l-mers are extracted from input sequences according to probabilistic analysis and statistical method so that one or more pairs of motif instances are included in them. Then an approximate strategy for refining pairs of l-mers with high accuracy is adopted in order to avoid the verification of most candidate motifs. Experimental results on the simulated data show that PairMotif+ can solve various (l, d) problems within an hour on a PC with 2.67 GHz processor, and has a better identification accuracy than the compared algorithms MEME, AlignACE and VINE. Also, the validity of the proposed algorithm is tested on multiple real data sets.

Motif discovery and transcription factor binding sites before and after the next-generation sequencing era

Motif discovery has been one of the most widely studied problems in bioinformatics ever since genomic and protein sequences have been available. In particular, its application to the de novo prediction of putative over-represented transcription factor binding sites in nucleotide sequences has been, and still is, one of the most challenging flavors of the problem. Recently, novel experimental techniques like chromatin immunoprecipitation (ChIP) have been introduced, permitting the genome-wide identification of protein-DNA interactions. ChIP, applied to transcription factors and coupled with genome tiling arrays (ChIP on Chip) or next-generation sequencing technologies (ChIP-Seq) has opened new avenues in research, as well as posed new challenges to bioinformaticians developing algorithms and methods for motif discovery.

coMOTIF: a mixture framework for identifying transcription factor and a coregulator motif in ChIP-seq data

MOTIVATION

ChIP-seq data are enriched in binding sites for the protein immunoprecipitated. Some sequences may also contain binding sites for a coregulator. Biologists are interested in knowing which coregulatory factor motifs may be present in the sequences bound by the protein ChIP'ed.

RESULTS

We present a finite mixture framework with an expectation-maximization algorithm that considers two motifs jointly and simultaneously determines which sequences contain both motifs, either one or neither of them. Tested on 10 simulated ChIP-seq datasets, our method performed better than repeated application of MEME in predicting sequences containing both motifs. When applied to a mouse liver Foxa2 ChIP-seq dataset involving ~ 12 000 400-bp sequences, coMOTIF identified co-occurrence of Foxa2 with Hnf4a, Cebpa, E-box, Ap1/Maf or Sp1 motifs in ~6-33% of these sequences. These motifs are either known as liver-specific transcription factors or have an important role in liver function.

AVAILABILITY

Freely available at http://www.niehs.nih.gov/research/resources/software/comotif/.

CONTACT

li3@niehs.nih.gov

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metamotifs--a generative model for building families of nucleotide position weight matrices

Background

Development of high-throughput methods for measuring DNA interactions of transcription factors together with computational advances in short motif inference algorithms is expanding our understanding of transcription factor binding site motifs. The consequential growth of sequence motif data sets makes it important to systematically group and categorise regulatory motifs. It has been shown that there are familial tendencies in DNA sequence motifs that are predictive of the family of factors that binds them. Further development of methods that detect and describe familial motif trends has the potential to help in measuring the similarity of novel computational motif predictions to previously known data and sensitively detecting regulatory motifs similar to previously known ones from novel sequence.

Results

We propose a probabilistic model for position weight matrix (PWM) sequence motif families. The model, which we call the 'metamotif' describes recurring familial patterns in a set of motifs. The metamotif framework models variation within a family of sequence motifs. It allows for simultaneous estimation of a series of independent metamotifs from input position weight matrix (PWM) motif data and does not assume that all input motif columns contribute to a familial pattern. We describe an algorithm for inferring metamotifs from weight matrix data. We then demonstrate the use of the model in two practical tasks: in the Bayesian NestedMICA model inference algorithm as a PWM prior to enhance motif inference sensitivity, and in a motif classification task where motifs are labelled according to their interacting DNA binding domain.

Conclusions

We show that metamotifs can be used as PWM priors in the NestedMICA motif inference algorithm to dramatically increase the sensitivity to infer motifs. Metamotifs were also successfully applied to a motif classification problem where sequence motif features were used to predict the family of protein DNA binding domains that would interact with it. The metamotif based classifier is shown to compare favourably to previous related methods. The metamotif has great potential for further use in machine learning tasks related to especially de novo computational sequence motif inference. The metamotif methods presented have been incorporated into the NestedMICA suite.

DISPARE: DIScriminative PAttern REfinement for Position Weight Matrices

Background

The accurate determination of transcription factor binding affinities is an important problem in biology and key to understanding the gene regulation process. Position weight matrices are commonly used to represent the binding properties of transcription factor binding sites but suffer from low information content and a large number of false matches in the genome. We describe a novel algorithm for the refinement of position weight matrices representing transcription factor binding sites based on experimental data, including ChIP-chip analyses. We present an iterative weight matrix optimization method that is more accurate in distinguishing true transcription factor binding sites from a negative control set. The initial position weight matrix comes from JASPAR, TRANSFAC or other sources. The main new features are the discriminative nature of the method and matrix width and length optimization.

Results

The algorithm was applied to the increasing collection of known transcription factor binding sites obtained from ChIP-chip experiments. The results show that our algorithm significantly improves the sensitivity and specificity of matrix models for identifying transcription factor binding sites.

Conclusion

When the transcription factor is known, it is more appropriate to use a discriminative approach such as the one presented here to derive its transcription factor-DNA binding properties starting with a matrix, as opposed to performing de novo motif discovery. Generating more accurate position weight matrices will ultimately contribute to a better understanding of eukaryotic transcriptional regulation, and could potentially offer a better alternative to ab initio motif discovery.