Finding motifs from all sequences with and without binding sites

Leung, Hoi Yan; Chin, Francis Y. L.

doi:10.1093/bioinformatics/btl371

Cited by 16 publications

(16 citation statements)

References 13 publications

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“…We choose three previously published programs, ALSE [25], Dips [26], and DEME [27], which are available for downloading and running locally, to benchmark against PhyloGibbs-MP. Other published programs, not available for download, include LearnPSSM [28], DME [29], and dPattern [30].…”

Section: Resultsmentioning

confidence: 99%

PhyloGibbs-MP: Module Prediction and Discriminative Motif-Finding by Gibbs Sampling

Siddharthan

2008

PLoS Comput Biol

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PhyloGibbs, our recent Gibbs-sampling motif-finder, takes phylogeny into account in detecting binding sites for transcription factors in DNA and assigns posterior probabilities to its predictions obtained by sampling the entire configuration space. Here, in an extension called PhyloGibbs-MP, we widen the scope of the program, addressing two major problems in computational regulatory genomics. First, PhyloGibbs-MP can localise predictions to small, undetermined regions of a large input sequence, thus effectively predicting cis-regulatory modules (CRMs) ab initio while simultaneously predicting binding sites in those modules—tasks that are usually done by two separate programs. PhyloGibbs-MP's performance at such ab initio CRM prediction is comparable with or superior to dedicated module-prediction software that use prior knowledge of previously characterised transcription factors. Second, PhyloGibbs-MP can predict motifs that differentiate between two (or more) different groups of regulatory regions, that is, motifs that occur preferentially in one group over the others. While other “discriminative motif-finders” have been published in the literature, PhyloGibbs-MP's implementation has some unique features and flexibility. Benchmarks on synthetic and actual genomic data show that this algorithm is successful at enhancing predictions of differentiating sites and suppressing predictions of common sites and compares with or outperforms other discriminative motif-finders on actual genomic data. Additional enhancements include significant performance and speed improvements, the ability to use “informative priors” on known transcription factors, and the ability to output annotations in a format that can be visualised with the Generic Genome Browser. In stand-alone motif-finding, PhyloGibbs-MP remains competitive, outperforming PhyloGibbs-1.0 and other programs on benchmark data.

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Section: Resultsmentioning

confidence: 99%

PhyloGibbs-MP: Module Prediction and Discriminative Motif-Finding by Gibbs Sampling

Siddharthan

2008

PLoS Comput Biol

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“…However, many of these use word-based models [19]–[21], which might not capture the diversity of binding sites. Others again use PWMs, but have binary hit models that do not distinguish between hits as long as they are over a threshold [22]. A discriminatory approach similar to ours has been combined with the use of expression data [18], but depending on the regions that are being investigated this might often not be available or even possible.…”

Section: Introductionmentioning

confidence: 99%

Discovery of Regulatory Elements is Improved by a Discriminatory Approach

et al. 2009

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A major goal in post-genome biology is the complete mapping of the gene regulatory networks for every organism. Identification of regulatory elements is a prerequisite for realizing this ambitious goal. A common problem is finding regulatory patterns in promoters of a group of co-expressed genes, but contemporary methods are challenged by the size and diversity of regulatory regions in higher metazoans. Two key issues are the small amount of information contained in a pattern compared to the large promoter regions and the repetitive characteristics of genomic DNA, which both lead to “pattern drowning”. We present a new computational method for identifying transcription factor binding sites in promoters using a discriminatory approach with a large negative set encompassing a significant sample of the promoters from the relevant genome. The sequences are described by a probabilistic model and the most discriminatory motifs are identified by maximizing the probability of the sets given the motif model and prior probabilities of motif occurrences in both sets. Due to the large number of promoters in the negative set, an enhanced suffix array is used to improve speed and performance. Using our method, we demonstrate higher accuracy than the best of contemporary methods, high robustness when extending the length of the input sequences and a strong correlation between our objective function and the correct solution. Using a large background set of real promoters instead of a simplified model leads to higher discriminatory power and markedly reduces the need for repeat masking; a common pre-processing step for other pattern finders.

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“…In species of agricultural importance, contigs with expression patterns similar to genes, which confer desirable traits, are of particular interest, as they may represent economically important "novel genes". Additionally, the promoter region from contigs with similar expression patterns can be sequenced and common transcription fac-tor binding site motifs detected in order to identify likely promoter regions contributing to the observed co-expression pattern (Leung and Chin, 2006;Zadissa et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Generation of a preliminary bovine gene atlas, using expression clustering to annotate gene function

Maqbool²,

Af³

et al. 2009

Genet. Mol. Res.

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ABSTRACT. Genes whose products function in a common biological process are often co-regulated. When regulation occurs at the transcriptional level, co-expressed genes can be detected globally by expression arrays or by sequencing non-normalized cDNA libraries. We examined bovine gene expression in 27 tissues using non-normalized cDNA library sequencing. Contigs were generated from expressed sequence tags whose sequences overlapped. Contigs containing a minimum of five expressed sequence tags were ordered via a hierarchical clustering process, where the distance between the contigs represents their expression pattern similarity across tissues. Gene ontology terms associated with the genes in each cluster showed that co-clustered genes encoded proteins involved in a common biological process. This process can be used to annotate genes of unknown function in the cluster. Gene expression was compared between bovine and human tissues; there were significant correlations between species for each tissue, with the exception of thyroid and placenta. Tissues were also clustered based on the genes they express; tissues with similar physiological functions clustered closely. Based on this information, we generated the first preliminary gene atlas of the bovine genome. Genes with similar expression patterns were clustered, and genes with a common function coclustered. This method can be used to annotate genes of unknown function in the bovine genome.

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Finding motifs from all sequences with and without binding sites

Abstract: cmleung2@cs.hku.hk.

Cited by 16 publications

References 13 publications

PhyloGibbs-MP: Module Prediction and Discriminative Motif-Finding by Gibbs Sampling

PhyloGibbs-MP: Module Prediction and Discriminative Motif-Finding by Gibbs Sampling

Discovery of Regulatory Elements is Improved by a Discriminatory Approach

Generation of a preliminary bovine gene atlas, using expression clustering to annotate gene function

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