MotifVoter: a novel ensemble method for fine-grained integration of generic motif finders

Wijaya, Edward; Yiu, Siu-Ming; Son, Ngo Thanh; Kanagasabai, Rajaraman; Sung, Wing-Kin

doi:10.1093/bioinformatics/btn420

Cited by 39 publications

(42 citation statements)

References 34 publications

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“…They later extended the method in their EMD algorithm (135). Recently, two additional applications (SCOPE and MotifVoter) that combine the results of different motif search algorithms for prokaryote data have become available (44,316). The application MOTIFATOR is focused on prokaryote data analysis and uses the SCOPE algorithm to search for overrepresented DNA motifs in upstream regions of DNA microarray targets (31).…”

Section: In Silico Prediction Of Tfbssmentioning

confidence: 99%

Mechanisms and Evolution of Control Logic in Prokaryotic Transcriptional Regulation

Hijum

Medema

Kuipers

2009

Microbiol Mol Biol Rev

139

149

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SUMMARY A major part of organismal complexity and versatility of prokaryotes resides in their ability to fine-tune gene expression to adequately respond to internal and external stimuli. Evolution has been very innovative in creating intricate mechanisms by which different regulatory signals operate and interact at promoters to drive gene expression. The regulation of target gene expression by transcription factors (TFs) is governed by control logic brought about by the interaction of regulators with TF binding sites (TFBSs) in cis-regulatory regions. A factor that in large part determines the strength of the response of a target to a given TF is motif stringency, the extent to which the TFBS fits the optimal TFBS sequence for a given TF. Advances in high-throughput technologies and computational genomics allow reconstruction of transcriptional regulatory networks in silico. To optimize the prediction of transcriptional regulatory networks, i.e., to separate direct regulation from indirect regulation, a thorough understanding of the control logic underlying the regulation of gene expression is required. This review summarizes the state of the art of the elements that determine the functionality of TFBSs by focusing on the molecular biological mechanisms and evolutionary origins of cis-regulatory regions.

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Section: In Silico Prediction Of Tfbssmentioning

confidence: 99%

Mechanisms and Evolution of Control Logic in Prokaryotic Transcriptional Regulation

Hijum

Medema

Kuipers

2009

Microbiol Mol Biol Rev

139

149

View full text Add to dashboard Cite

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“…In first step, based on a careful inspection of the available literatures and Catalog of Somatic Mutations in Cancer (COSMIC) database, mutation hotspots in 20 cancer genes were selected ( Table 1, Supplementary file 1) [15,16]. We considered the 660 nucleotide around a mutation hotspot by most common motif finder programs including AlignACE, ANN-Spec, BioProspector, Improbizer, MDScan, MEME, MITRA, Motif Sampler, SPACE, Weeder, and Trawler [17]. After analyzing the selected regions of human genome, huge numbers of DNA motifs were collected.…”

Section: Methodsmentioning

confidence: 99%

A Sequence Motif Associated with Intrinsic Mutation Hot-Spots in Human Cancers

Nassiri¹,

Azadian

Masoudi‐Nejad³

2013

J Proteomics Bioinform

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“…We have modified the similarity function described by [44], to compute the similarity score of two binding segments x and y. Let A(x) = {x 1 , x 2 , …, x n } be the set of binding sites clustered at binding segment x.…”

Section: Motif Similaritymentioning

confidence: 99%

DeepFinder: An integration of feature-based and deep learning approach for DNA motif discovery

Lee

Azizan

Wong

et al. 2018

Biotechnology & Biotechnological Equipment

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We propose an improved solution to the three-stage DNA motif prediction approach. The threestage approach uses only a subset of input sequences for initial motif prediction, and the initial motifs obtained are employed for site detection in the remaining input subset of non-overlaps. The currently available solution is not robust because motifs obtained from the initial subset are represented as a position weight matrices, which results in high false positives. Our approach, called DeepFinder, employs deep learning neural networks with features associated with binding sites to construct a motif model. Furthermore, multiple prediction tools are used in the initial motif prediction process to obtain a higher number of positive hits. Our features are engineered from the context of binding sites, which are assumed to be enriched with specificity information of sites recognized by transcription factor proteins. DeepFinder is evaluated using several performance metrics on ten chromatin immunoprecipitation (ChIP) datasets. The results show marked improvement of our solution in comparison with the existing solution. This indicates the effectiveness and potential of our proposed DeepFinder for large-scale motif analysis.

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MotifVoter: a novel ensemble method for fine-grained integration of generic motif finders

Cited by 39 publications

References 34 publications

Mechanisms and Evolution of Control Logic in Prokaryotic Transcriptional Regulation

Mechanisms and Evolution of Control Logic in Prokaryotic Transcriptional Regulation

A Sequence Motif Associated with Intrinsic Mutation Hot-Spots in Human Cancers

DeepFinder: An integration of feature-based and deep learning approach for DNA motif discovery

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