Evidence map›Paper›PMID 26099518›Full record

ArticleBMC genomics2015

MOST+: A de novo motif finding approach combining genomic sequence and heterogeneous genome-wide signatures.

Yizhe Zhang, Yupeng He, Guangyong Zheng, Chaochun Wei

Abstract read
In one paragraph

Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yizhe Zhang
Yupeng He
Guangyong Zheng
Chaochun Wei

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMotifs are regulatory elements that will activate or inhibit the expression of related genes when proteins (such as transcription factors, TFs) bind to them. Therefore, motif finding is important to understand the mechanisms of gene regulation. De novo discovery of regulatory elements, like transcription factor binding sites (TFBSs), has long been a major challenge to gain insight on mechanisms of gene regulation. Recent advances in experimental profiling of genome-wide signals such as histone modifications and DNase I hypersensitivity sites allow scientists to develop better computational methods to enhance motif discovery. However, existing methods for motif finding suffer from high false positive rates and slow speed, and it's difficult to evaluate the performance of these methods systematically.

resultHere we present MOST+, a motif finder integrating genomic sequences and genome-wide signals such as intensity and shape features from histone modification marks and DNase I hypersensitivity sites, to improve the prediction accuracy. MOST+ can detect motifs from a large input sequence of about 100 Mbs within a few minutes. Systematic comparison method has been established and MOST+ has been compared with existing methods.

conclusionMOST+ is a fast and accurate de novo method for motif finding by integrating genomic sequence and experimental signals as clues.

Indexed as

AlgorithmsRegulatory Elements, TranscriptionalAnimalsComputational BiologyDatabases, GeneticEpigenesis, GeneticGene Expression RegulationGenomeHumansMice

Identifiers

PMID26099518
PMCPMC4474412

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.