ArticleBMC genomics2015
MOST+: A de novo motif finding approach combining genomic sequence and heterogeneous genome-wide signatures.
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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3 citing papers in PubMed.
- Computational modeling of in vivo and in vitro protein-DNA interactions by multiple instance learning.Bioinformatics (Oxford, England) · 2017Article
- Education, collaboration, and innovation: intelligent biology and medicine in the era of big data.BMC genomics · 2015Article
- Transcription factor motif quality assessment requires systematic comparative analysis.F1000Research · 2015Article
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4 authors.
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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.
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