Computational localization of transcription factor binding sites using extreme learning machines

Wang, Dianhui; Thanh, Hai

doi:10.1007/s00500-012-0820-x

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Random forests have been used to predict p300 binding sites from histone modifications in human embryonic stem cells and lung fibroblasts [56] . Machine-learning algorithms have also been applied to the related problem of selecting functional TF binding sites out of the thousands of hits to a TF's binding motif throughout the genome [57] , [58] , [59] , [60] , [61] , [62] , [63] . Finally, two groups have taken a less supervised approach and used hidden Markov models (ChromHMM) [64] and dynamic Bayesian networks (Segway) [65] to segment the human genome into regions with unique signatures in ENCODE data and then assigned potential functions, such as enhancer activity, to these states.…”

Section: Introductionmentioning

confidence: 99%

Integrating Diverse Datasets Improves Developmental Enhancer Prediction

et al. 2014

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Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology.

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