title: Discovering Gene Regulatory Elements Using Coverage-Based Heuristics.
abstract: Data mining algorithms and sequencing methods (such as RNA-seq and ChIP-seq) are being combined to discover genomic regulatory motifs that relate to a variety of phenotypes. However, motif discovery algorithms often produce very long lists of putative transcription factor binding sites, hindering the discovery of phenotype-related regulatory elements by making it difficult to select a manageable set of candidate motifs for experimental validation. To address this issue, the authors introduce the motif selection problem and provide coverage-based search heuristics for its solution. Analysis of 203 ChIP-seq experiments from the ENCyclopedia of DNA Elements project shows that our algorithms produce motifs that have high sensitivity and specificity and reveals new insights about the regulatory code of the human genome. The greedy algorithm performs the best, selecting a median of two motifs per ChIP-seq transcription factor group while achieving a median sensitivity of 77 percent.
authors.forename: Rami
authors.initials: R
authors.lastname: Al-Ouran
authors.forename: Robert
authors.initials: R
authors.lastname: Schmidt
authors.forename: Ashwini
authors.initials: A
authors.lastname: Naik
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Jones
authors.forename: Frank
authors.initials: F
authors.lastname: Drews
authors.forename: David
authors.initials: D
authors.lastname: Juedes
authors.forename: Laura
authors.initials: L
authors.lastname: Elnitski
authors.forename: Lonnie
authors.initials: L
authors.lastname: Welch
mesh_terms: D000465:Algorithms; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology / Q000379:methods*; D000067508:Computer Heuristics; D004194:Disease / Q000235:genetics; D006801:Humans; D059372:Nucleotide Motifs / Q000235:genetics; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D017422:Sequence Analysis, DNA