title: FunSeq2: a framework for prioritizing noncoding  regulatory variants in cancer.
abstract: Identification of noncoding drivers from thousands of somatic alterations in a typical tumor is a difficult and unsolved problem. We report a computational framework, FunSeq2, to annotate and prioritize these mutations. The framework combines an adjustable data context integrating large-scale genomics and cancer resources with a streamlined variant-prioritization pipeline. The pipeline has a weighted scoring system combining: inter- and intra-species conservation;loss- and gain-of-function events for transcription-factor binding; enhancer-gene linkages and network centrality; and per-element recurrence across samples. We further highlight putative drivers with information specific to a particular sample, such as differential expression. FunSeq2 is available from funseq2.gersteinlab.org.
authors.affiliation: Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
authors.forename: Yao
authors.initials: Y
authors.lastname: Fu
authors.forename: Zhu
authors.initials: Z
authors.lastname: Liu
authors.forename: Shaoke
authors.initials: S
authors.lastname: Lou
authors.forename: Jason
authors.initials: J
authors.lastname: Bedford
authors.forename: Xinmeng Jasmine
authors.initials: XJ
authors.lastname: Mu
authors.forename: Kevin Y
authors.initials: KY
authors.lastname: Yip
authors.forename: Ekta
authors.initials: E
authors.lastname: Khurana
authors.forename: Mark
authors.initials: M
authors.lastname: Gerstein
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D004252:DNA Mutational Analysis / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D023281:Genomics / Q000379:methods; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D012984:Software