title: GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding.
abstract: BACKGROUND
  The majority of disease-associated variants identified in genome-wide association studies reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of genome-wide association studies.
  
  
  RESULTS
  We present GERV (generative evaluation of regulatory variants), a novel computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer-based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence determinants of transcription factor binding than a motif-based approach alone, including both a transcription factor's canonical motif and associated co-factor motifs. We show that GERV outperforms existing methods in predicting single-nucleotide polymorphisms associated with allele-specific binding. GERV correctly predicts a validated causal variant among linked single-nucleotide polymorphisms and prioritizes the variants previously reported to modulate the binding of FOXA1 in breast cancer cell lines. Thus, GERV provides a powerful approach for functionally annotating and prioritizing causal variants for experimental follow-up analysis.
  
  
  METHODS
  The implementation of GERV and related data are available at http://gerv.csail.mit.edu/.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA and.
authors.forename: Haoyang
authors.initials: H
authors.lastname: Zeng
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA and.
authors.forename: Tatsunori
authors.initials: T
authors.lastname: Hashimoto
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA and.
authors.forename: Daniel D
authors.initials: DD
authors.lastname: Kang
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA and Department of Stem Cell and Regenerative Biology, Harvard University and Harvard Medical School, Cambridge, MA 02138, USA.
authors.forename: David K
authors.initials: DK
authors.lastname: Gifford
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology / Q000379:methods*; D015894:Genome, Human; D055106:Genome-Wide Association Study; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D015233:Models, Statistical; D058977:Molecular Sequence Annotation; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D011485:Protein Binding; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors