title: SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps.
abstract: Genome-wide maps of transcription factor (TF) occupancy and regions of open chromatin implicitly contain DNA sequence signals for multiple factors. We present SeqGL, a novel de novo motif discovery algorithm to identify multiple TF sequence signals from ChIP-, DNase-, and ATAC-seq profiles. SeqGL trains a discriminative model using a k-mer feature representation together with group lasso regularization to extract a collection of sequence signals that distinguish peak sequences from flanking regions. Benchmarked on over 100 ChIP-seq experiments, SeqGL outperformed traditional motif discovery tools in discriminative accuracy. Furthermore, SeqGL can be naturally used with multitask learning to identify genomic and cell-type context determinants of TF binding. SeqGL successfully scales to the large multiplicity of sequence signals in DNase- or ATAC-seq maps. In particular, SeqGL was able to identify a number of ChIP-seq validated sequence signals that were not found by traditional motif discovery algorithms. Thus compared to widely used motif discovery algorithms, SeqGL demonstrates both greater discriminative accuracy and higher sensitivity for detecting the DNA sequence signals underlying regulatory element maps. SeqGL is available at http://cbio.mskcc.org/public/Leslie/SeqGL/.
authors.affiliation: Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Manu
authors.initials: M
authors.lastname: Setty
authors.affiliation: Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Christina S
authors.initials: CS
authors.lastname: Leslie
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D001665:Binding Sites / Q000235:genetics; D045744:Cell Line, Tumor; D002843:Chromatin / Q000502:physiology*; D047369:Chromatin Immunoprecipitation; D002874:Chromosome Mapping; D016000:Cluster Analysis; D019295:Computational Biology; D003851:Deoxyribonucleases / Q000737:chemistry; D016678:Genome; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D015233:Models, Statistical; D011485:Protein Binding; D012372:ROC Curve; D012045:Regulatory Sequences, Nucleic Acid; D015203:Reproducibility of Results; D014157:Transcription Factors / Q000378:metabolism / Q000502:physiology*
chemical_list: D002843:Chromatin; D014157:Transcription Factors; D003851:Deoxyribonucleases