SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps

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 tr...

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Bibliographic Details
Published in:PLoS computational biology 2015-05, Vol.11 (5), p.e1004271-e1004271
Main Authors: Setty, Manu, Leslie, Christina S
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Amino Acid Motifs
Binding Sites - genetics
Cell Line, Tumor
Chromatin
Chromatin - physiology
Chromatin Immunoprecipitation
Chromosome Mapping
Cluster Analysis
Computational Biology
Deoxyribonucleases - chemistry
Deoxyribonucleic acid
Discovery tools
DNA
DNA sequencing
Experiments
Gene expression
Genome
Genome-wide association studies
Genomes
High-Throughput Nucleotide Sequencing
Humans
Methods
Models, Statistical
Physiological aspects
Protein Binding
Regulatory Sequences, Nucleic Acid
Reproducibility of Results
ROC Curve
Transcription factors
Transcription Factors - metabolism
Transcription Factors - physiology
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Summary: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/.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1004271