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/.
Highlights
Transcription factor (TF) ChIP-seq profiles and genome-wide regulatory element maps based on DNase I hypersensitive site sequencing (DNase-seq) or transposase-accessible chromatin sequencing (ATAC-seq) implicitly contain rich information about the cell-type specific and genomiccontext dependent binding of multiple factors
Transcription factors (TFs) are proteins that recognize and bind specific DNA sequence signals to regulate the expression of target genes
Recent years have seen the rapid development of genome-wide assays to profile the binding locations of a single TF or, more generally, regions of open chromatin that are occupied by a complex repertoire of DNA binding factors
Summary
Transcription factor (TF) ChIP-seq profiles and genome-wide regulatory element maps based on DNase I hypersensitive site sequencing (DNase-seq) or transposase-accessible chromatin sequencing (ATAC-seq) implicitly contain rich information about the cell-type specific and genomiccontext dependent binding of multiple factors. Several methods use DNase-seq profiles to scan for instances of known motifs [10, 11], and one recently proposed approach exploits the read-level properties of high-depth digital genomic footprinting (DGF) to improve localization of known motifs [12]. These methods do not enable de novo discovery of binding signals that are not represented in TF motif databases, and methods that rely on the depth and read-level properties of DNase I cleavage in DGF may not readily generalize to newer assays like ATAC-seq, which can be used in low cell number settings where DNase-seq is not feasible. We show how SeqGL can be trained in a multi-task setting, where we jointly train on experiments from multiple cell types in order to identify shared and cell-type specific binding signals or encode information about genomic context, such as gene proximity or chromatin state, into the task structure to reveal more detailed regulatory sequence information
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