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

By Manu N. Setty, Christina S. Leslie. 2015

70 citations recorded by the source corpus.

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/.

Subfields: Molecular Biology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “Later, support vector machines (SVMs) outperformed the generative methods by using k-mer features [162,163], but string kernel-based SVM systems are limited by their expensive computational cost, which is proportional to the number of training and testing sequences.”
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks (2015, background): “This upgrades previous approaches in two ways.”
Genome-wide prediction of cis-regulatory regions using supervised deep learning methods (2016, background): “performance of DECRES Recent studies confirmed that DNA sequence properties can be useful for the recognition of promoters and enhancers [3, 5, 25], and the discrimination between active and inactive regulatory sequences [36, 37] using string sequence kernels.”
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks (2016, methodology): “Later, SVM techniques outperformed the generative methods by using k-mer features [6, 20], but string kernel based SVM systems are limited by expensive computational cost proportional to the number of training and testing sequences.”