Enhanced regulatory sequence prediction using gapped k-mer features

A method to predict the impact of regulatory variants from DNA sequence

Most variants implicated in common human disease by genome-wide association studies (GWAS) lie in noncoding sequence intervals. Despite the suggestion that regulatory element disruption represents a common theme, identifying causal risk variants within implicated genomic regions remains a major challenge. Here we present a new sequence-based computational method to predict the effect of regulatory variation, using a classifier (gkm-SVM) that encodes cell type-specific regulatory sequence vocabularies. The induced change in the gkm-SVM score, deltaSVM, quantifies the effect of variants. We show that deltaSVM accurately predicts the impact of SNPs on DNase I sensitivity in their native genomic contexts and accurately predicts the results of dense mutagenesis of several enhancers in reporter assays. Previously validated GWAS SNPs yield large deltaSVM scores, and we predict new risk-conferring SNPs for several autoimmune diseases. Thus, deltaSVM provides a powerful computational approach to systematically identify functional regulatory variants.

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 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/.

Transcriptional regulation is the cell’s primary mode of controlling gene expression. 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. New methods are therefore needed to detect and represent DNA sequence signals in these genome-wide regulatory element maps. Here we present a novel tool called SeqGL to extract multiple TF binding signals from genome-wide maps. SeqGL employs a machine learning framework to identify features that best discriminate the peaks, where we expect DNA sequence signals to occur, from the flank regions that should not contain these signals. Our tool performed significantly better than widely used motif discovery methods in discriminative accuracy and achieved higher sensitivity in detecting the numerous sequence signals underlying regulatory element maps.

Estimating evolutionary distances between genomic sequences from spaced-word matches

Alignment-free methods are increasingly used to calculate evolutionary distances between DNA and protein sequences as a basis of phylogeny reconstruction. Most of these methods, however, use heuristic distance functions that are not based on any explicit model of molecular evolution. Herein, we propose a simple estimator d N of the evolutionary distance between two DNA sequences that is calculated from the number N of (spaced) word matches between them. We show that this distance function is more accurate than other distance measures that are used by alignment-free methods. In addition, we calculate the variance of the normalized number N of (spaced) word matches. We show that the variance of N is smaller for spaced words than for contiguous words, and that the variance is further reduced if our spaced-words approach is used with multiple patterns of 'match positions' and 'don't care positions'. Our software is available online and as downloadable source code at: http://spaced.gobics.de/.