Article

A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction

2017-04-26

Abstract excerpt

<h4>ABSTRACT</h4> The representation and discovery of transcription factor (TF) sequence binding specificities is critical for understanding gene regulatory networks and interpreting the impact of disease-associated non-coding genetic variants. We present a novel TF binding motif representation, the K-mer Set Memory (KSM), which consists of a set of aligned k-mers that are over-represented at TF binding sites, an...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 09a6b306-1478-5718-b314-7feaab9facc6
DOI: 10.1101/130815

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A novel k-mer set memory (KSM) motif representation improves regulatory variant predictionDOI 10.1101/130815Read

Factorbook: an Updated Catalog of Transcription Factor Motifs and Candidate Regulatory Motif SitesSemantic proximity Read

Transcription factor-binding k-mer analysis clarifies the cell type dependency of binding specificities and cis-regulatory SNPs in humans.Semantic proximity Read

MOCCS profile analysis clarifies the cell type dependency of transcription factor-binding sequences and cis-regulatory SNPs in humansSemantic proximity Read

Classic machine learning on top of multiple position weight matrices improves genomic prediction of transcription factor binding sitesSemantic proximity Read

Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.Semantic proximity Read

Discovery and validation of information theory-based transcription factor and cofactor binding site motifs.Semantic proximity Read

Benchmarking PWM and SVM-based Models for Transcription Factor Binding Site Prediction: A Comparative Analysis on Synthetic and Biological DataSemantic proximity Read

motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites.Semantic proximity Read

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function.Semantic proximity Read

Discovery of Primary, Cofactor, and Novel Transcription Factor Binding Site Motifs by Recursive, Thresholded Entropy MinimizationSemantic proximity Read

Ultra-fast variant effect prediction using biophysical transcription factor binding modelsSemantic proximity Read

Systematic Prediction of Regulatory Motifs from Human ChIP-Sequencing Data Based on a Deep Learning FrameworkSemantic proximity Read