Biology subjects

Ouyang, N.

Publications and source records attributed to Ouyang, N..

4 recordsLinked to original sources

bioRxiv2022

Quantitative assessment of association between noncoding variants and transcription factor binding

Association fine-mapping of molecular traits is an essential method for understanding the impact of genetic variation. Sequencing-based assays, including RNA-seq, DNase-seq and ChIP-seq, have been widely used to measure different cellular traits and enabled genome-wide mapping of quantitative trait loci (QTLs). The disruption of cis-regulatory sequence, often occurring through variation within transcription factor binding motifs, has been strongly associated with gene dysregulation and human disease. We recently developed a computational method, TRACE, for transcription factor binding footprint prediction. TRACE integrates chromatin accessibility and transcription factor binding motifs to produce quantitative scores that describe the binding affinity of a TF for a specific TFBS locus. Here we have extended this method to incorporate variant data for 57 Yoruban individuals. Using genome-wide chromatin-accessibility data and human TF binding motifs, we have generated precise, genome-wide predictions of individual-specific transcription factor binding footprints. Subsequent association mapping between these footprints and nearby regulatory variants yielded numerous footprint-variant pairs with significant evidence for correlation, which we call footprint-QTLs (fpQTLs). fpQTLs appear to affect TF binding in a distance-dependent manner and share significant overlap with known dsQTLs and eQTLs. fpQTLs provide a rich resource for the study of regulatory variants, both within and outside known TFBSs, leading to improved functional interpretation of noncoding variation.

bioinformatics↗

bioRxiv2019

TRACE: transcription factor footprinting using DNase I hypersensitivity data and DNA sequence

Transcription is tightly regulated by cis-regulatory DNA elements where transcription factors can bind. Thus, identification of transcription factor binding sites is key to understanding gene expression and whole regulatory networks within a cell. The standard approaches for transcription factor binding sites (TFBSs) prediction such as position weight matrices (PWMs) and chromatin immunoprecipitation followed by sequencing (ChIP-seq) are widely used but have their drawbacks such as high false positive rates and limited antibody availability, respectively. Several computational footprinting algorithms have been developed to detect TFBSs by investigating chromatin accessibility patterns, but also have their limitations. To improve on these methods, we have developed a footprinting method to predict Transcription factor footpRints in Active Chromatin Elements (TRACE). Trace incorporates DNase-seq data and PWMs within a multivariate Hidden Markov Model (HMM) to detect footprint-like regions with matching motifs. Trace is an unsupervised method that accurately annotates binding sites for specific TFs automatically with no requirement on pre-generated candidate binding sites or ChIP-seq training data. Compared to published footprinting algorithms, TRACE has the best overall performance with the distinct advantage of targeting multiple motifs in a single model.

bioinformatics↗

bioRxiv2019

Transposable elements strongly contribute to cell-specific and species-specific looping diversity in mammalian genomes.

BackgroundChromatin looping is exceedingly important to gene regulation and a host of other nuclear processes. Many recent insights into 3D chromatin structure across species and cell types have contributed to our understanding of the principles governing chromatin looping. However, 3D genome evolution and how it relates to Mendelian selection remain largely unexplored. CTCF, an insulator protein found at most loop anchors, has been described as the \"master weaver\" of mammalian genomes, and variations in CTCF occupancy are known to influence looping divergence. A large fraction of mammalian CTCF binding sites fall within transposable elements (TEs) but their contributions to looping variation are unknown. Here we investigated the effect of TE-driven CTCF binding site expansions on chromatin looping in human and mouse.\n\nResultsTEs have broadly contributed to CTCF binding and loop boundary specification, primarily forming variable loops across species and cell types and contributing nearly 1/3 of species-specific and cell-specific loops.\n\nConclusionsOur results demonstrate that TE activity is a major source of looping variability across species and cell types. Thus, TE-mediated CTCF expansions explain a large fraction of population-level looping variation and may play a role in adaptive evolution.

genomics↗

bioRxiv2017

Genome Graphs

There is increasing recognition that a single, monoploid reference genome is a poor universal reference structure for human genetics, because it represents only a tiny fraction of human variation. Adding this missing variation results in a structure that can be described as a mathematical graph: a genome graph. We demonstrate that, in comparison to the existing reference genome (GRCh38), genome graphs can substantially improve the fractions of reads that map uniquely and perfectly. Furthermore, we show that this fundamental simplification of read mapping transforms the variant calling problem from one in which many non-reference variants must be discovered de-novo to one in which the vast majority of variants are simply re-identified within the graph. Using standard benchmarks as well as a novel reference-free evaluation, we show that a simplistic variant calling procedure on a genome graph can already call variants at least as well as, and in many cases better than, a state-of-the-art method on the linear human reference genome. We anticipate that graph-based references will supplant linear references in humans and in other applications where cohorts of sequenced individuals are available.

bioinformatics↗