Biology subjects

Charles G Danko

Publications and source records attributed to Charles G Danko.

3 recordsLinked to original sources

bioRxiv2016

RTFBSDB: an integrated framework for transcription factor binding site analysis

Transcription factors (TFs) regulate complex programs of gene transcription by binding to short DNA sequence motifs. Here we introduce rtfbsdb, a unified framework that integrates a database of more than 65,000 TF binding motifs with tools to easily and efficiently scan target genome sequences. Rtfbsdb clusters motifs with similar DNA sequence specificities and optionally integrates RNA-seq or PRO-seq data to restrict analyses to motifs recognized by TFs expressed in the cell type of interest. Our package allows common analyses to be performed rapidly in an integrated environment.\n\nAvailability: rtfbsdb is available via github (https://github.com/Danko-Lab/rtfbsdb).

Bioinformatics↗

bioRxiv2015

A Unified Architecture of Transcriptional Regulatory Elements

Gene expression is precisely controlled in time and space through the integration of signals that act at gene promoters and gene-distal enhancers. Classically, promoters and enhancers are considered separate classes of regulatory elements, often distinguished by histone modifications. However, recent studies have revealed broad similarities between enhancers and promoters, blurring the distinction: active enhancers often initiate transcription, and some gene promoters have the potential of enhancing transcriptional output of other promoters. Here, we propose a model in which promoters and enhancers are considered a single class of functional element, with a unified architecture for transcription initiation. The context of interacting regulatory elements, and surrounding sequences, determine local transcriptional output as well as the enhancer and promoter activities of individual elements.

Genomics↗

bioRxiv2014

Accurate Identification of Active Transcriptional Regulatory Elements from Global Run-On and Sequencing Data

Identification of the genomic regions that regulate transcription remains an important open problem. We have recently shown that global run-on and sequencing (GRO-seq) with enrichment for 5'-capped RNAs reveals patterns of divergent transcription that accurately mark active transcriptional regulatory elements (TREs), including enhancers and promoters. Here, we demonstrate that active TREs can be identified with comparable accuracy by applying sensitive machine-learning methods to standard GRO-seq and PRO-seq data, allowing TREs to be assayed together with transcription levels, elongation rates, and other transcriptional features, in a single experiment. Our method, called discriminative Regulatory Element detection from GRO-seq (dREG), summarizes GRO-seq read counts at multiple scales and uses support vector regression to predict active TREs. The predicted TREs are strongly enriched for marks associated with functional elements, including H3K27ac, transcription factor binding sites, eQTLs, and GWAS-associated SNPs. Using dREG, we survey TREs in eight cell types and provide new insights into global patterns of TRE assembly and function.

Genomics↗