Biology subjects

Hardison, R. C.

Publications and source records attributed to Hardison, R. C..

3 recordsLinked to original sources

bioRxiv2018

Transcriptional burst initiation and polymerase pause release are key control points of transcriptional regulation

Transcriptional regulation occurs via changes to the rates of various biochemical processes. Sequencing-based approaches that average together many cells have suggested that polymerase binding and polymerase release from promoter-proximal pausing are two key regulated steps in the transcriptional process. However, single cell studies have revealed that transcription occurs in short, discontinuous bursts, suggesting that transcriptional burst initiation and termination might also be regulated steps. Here, we develop and apply a quantitative framework to connect changes in both Pol II ChIP-seq and single cell transcriptional measurements to changes in the rates of specific steps of transcription. Using a number of global and targeted transcriptional regulatory perturbations, we show that burst initiation rate is indeed a key regulated step, demonstrating that transcriptional activity can be frequency modulated. Polymerase pause release is a second key regulated step, but the rate of polymerase binding is not changed by any of the biological perturbations we examined. Our results establish an important role for transcriptional burst regulation in the control of gene expression.

systems biology↗

bioRxiv2017

Accurate and Reproducible Functional Maps in 127 Human Cell Types via 2D Genome Segmentation

The Roadmap Epigenomics consortium has published whole-genome functional annotation maps in 127 human cell types and cancer cell lines by integrating data from multiple epigenetic marks. These maps have thereby been widely used by the community for studying gene regulation in cell type specific contexts and predicting functional impacts of DNA mutations on disease. Here, we present a new map of functional elements produced by a recently published method called IDEAS on the same data set. The IDEAS method has several unique advantages and was shown to outperform existing methods, including the one used by the Roadmap Epigenomics consortium. We further introduce a simple but highly effective pipeline to greatly improve the reproducibility of functional annotation. Using five categories of independent experimental results, we extensively compared the annotation produced by IDEAS and the Roadmap Epigenomics consortium. While the overall concordance between the two maps was high, we observed many differences in the details and in the position-wise consistency of annotation across cell types. We show that the IDEAS annotation was uniformly and often substantially more accurate than the Roadmap Epigenomics result. This study therefore reports on the quality of an existing functional map in 127 human genomes and provides an alternative and better map to be used by the community. The annotation result can be visualized in the UCSC genome browser via the hub at http://bx.psu.edu/~yuzhang/Roadmap_ideas/ideas_hub.txt

genomics↗

bioRxiv2017

HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient

Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility can produce misleading results because they ignore spatial features in Hi-C data, such as domain structure and distance dependence. We present HiCRep, a framework for assessing the reproducibility of Hi-C data that systematically accounts for these features. In particular, we introduce a novel similarity measure, the stratum adjusted correlation coefficient (SCC), for quantifying the similarity between Hi-C interaction matrices. Not only does it provide a statistically sound and reliable evaluation of reproducibility, SCC can also be used to quantify differences between Hi-C contact matrices and to determine the optimal sequencing depth for a desired resolution. The measure consistently shows higher accuracy than existing approaches in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages. The proposed measure is straightforward to interpret and easy to compute, making it well-suited for providing standardized, interpretable, automatable, and scalable quality control. The freely available R package HiCRep implements our approach.

bioinformatics↗