Biology subjects

Xiang, G.

Publications and source records attributed to Xiang, G..

23 records · Page 2Linked to original sources

bioRxiv2019

Systematic integration of GATA transcription factors and epigenomes via IDEAS paints the regulatory landscape of mouse hematopoietic cells

Members of the GATA family of transcription factors play key roles in the differentiation of specific cell lineages by regulating the expression of target genes. Three GATA factors play distinct roles in hematopoietic differentiation. In order to better understand how these GATA factors function to regulate genes throughout the genome, we are studying the epigenomic and transcriptional landscapes of hematopoietic cells in a model-driven, integrative fashion. We have formed the collaborative multi-lab VISION project to conduct ValIdated Systematic IntegratiON of epigenomic data in mouse and human hematopoiesis. The epigenomic data included nuclease accessibility in chromatin, CTCF occupancy, and histone H3 modifications for twenty cell types covering hematopoietic stem cells, multilineage progenitor cells, and mature cells across the blood cell lineages of mouse. The analysis used the Integrative and Discriminative Epigenome Annotation System (IDEAS), which learns all common combinations of features (epigenetic states) simultaneously in two dimensions - along chromosomes and across cell types. The result is a segmentation that effectively paints the regulatory landscape in readily interpretable views, revealing constitutively active or silent loci as well as the loci specifically induced or repressed in each stage and lineage. Nuclease accessible DNA segments in active chromatin states were designated candidate cis-regulatory elements in each cell type, providing one of the most comprehensive registries of candidate hematopoietic regulatory elements to date. Applications of VISION resources are illustrated for regulation of genes encoding GATA1, GATA2, GATA3, and Ikaros. VISION resources are freely available from our website http://usevision.org.

genomics↗

bioRxiv2019

An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis

Thousands of epigenomic datasets have been generated in the past decade, but it is difficult for researchers to effectively utilize all the data relevant to their projects. Systematic integrative analysis can help meet this need, and the VISION project was established for ValIdated Systematic IntegratiON of epigenomic data in hematopoiesis. Here, we systematically integrated extensive data recording epigenetic features and transcriptomes from many sources, including individual laboratories and consortia, to produce a comprehensive view of the regulatory landscape of differentiating hematopoietic cell types in mouse. By employing IDEAS as our Integrative and Discriminative Epigenome Annotation System, we identified and assigned epigenetic states simultaneously along chromosomes and across cell types, precisely and comprehensively. Combining nuclease accessibility and epigenetic states produced a set of over 200,000 candidate cis-regulatory elements (cCREs) that efficiently capture enhancers and promoters. The transitions in epigenetic states of these cCREs across cell types provided insights into mechanisms of regulation, including decreases in numbers of active cCREs during differentiation of most lineages, transitions from poised to active or inactive states, and shifts in nuclease accessibility of CTCF-bound elements. Regression modeling of epigenetic states at cCREs and gene expression produced a versatile resource to improve selection of cCREs potentially regulating target genes. These resources are available from our VISION website (usevision.org) to aid research in genomics and hematopoiesis.

genomics↗

bioRxiv2018

S3norm: simultaneous normalization of sequencing depth and signal-to-noise ratio in epigenomic data

Quantitative comparison of epigenomic data across multiple cell types or experimental conditions is a promising way to understand the biological functions of epigenetic modifications. However, differences in sequencing depth and signal-to-noise ratios in the data from different experiments can hinder our ability to identify real biological variation from raw epigenomic data. Proper normalization is required prior to data analysis to gain meaningful insights. Most existing methods for data normalization standardize signals by rescaling either background regions or peak regions, assuming that the same scale factor is applicable to both background and peak regions. While such methods adjust for differences in sequencing depths, they do not address differences in the signal-to-noise ratios across different experiments. We developed a new data normalization method, called S3norm, that normalizes the sequencing depths and signal-to-noise ratios across different data sets simultaneously by a monotonic nonlinear transformation. We show empirically that the epigenomic data normalized by our method, compared to existing methods, can better capture real biological variation, such as impact on gene expression regulation.

bioinformatics↗

bioRxiv2018

Snapshot: clustering and visualizing epigenetic history during cell differentiation

Epigenetic modification of chromatin plays a pivotal role in regulating gene expression during cell differentiation. The scale and complexity of epigenetic data pose significant challenges for biologists to identify the regulatory events controlling cell differentiation. Here, we present a new method, called Snapshot, that uses epigenetic data to generate a hierarchical visualization for DNA regions with epigenetic features segregating along any given cell differentiation hierarchy of interest. Different hierarchies of cell types may be used to highlight the epigenetic history specific to any particular cell lineage. We demonstrate the utility of Snapshot using data from the VISION project, an international project for ValIdated Systematic IntegratiON of epigenomic data in mouse and human hematopoiesis.\n\nAvailability and implementation: https://github.com/guanjue/snapshot

bioinformatics↗

bioRxiv2017

Resequencing the Escherichia coli genome by GenoCare single molecule sequencing platform

Next generation sequencing (NGS) has revolutionized life sciences research. Recently, a new class of third-generation sequencing platforms has arrived to meet increasing demands in the clinic, capable of directly measuring DNA and RNA sequences at the single-molecule level without amplification. Here, we use the new GenoCare single molecule sequencing platform from Direct Genomics to resequence the E. coli genome and show comparable performance to the Illumina MiSeq system. Our platform detects single-molecule fluorescence by total internal reflection microscopy, with sequencing-by-synthesis chemistry. With a consensus sequence of 99.71% nucleotide identity to that of the Illumina MiSeq systems, GenoCare was determined to be a reliable platform for single-molecule sequencing, with strong potential for clinical applications.

genomics↗