Biology subjects

Chapman, A. R.

Publications and source records attributed to Chapman, A. R..

5 recordsLinked to original sources

bioRxiv2026

EpiATLAS - a reference for human epigenomic research

The sequence of the human genome provides a foundation for understanding cellular processes in health and disease1. The organisation of this primary genetic information into cell-specific structure and function is critical to understanding the cell type-specific interpretation and execution of the genome. Epigenetic processes are essential for packaging and higher-level functional organisation of the genome, and changes therein are increasingly recognised as contributors to human disease. Building on primary data generated by multinational consortia, the International Human Epigenome Consortium2 (IHEC) has uniformly processed a collection of more than 2000 comprehensive human reference epigenomes, collectively referred to as EpiATLAS. This effort involved the development of standardised molecular and bioinformatics protocols, metadata models, and analytical tools to manage, integrate, display, and share vast amounts of epigenomic data. This includes the creation of a publicly available Epigenome Reference Registry, which provides a system for accessing protected human subject datasets and facilitates open searching of de-identified samples and experimental data. The integrated EpiATLAS ecosystem and its comprehensive human reference epigenome maps provide an unprecedented resource for the biosciences, expanding the annotated epigenomic landscape while uncovering previously unappreciated relationships among regulatory layers and revealing how epigenetic inputs underpin fundamental cellular functions and disease associations.

genomics↗

bioRxiv2024

Epigenetic control of metabolic identity across cell types

BackgroundConstraint-based network modeling is a powerful genomic-scale approach for analyzing cellular metabolism, capturing metabolic variations across tissues and cell types, and defining the metabolic identity essential for identifying disease-associated transcriptional states. ResultsUsing RNA-seq and epigenomic data from the EpiATLAS resource of the International Human Epigenome Consortium (IHEC), we reconstructed metabolic networks for 1,555 samples spanning 58 tissues and cell types. Analysis of these networks revealed the distribution of metabolic functionalities across human cell types and provides a compendium of human metabolic activity. This integrative approach allowed us to define, across tissues and cell types, i) reactions that fulfil the basic metabolic processes (core metabolism), and ii) cell type-specific functions (unique metabolism), that shape the metabolic identity of a cell or a tissue. Integration with EpiATLAS-derived cell-type-specific gene-level chromatin states and enhancer-gene interactions identified enhancers, transcription factors, and key nodes controlling core and unique metabolism. Transport and first reactions of pathways were enriched for high expression, active chromatin state, and Polycomb-mediated repression in cell types where pathways are inactive, suggesting that key nodes are targets of repression. DiscussionThis integrative analysis forms the basis for identifying regulation points that control metabolic identity in human cells.

systems biology↗

bioRxiv2021

AusTraits: a curated plant trait database for the Australian flora

We introduce the AusTraits database - a compilation of measurements of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 375 traits across 29230 taxa from field campaigns, published literature, taxonomic monographs, and individual taxa descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological parameters (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual-, species- and genus-level observations coupled to, where available, contextual information on site properties. This data descriptor provides information on version 2.1.0 of AusTraits which contains data for 937243 trait-by-taxa combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data to increase our collective understanding of the Australian flora.

ecology↗

bioRxiv2020

Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

By circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq (METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent. We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs. HighlightsO_LIGenomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetus C_LIO_LIDetermining correlated gene modules (CGMs) in different cell types by MALBAC-DT C_LIO_LIMeasuring chromatin open regions in single cells with high detectability by METATAC C_LIO_LIIntegrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM C_LI

genomics↗

bioRxiv2020

Correlated Gene Modules Uncovered by Single-Cell Transcriptomics with High Detectability and Accuracy

Single cell transcriptome sequencing has become extremely useful for cell typing. However, such differential expression data has shed little light on regulatory relationships among genes. Here, by examining pairwise correlations between mRNA levels of any two genes under steady-state conditions, we uncovered correlated gene modules (CGMs), clusters of intercorrelated genes that carry out certain biological functions together. We report a novel single-cell RNA-seq method called MALBAC-DT with higher detectability and accuracy, allowing determination of the covariance matrix of the expressed mRNAs for a homogenous cell population. We observed a prevalence of positive correlations between pairs of genes, with higher correlations corresponding to higher likelihoods of protein-protein interactions. Some CGMs, such as the p53 module in a cancer cell line, are cell type specific, while others, such as the protein synthesis CGM, are shared by different cell types. CGMs distinguished direct targets of p53 and exposed different modes of regulation of these genes in different cell types. Our covariance analyses of steady-state fluctuations provides a powerful way to advance our functional understanding of gene-to-gene interactions.

genomics↗