Biology subjects

Chow, S. H.-C.

Publications and source records attributed to Chow, S. H.-C..

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↗

bioRxiv2026

Correcting spatial transcriptomics data affected by a prevalent transcript leakage problem across platforms, species, and tissues

Spatial transcriptomics has been widely applied to study the spatial distribution of cell types, cell states, and specific gene expression in tissue samples. However, we show that there is a prevalent transcript leakage problem in spatial transcriptomics data, where transcripts expressed by a cell diffuse to its neighborhood and are recurrently detected in the nearby cells. By analyzing published data sets, we show that this problem is general across data produced from different tissues and different species using different imaging-based and sequencing-based spatial transcriptomics platforms. It affects both upstream tasks such as expression quantification as well as downstream tasks such as cell-type annotation and detection of spatially-dependent gene expression. To tackle the transcript leakage problem, we propose a reference-free Bayesian model-based method, DeLeakage, which cleans up the data much more effectively than existing denoising methods. DeLeakage also improves cell-type annotation and avoids false detection of spatially dependent expression.

bioinformatics↗

bioRxiv2025

Towards universal modeling of transcript isoform expression levels

A holy grail in computational biology is accurate modeling of transcript expression levels using epigenetic features, which would provide a quantitative way to study gene regulation in normal and disease states. Previous studies relied heavily on immortalized cell lines that exhibit properties different from cells in natural tissue environments. Most studies also quantified the expression of each gene by a single expression level, which fails to capture separate expression levels of different transcript isoforms of the same gene. In this study, making use of the latest large-scale dataset of paired transcriptomic and epigenomic data of human samples produced by the International Human Epigenome Consortium (IHEC), we computationally modeled the expression levels of individual transcript isoforms in 324 samples from 29 tissue types. We constructed the models using graph-based methods that integrate both location-specific epigenomic features and multiple types of gene-gene relationships. We found that to infer transcript isoform expression levels in a sample, a model that integrates information from many samples of other tissue types consistently outperforms a model trained on data from this sample itself, providing strong support that it is possible to construct a "universal" model that can accurately infer transcript isoform expression levels across tissue types.

bioinformatics↗

bioRxiv2024

Epigenomic analysis of hepatocellular carcinoma reveals aberrant cis-regulatory changes and dysregulated retrotransposons with prognostic potentials

Hepatocellular carcinoma (HCC) exhibits widespread epigenetic alterations, yet their impact on cis-regulatory elements (CREs) and retrotransposons remains poorly understood. Here, we present an integrated epigenomic and transcriptomic analysis of HCC tumors and matched tumor-adjacent normal tissues. We identified extensive DNA hypomethylation coupled with changes in histone modifications at partially methylated domains, CREs, and retrotransposons. These epigenetic aberrations were associated with dysregulated expression of genes involved in cell cycle regulation, immune response, and extracellular matrix organization. Notably, our findings revealed a novel mechanism for the transcriptional dysregulation of GPC3, a key HCC biomarker and immunotherapeutic target. We observed that GPC3 upregulation is driven by both the reactivation of a fetal liver super enhancer and hypomethylation of GPC3-associated CpG islands. Moreover, we found that DNA hypomethylation-driven aberrant expression of retrotransposons carries prognostic significance in HCC. Patients with high expression of a long non-coding RNA driven by a HERVE-int element exhibited more aggressive tumors, poorer clinical outcomes, and molecular features associated with favorable immunotherapy response. Together, our study provides a comprehensive resource for understanding the role of epigenetic dysregulation in HCC and identifies retrotransposon-associated transcripts as potential biomarkers.

genomics↗

bioRxiv2021

Quantifying full-length circular RNAs in cancer

Circular RNAs (circRNAs) are abundantly expressed in cancer. Their resistance to exonucleases enables them to have potentially stable interactions with different types of biomolecules. Alternative splicing can create different circRNA isoforms that have different sequences and unequal interaction potentials. The study of circRNA function thus requires knowledge of complete circRNA sequences. Here we describe psirc, a method that can identify full-length circRNA isoforms and quantify their expression levels from RNA sequencing data. We confirm the effectiveness and computational efficiency of psirc using both simulated and actual experimental data. Applying psirc on transcriptome profiles from nasopharyngeal carcinoma and normal nasopharynx samples, we discover and validate circRNA isoforms differentially expressed between the two groups. Compared to the assumed circular isoforms derived from linear transcript annotations, some of the alternatively spliced circular isoforms have 100 times higher expression and contain substantially fewer microRNA response elements, demonstrating the importance of quantifying full-length circRNA isoforms.

bioinformatics↗