Computational Epigenetics

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The Computational Epigenetics Lab is hiring! Interested individuals should inquire here

Bioinformatics for DNA methylation: The Computational Epigenetics Lab develops computational methods for DNA methylation assay of the next generation. The team also creates novel analytics to support technologies that integrate DNA methylation, genetic variation, chromatin accessibility and conformation.

Multi-omics data analysis for biological discovery: The Computational Epigenetics Lab does integrative data analysis to understand how cells translate epigenetic information into transcriptional regulation and eventually into phenotypical manifestation. The team focuses on leveraging DNA methylation as a robust readout of the chromatin state and cell identity.

Biomarkers discovery for medical applications: The Computational Epigenetics Lab develops biomarkers and diagnostic methods to aid translational research of childhood disease, including pediatric malignancies, developmental abnormalities, cognitive deficits, and infectious disease.

Project Highlights

  • Highly proliferative tissue such as cancer is known for having undergone DNA methylation loss in adult humans. Through analyzing hundreds of human and mice methylomes, Dr. Zhou and his colleagues provided evidences to support mitotic division as the primary driving force of this DNA methylation attrition (Nature Genetics, 2018).
  • Infinium microarray has been the primary workhorse of DNA methylation assay. Dr. Zhou and his colleagues comprehensively characterized this array and identified its novel usage (Nucleic Acids Res, 2017). The team developed critical statistical methods that lower Type-I error rate in signal detection calling of this array (Nucleic Acids Res, 2018).
  • DNA methylation hypomethylating agent has been proposed as an effective adjuvant therapeutics for cancer immunotherapy. Dr. Zhou and his colleagues have performed data analysis and cell experiments to investigate DNMTi-ascorbate combination treatment for leukemia and MDS patients (PNAS, 2016).
  • Ambiguity in variant annotation has been hampering the implementation of precision medicine. Dr. Zhou developed a fuzzy searching algorithm that allows scientists to process and reverse-inference the genomic variants based on isoform-specific notations (Nature Methods, 2015).
  • DNA methylation is involved in genomic imprinting and is usually subject to strong purifying selection in the human population. Dr. Zhou and his colleagues identified nc886 genomic imprinting as a prominent example of polymorphic imprinting and linked its imprinting status to multiple human disease phenotypes (PNAS, 2018).
Zhou W, Triche TJ Jr, Laird PW, Shen H. SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res. 2018 Nov 16; 46(20):e123. doi: 10.1093/nar/gky691. PubMed PMID: 30085201; PubMed Central PMCID: PMC6237738
Zhou W, Dinh HQ, Ramjan Z, Weisenberger DJ, Nicolet CM, Shen H, Laird PW, Berman BP. DNA methylation loss in late-replicating domains is linked to mitotic cell division. Nat Genet. 2018 Apr 01; 50(4):591-602. doi: 10.1038/s41588-018-0073-4. Epub 2018 Apr 2. PubMed PMID: 29610480; PubMed Central PMCID: PMC5893360
Zhou W, Laird PW, Shen H. Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes. Nucleic Acids Res. 2017 Feb 28; 45(4):e22. doi: 10.1093/nar/gkw967. PubMed PMID: 27924034; PubMed Central PMCID: PMC5389466
Zhou W, Chen T, Chong Z, Rohrdanz MA, Melott JM, Wakefield C, Zeng J, Weinstein JN, Meric-Bernstam F, Mills GB, Chen K. TransVar: a multilevel variant annotator for precision genomics. Nat Methods. 2015 Nov 01; 12(11):1002-3. doi: 10.1038/nmeth.3622. PubMed PMID: 26513549; PubMed Central PMCID: PMC4772859
Zhou W, Nakhleh L. Quantifying and assessing the effect of chemical symmetry in metabolic pathways. J Chem Inf Model. 2012 Oct 22; 52(10):2684-96. doi: 10.1021/ci300259u. Epub 2012 Sep 28. PubMed PMID: 22985501

Wanding Zhou, PhD

Assistant Professor
Dr. Zhou’s outstanding research interests include mitosis-coupled DNA methylation drift and inference of cell-type-specific epigenetic signals. He developed multiple computational tools for analyzing DNA methylation data and has actively contributed to cancer genomics data analysis.