The Fullwood lab focuses on human genome annotation, transcriptome characterization, and understanding transcription regulation, to promote human personal genomics, particularly in cancer. In vivo, DNA is compacted into chromatin. The processes surrounding DNA are complex, and these “epigenetic” processes can include histone modifications, transcription factors that regulate gene expression. Cancer genome sequencing has revealed that many factors associated with epigenetic processes are mutated in cancers, and epigenetic processes have been targeted by specific drugs. In addition, RNA has been found to be more complex than previously thought, and long non-coding RNAs, previously thought of as “transcriptional noise”, have been found to play roles in cancer.

Chromatin immunoprecipitation (ChIP) with sequencing and RNA sequencing are powerful tools that rely on new next-generation sequencing approaches to provide deep insights into epigenetic processes. Moreover, Dr Fullwood’s research on developing Chromatin Interaction Analysis with Paired-End Tag sequencing (ChIA-PET) has suggested that chromatin interactions, which are regions of the genome that are far apart in the linear genome sequence but come together in close 3-dimensional spatial proximity, may constitute common mechanisms for gene regulation.

Dr Fullwood’s current work focuses on using ChIP sequencing, RNA sequencing, and ChIA-PET on a gastric cancer cell model, as well as other cancer cell models, to elucidate the detailed epigenomic profiles, allowing for new insights into possible cancer-associated biomarkers and cancer therapies. In addition, Dr Fullwood is interested in continuing to develop and refine new genomic technologies to understand chromatin and transcription.

1. Cao F.*, Zhang Y*., Cai Y.*, Animesh S., Zhang Y., Akincilar S., Loh Y. P., Chng W.J., Tergaonkar V., Kwoh C.K., Fullwood M.J.^, “Chromatin interaction Neural Network (ChINN): A machine learning-based method for predicting chromatin interactions from DNA sequences”, Genome Biology, 2021.

2. Zhang, Y.*, Cai, Y.*, Roca, X., Kwoh C.K.^, Fullwood M.J.^, “Chromatin loop anchors predict transcript and exon usage”, Briefings in Bioinformatics, 2021

3. Cai, Y.*, Zhang, Y.*, Loh, Y.P., Tng, J.Q., Lim, M.C., Cao, Z., Anandhkumar, R., Shang, L., Lakshmanan, M., Tergaonkar, V.., Tucker-Kellogg, G.^, Fullwood, M.J, ^, “H3K27me3-rich regions can function as silencers to repress gene expression via chromatin interactions”, Nat Commun, 2021

4. Jiang, Y.*, Jiang, Y.*, Li, C.*, Zhang, Y.*, Dakle, P.*, Kaur, H., Deng, J., Lin, Y., Han, L., Xie, J., Mayakonda, A., Hazawa, M., Xu, L., Li, Y., Aswad, L., Jeitany, M., Kanojia, D., Guan, X.,Fullwood, M.J.^, Lin, D.^ and Koeffler, H.P.^ “TP63, SOX2 and KLF5 Establish Core Regulatory Circuitry and Construct Cancer Specific Epigenome in Esophageal Squamous Cell Carcinoma”, 2020 

5. Cao F. and Fullwood, M.J., “Inflated performance measures in enhancer-promoter interaction-prediction methods”, Nature Genetics, 2019 

6. See, Y.*, Wang, B.*, and Fullwood, M.J., “Chromatin Interactions and Regulatory Elements in Cancer: From Bench to Bedside,” Trends in Genetics, 2019

7. Cao et al., Super-Enhancers and Broad H3K4me3 Domains Form Complex Gene Regulatory Circuits Involving Chromatin Interactions, Scientific Reports., 2017

8. Fang and Fullwood, Roles, Functions and Mechanisms of Long Non-Coding RNAs in Cancer, Genomics Proteomics Bioinformatics, 2016

9. Babu, D. and Fullwood, M.J., “3D Genome Organization in Health and Disease: Emerging Opportunities in Cancer Translational Medicine”, Nucleus. 2015

10. Choy, J.Y.*, Boon, P.L.*, Bertin, N.*, and Fullwood, M.J., “A Resource of Ribosomal RNA-depleted RNA-Seq data from Different Normal Adult and Fetal Human Tissues”,  Data, 2015