A photo of Yaping Liu.

Yaping Liu, PhD

  • Assistant Professor, UC Department of Pediatrics



Thanks to my diverse academic and industry experiences, I have a broad background in epigenomics, single-cell multi-omics, computational biology, gene regulation, population genetics and liquid biopsy. These skills and interests drive me to understand the interactions between genetic and epigenetic variations — and bridge the gaps between genotype and phenotype.

My PhD training included developing computational and experimental methods for the high throughput epigenomic assay, specifically in DNA methylation and DNA accessibility/nucleosome positioning. Following my postdoctoral training, where I focused on population epigenomics, I worked as a principal computational biologist at a liquid biopsy company.

Together with experimental biologists, I developed NOMe-seq, Methyl-HiC and single-cell Methyl-HiC to profile the multi-omics within the same assay and even in the same single cell. Our NOMe-seq technology was selected as one of the Top 10 Innovations in 2013 by Scientist Magazine. Our single-cell Methyl-HiC technology was highlighted by Nature Method as Method of the Year 2019 (single-cell multimodal omics).

Since joining Cincinnati Children’s in 2019, my lab has focused on developing and applying machine learning and high-throughput experimental methods to understand gene regulation and non-coding genetic variants.

I’ve received numerous honors and awards, including:

  • Trustee Award, Cincinnati Children’s Research Foundation (2019)
  • Stellar Abstract Award, 11th Annual Program in Quantitative Genomics (PQG) Conference, School of Public Health, Harvard University (2017)
  • Stellar Abstract Award, 7th Annual PQG conference (2013)
  • Charles Heidelberger Memorial Fellowship, University of Southern California (2013)
  • SABRETRAIN Fellowship, European Marie Curie Host Fellowships for Early Stage Research Training, FP6 program (2009)


At the dawn: cell-free DNA fragmentomics and gene regulation. Liu, Y. British Journal of Cancer. 2022; 126:379-390.

FinaleDB: a browser and database of cell-free DNA fragmentation patterns. Zheng, H; Zhu, MS; Liu, Y. Computer Applications in the Biosciences. 2021; 37:2502-2503.

Expanded encyclopaedias of DNA elements in the human and mouse genomes. Abascal, F; Acosta, R; Addleman, NJ; Adrian, J; Afzal, V; Aken, B; Akiyama, JA; Jammal, OA; Amrhein, H; Anderson, SM; et al. Nature. 2020; 583:699-710.

Perspectives on ENCODE. Abascal, F; Acosta, R; Addleman, NJ; Adrian, J; Afzal, V; Aken, B; Akiyama, JA; Jammal, OA; Amrhein, H; Anderson, SM; et al. Nature. 2020; 583:693-698.

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA. Wan, N; Weinberg, D; Liu, TY; Niehaus, K; Ariazi, EA; Delubac, D; Kannan, A; White, B; Bailey, M; Bertin, M; et al. BMC Cancer. 2019; 19.

Joint profiling of DNA methylation and chromatin architecture in single cells. Li, G; Liu, Y; Zhang, Y; Kubo, N; Yu, M; Fang, R; Kellis, M; Ren, B. Nature Methods. 2019; 16:991-993.

Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Gamazon, ER; Segrè, AV; van de Bunt, M; Wen, X; Xi, HS; Hormozdiari, F; Ongen, H; Konkashbaev, A; Derks, EM; Aguet, F; et al. Nature Genetics. 2018; 50:956-967.

Evidence of reduced recombination rate in human regulatory domains. Liu, Y; Sarkar, A; Kheradpour, P; Ernst, J; Kellis, M. Genome Biology. 2017; 18.

Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease. Stranger, BE; Brigham, LE; Hasz, R; Hunter, M; Johns, C; Johnson, M; Kopen, G; Leinweber, WF; Lonsdale, JT; McDonald, A; et al. Nature Genetics. 2017; 49:1664-1670.

Estimating the causal tissues for complex traits and diseases. Ongen, H; Brown, AA; Delaneau, O; Panousis, NI; Nica, AC; GTEx Consortium, ; Dermitzakis, ET. Nature Genetics. 2017; 49:1676-1683.