Kexin Dong

Koch Institute, MIT

Cambridge, MA

I am a PhD candidate in Biology at MIT, working in the Sánchez-Rivera Lab and the Hemann Lab at the Koch Institute for Integrative Cancer Research. I use computational and experimental tools to study cancer-associated genetic variants at scale.

My research sits at the interface of cancer biology, precision genome editing and computational genomics. I developed H2M, a framework that predicts how human genetic variants can be modeled in the mouse genome — pairing each human variant with a murine equivalent that mirrors its sequence and functional consequences, and providing ready-to-use base- and prime-editing libraries. I also work on high-throughput prime editing and base editing sensor screens that measure the functional impact of thousands of cancer-associated variants at once. Currently, I’m studying how genetic events influence early leukemia evolution.

Before MIT, I earned my Biology undergraduate degree at the University of Chinese Academy of Sciences.

Outside the lab, I’m a boulderer, runner, and photographer.

News

Oct 20, 2026	Gave a short talk and received the MIT Prize for Open Data at the Open Data @ MIT event.
Oct 05, 2026	Honored to receive the MIT Prize for Open Data, awarded by the MIT School of Science and the MIT Libraries.
Jul 21, 2026	Passed my PhD qualifying exam.
Dec 18, 2025	Our H2M paper, Computational prediction of human genetic variants in the mouse genome, is published in Nature Biotechnology. Try the tool at human2mouse.com.
May 15, 2025	Joined the Sánchez-Rivera Lab and the Hemann Lab.
Feb 25, 2025	Two preprints out on bioRxiv: computational modeling of human variants in mice and multiplexed in vivo base editing.
Sep 01, 2024	Started my PhD in Biology at MIT.

Selected publications

Nat Biotechnol
Computational prediction of human genetic variants in the mouse genome

Kexin Dong, Samuel I. Gould, Minghang Li, and 1 more author

Nature Biotechnology, Dec 2025

Abs DOI Bib HTML Code Website

H2M is a computational framework that models human genetic variants in the mouse genome, predicting murine equivalents that mirror the sequence and functional consequences of human variants. H2M supports mouse-to-human and paralog-to-paralog mapping, and ships with a database of over three million human-mouse mutation pairs together with base- and prime-editing libraries for thousands of variant pairs.
@article{dong2025h2m, title = {Computational prediction of human genetic variants in the mouse genome}, author = {Dong, Kexin and Gould, Samuel I. and Li, Minghang and S{\'a}nchez-Rivera, Francisco J.}, journal = {Nature Biotechnology}, year = {2025}, month = dec, doi = {10.1038/s41587-025-02925-0}, url = {https://doi.org/10.1038/s41587-025-02925-0}, }

Nat Biotechnol

High-throughput evaluation of genetic variants with prime editing sensor libraries

Samuel I. Gould, Alexandra N. Wuest, Kexin Dong, and 7 more authors

Nature Biotechnology, 2024

Abs DOI Bib HTML

@article{gould2024sensor,
  title = {High-throughput evaluation of genetic variants with prime editing sensor libraries},
  author = {Gould, Samuel I. and Wuest, Alexandra N. and Dong, Kexin and Johnson, Grace A. and Hsu, Alvin and Narendra, Varun K. and Atwa, Ondine and Levine, Stuart S. and Liu, David R. and S{\'a}nchez-Rivera, Francisco J.},
  journal = {Nature Biotechnology},
  volume = {43},
  number = {10},
  pages = {1648--1662},
  year = {2024},
  doi = {10.1038/s41587-024-02172-9},
  url = {https://doi.org/10.1038/s41587-024-02172-9},
}