David McCandlish
Associate Professor
Cancer Center Member
Ph.D., Duke University, 2012
mccandlish@cshl.edu | 516-367-5286
Faculty ProfileSome mutations are harmful but others are benign. How can we predict the effects of mutations, both singly and in combination? Using data from experiments that simultaneously measure the effects of thousands of mutations, I develop computational tools to predict the functional impact of mutations and apply these tools to problems in protein design, molecular evolution, and cancer.
The McCandlish lab develops computational and mathematical tools to analyze and exploit data from high-throughput functional assays. The current focus of the lab is on analyzing data from so-called “deep mutational scanning” experiments. These experiments simultaneously determine, for a single protein, the functional effects of thousands of mutations. By aggregating information across the proteins assayed using this technique, we seek to develop data-driven insights into basic protein biology, improved models of molecular evolution, and more accurate methods for predicting the functional effects of mutations in human genome sequences.
Critically, these data also show that the functional effects of mutations often depend on which other mutations are present in the sequence. We are developing new techniques in statistics and machine learning to infer and interpret the complex patterns of genetic interaction observed in these experiments. Our ultimate goal is to be able to model these sequence-function relationships with sufficient accuracy to guide the construction of a new generation of designed enzymes and drugs, and to be able to predict the evolution of drug resistance phenotypes in both populations of cancer cells and rapidly evolving microbial pathogens.
How evolved is your knowledge?
January 26, 2023
Test your knowledge of evolution with this quiz, inspired by the March 2023 performances of Isabella Rossellini’s play, Darwin’s Smile, at CSHL.
Getting a step ahead of TB’s drug resistance evolution
February 15, 2022
Mutations are not random, with some kinds of changes occurring more often than others. CSHL researchers may be able to predict which direction evolution is li
Calculating the path of cancer
October 4, 2021
A new mathematical approach is helping cancer researchers at CSHL determine how mutations lead to different behaviors in cancerous cells.
How to outwit evolution
July 21, 2021
CSHL Assistant Professor David McCandlish uses statistical methods to predict the evolution of antibiotic resistance in bacteria.
Predicting the evolution of genetic mutations
April 14, 2020
CSHL quantitative biologists have designed a computational approach for predicting the evolution of a rapidly mutating virus or cancer.
A science career path: David McCandlish
April 10, 2020
Assistant Professor David McCandlish is a quantitative biologist who walks the line between advanced mathematics and the life sciences at CSHL.
Peter Koo wants to understand how machines learn biology
September 20, 2019
Dr. Peter Koo joins the CSHL faculty as an assistant professor. His focus is on exploring how artificial intelligence integrates with biology and genomics.
Event: Public Lecture: Seeing With Sequencing
August 8, 2019
Come hear from three quantitative biologists as they discuss how they see with sequencing to solve mysteries ranging from the genetics of evolution.
Seeing with sequencing—A public lecture with three CSHL experts
April 19, 2019
Quantitative biologists discuss how physics, modern computing power, and a new perspective on biology can make sense of our complex genomes.
David McCandlish named Sloan Research Fellow
February 19, 2019
Assistant Professor David McCandlish has been named a 2019 Sloan Research Fellow for his promising work in the field of quantitative biology.
All Publications
Higher-order epistasis and phenotypic prediction
27 Sep 2022 | Proceedings of the National Academy of Sciences of USA | 119(39):e2204233119
Zhou, Juannan, Wong, Mandy, Chen, Wei-Chia, Krainer, Adrian, Kinney, Justin, McCandlish, David
MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
15 Apr 2022 | Genome Biology | 23(1):98
Tareen, Ammar, Kooshkbaghi, Mahdi, Posfai, Anna, Ireland, William, McCandlish, David, Kinney, Justin
Mutation bias shapes the spectrum of adaptive substitutions
15 Feb 2022 | Proceedings of the National Academy of Sciences of USA | 119(7):e2119720119
Cano, Alejandro, Rozhoňová, Hana, Stoltzfus, Arlin, McCandlish, David, Payne, Joshua
Genomic and experimental evidence that ALKATI does not predict single agent sensitivity to ALK inhibitors
19 Nov 2021 | iScience | 24(11):103343
Inam, H, Sokirniy, I, Rao, Y, Shah, A, Naeemikia, F, O'Brien, E, Dong, C, McCandlish, D, Pritchard, J
Field-theoretic density estimation for biological sequence space with applications to 5' splice site diversity and aneuploidy in cancer.
5 Oct 2021 | Proceedings of the National Academy of Sciences of USA | 118(40)
Chen, Wei-Chia, Zhou, Juannan, Sheltzer, Jason, Kinney, Justin, McCandlish, David
System-specificity of genotype-phenotype map structure: Comment on “From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics” by Susanna Manrubia et al.
2 Sep 2021 | Physics of Life Reviews
McCandlish, D
Non-parametric Bayesian density estimation for biological sequence space with applications to pre-mRNA splicing and the karyotypic diversity of human cancer
10 Dec 2020 | bioRxiv
Chen, Wei-Chia, Zhou, Juannan, Sheltzer, Jason, Kinney, Justin, McCandlish, David
Empirical variance component regression for sequence-function relationships
15 Oct 2020 | BioRxiv
Zhou, Juannan, Wong, Mandy, Chen, Wei-Chia, Krainer, Adrian, Kinney, Justin, McCandlish, David
MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
14 Jul 2020 | bioRxiv
Tareen, Ammar, Posfai, Anna, Ireland, William, McCandlish, David, Kinney, Justin
Evolution of Epistasis: Small Populations Go Their Separate Ways
Jul 2020 | Journal of Molecular Evolution | 88(5):418-420
McCandlish, D, Lang, G