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David McCandlish

David McCandlish

Associate Professor
Cancer Center Member

Ph.D., Duke University, 2012

mccandlish@cshl.edu | 516-367-5286

Faculty Profile

Some 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.

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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

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