David McCandlish
Associate Professor
Cancer Center Member
Ph.D., Duke University, 2012
mccandlish@cshl.edu | 516-367-5286
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 鈥渄eep mutational scanning鈥� 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.
You say genome editing, I say natural mutation
October 19, 2023
Laboratoryscientists have discovered that evolution and genome editing in crops are less predictable than previously thought.
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鈥檚 play, Darwin鈥檚 Smile, at 麻豆传媒社区.
Getting a step ahead of TB鈥檚 drug resistance evolution
February 15, 2022
Mutations are not random, with some kinds of changes occurring more often than others. Laboratoryresearchers 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 Laboratorydetermine how mutations lead to different behaviors in cancerous cells.
How to outwit evolution
July 21, 2021
LaboratoryAssistant Professor David McCandlish uses statistical methods to predict the evolution of antibiotic resistance in bacteria.
Predicting the evolution of genetic mutations
April 14, 2020
Laboratoryquantitative 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 麻豆传媒社区.
Peter Koo wants to understand how machines learn biology
September 20, 2019
Dr. Peter Koo joins the Laboratoryfaculty 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 Laboratoryexperts
April 19, 2019
Quantitative biologists discuss how physics, modern computing power, and a new perspective on biology can make sense of our complex genomes.
All Publications
30 Oct 2024 | Physical Review E | 110(4)
Chen, Wei-Chia; Zhou, Juannan; McCandlish, David;
15 Oct 2024 | bioRxiv
Sun, Mengyi; Stoltzfus, Arlin; McCandlish, David;
26 Sep 2024 | bioRxiv
Sokirniy, Ivan; Inam, Haider; Tomaszkiewicz, Marta; Reynolds, Joshua; McCandlish, David; Pritchard, Justin;
19 Sep 2024 | Nature Communications | 15(1):8237
Gitschlag, Bryan; Pereira, Claudia; Held, James; McCandlish, David; Patel, Maulik;
Jun 2024 | Nature Machine Intelligence | 6(6):701-713
Seitz, E; McCandlish, D; Kinney, J; Koo, P;
16 May 2024 | PLoS Biology | 22(5):e3002594
Rozho艌ová, Hana; Martí-Gómez, Carlos; McCandlish, David; Payne, Joshua; Agashe, Deepa;
13 May 2024 | bioRxiv
Posfai, Anna; McCandlish, David; Kinney, Justin;
13 May 2024 | bioRxiv
Posfai, Anna; Zhou, Juannan; McCandlish, David; Kinney, Justin;
Guidelines for releasing a variant effect predictor
16 Apr 2024 | ArXiv
Livesey, Benjamin; Badonyi, Mihaly; Dias, Mafalda; Frazer, Jonathan; Kumar, Sushant; Lindorff-Larsen, Kresten; McCandlish, David; Orenbuch, Rose; Shearer, Courtney; Muffley, Lara; Foreman, Julia; Glazer, Andrew; Lehner, Ben; Marks, Debora; Roth, Frederick; Rubin, Alan; Starita, Lea; Marsh, Joseph;
29 Feb 2024 | Nature Communications | 15(1):1880
Ishigami, Yuma; Wong, Mandy; Martí-Gómez, Carlos; Ayaz, Andalus; Kooshkbaghi, Mahdi; Hanson, Sonya; McCandlish, David; Krainer, Adrian; Kinney, Justin;