Ph.D., Duke University, 2012
firstname.lastname@example.org | (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 in protein coding sequences.
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 antigenic and drug resistance phenotypes in rapidly evolving microbial pathogens.
Public Lecture: SEEING WITH SEQUENCING
March 13, 2019
Over 400 years ago, the invention of the microscope opened up a new world of discovery. Just sixteen years ago, the completion of the Human Genome Project launched a similar revolution in science. Since then, the continued advancement of high-throughput DNA sequencing – and quantitative methods for extracting knowledge from massive sequence datasets – has...
David McCandlish named Sloan Research Fellow
February 19, 2019
Assistant Professor David McCandlish has been named a 2019 Sloan Research Fellow. McCandlish is a quantitative biologist at Cold Spring Harbor Laboratory (CSHL), where he develops computational and mathematical tools to analyze genetic data. His lab focuses specifically on analyzing data from so-called “deep mutational scanning” experiments, which determine, for a single protein, the functional...
McCandlish, D. M. and Shah, P. and Plotkin, J. B. (2016) Epistasis and the Dynamics of Reversion in Molecular Evolution. Genetics, 203(3) pp. 1335-51.
McCandlish, D. M. and Otwinowski, J. and Plotkin, J. B. (2015) Detecting epistasis from an ensemble of adapting populations. Evolution, 69(9) pp. 2359-70.
Shah, P. and McCandlish, D. M. and Plotkin, J. B. (2015) Contingency and entrenchment in protein evolution under purifying selection. Proc Natl Acad Sci U S A, 112(25) pp. E3226-35.
McCandlish, David M. and Stoltzfus, Arlin (2014) Modeling evolution using the probability of fixation: history and implications. The Quarterly Review of Biology, 89(3) pp. 225-252.
McCandlish, D. M. (2011) Visualizing fitness landscapes. Evolution, 65(6) pp. 1544-58.Additional materials of the author at
CSHL Institutional Repository