From regulating gene expression to fighting off pathogens, biology uses DNA sequence information in many different ways. My research combines theory, computation, and experiment in an effort to better understand the quantitative relationships between DNA sequence and biological function. Much of my work is devoted to developing new methods in statistics and machine learning.
Justin Kinney completed his PhD in Physics at Princeton University in 2008 and began his term as a Quantitative Biology Fellow in 2010. His research focuses on developing next-generation DNA sequencing as a tool for dissecting the structure and function of large macromolecular complexes. Of particular interest to his lab is the biophysical basis of transcriptional regulation—how simple interactions between proteins and DNA allow promoters and enhancers to modulate genes in response to physiological signals.
In 2010, Kinney and colleagues published a paper demonstrating Sort-Seq, a novel sequencing-based method that can measure the functional activity of hundreds of thousands of slightly mutated versions of a specific DNA sequence of interest. Using a novel information-theoretic analysis of the resulting data, Kinney et al. were able to quantitatively measure, in living cells, the protein–DNA and protein–protein interactions controlling mRNA transcription at a chosen promoter. Kinney continues to develop this approach using a combination of theory, computation, and experiment. From a biological standpoint, Sort-Seq allows researchers to investigate important but previously inaccessible biological systems. Kinney’s lab is currently using Sort-Seq to address open problems in transcriptional regulation, DNA replication, and immunology. These experiments also present new challenges for the field of machine learning, and a substantial fraction of Kinney’s efforts are devoted to addressing the theoretical and computational problems relevant to the analysis of Sort-Seq data.
The big problem of small data: A new approach
October 18, 2018
You’ve heard of “big data” but what about small? Researches have crafted a modern approach that could solve a decades-old problem in statistics.
Predicting how splicing errors impact disease risk
August 30, 2018
New research helps correlate genetic mutations with errors in RNA splicing that can cause serious illness
CSHL receives $50 million to establish Simons Center for Quantitative Biology
July 7, 2014
CSHL announced a $50 million gift from Jim and Marilyn Simons to establish the Simons Center for Quantitative Biology.
Researchers propose new way to make sense of ‘Big Data’
February 15, 2014
New application of a classic concept challenges the latest statistical tools.
Forcier, T. L. and Ayaz, A. and Gill, M. S. and Jones, D. and Phillips, R. and Kinney, J. B. (2018) Measuring cis-regulatory energetics in living cells using allelic manifolds. eLife, 7
Chen, W. C. and Tareen, A. and Kinney, J. B. (2018) Density Estimation on Small Data Sets. Physical Review Letters, 121(16)
Wong, M. S. and Kinney, J. B. and Krainer, A. R. (2018) Quantitative Activity Profile and Context Dependence of All Human 5' Splice Sites. Mol Cell,
Adams, R. M. and Mora, T. and Walczak, A. M. and Kinney, J. B. (2016) Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves. Elife, 5
Kinney, J. B. (2015) Unification of field theory and maximum entropy methods for learning probability densities. Physical Review E, 92(3)
Kinney, J. B. and Murugan, A. and Callan, C. G. and Cox, E. C. (2010) Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proceedings of the National Academy of Sciences of the United States of America, 107(20) pp. 9158-9163.Additional materials of the author at
CSHL Institutional Repository