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.
Researchers propose new way to make sense of ‘Big Data’
February 15, 2014
Justin Kinney and Gurinder Atwal collaborated to show how a fundamental mathematical quantity called “mutual information” can be used to detect and quantify relationships between variables in large, noisy datasets.
Kinney, J. B. (2014) Estimation of probability densities using scale-free field theories. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 90(1) pp. Article no. 011301.
Kinney, J. B. and Atwal, G. S. (2014) Equitability, mutual information, and the maximal information coefficient. Proceedings of the National Academy of Sciences of the United States of America, 111(9) pp. 3354-9.
Kinney, J. B. and Atwal, G. S. (2014) Parametric Inference in the Large Data Limit Using Maximally Informative Models. Neural Comput, 26(4) pp. 637-653.
Melnikov, A. and Murugan, A. and Zhang, X. and Tesileanu, T. and Wang, L. and Rogov, P. and Feizi, S. and Gnirke, A. and Callan Jr, C. G. and Kinney, J. B. and Kellis, M. and Lander, E. S. and Mikkelsen, T. S. (2012) Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nature Biotechnology, 30(3) pp. 271-277.
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
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