The Kinney Lab pursues an integrated combination of experiment, computation, and theory focused on problems in molecular biophysics, machine learning, and the study of sequence-function relationships. We are part of the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory. Principal Investigator
Justin B. Kinney
Assistant Professor
Simons Center for Quantitative Biology
Cold Spring Harbor Laboratory
PhD, Princeton, 2008; CV
jkinney@cshl.edu
News
Dec 2019: Congrats to Ammar on his paper, “Logomaker: beautiful sequence logos in Python”, which has now been published in Bioinformatics! Nov 2019: Congrats to Ammar on his paper, “Biophysical models of cis-regulation as interpretable neural networks”, which has been accepted to MLCB 2019! May 2019: Justin and David McCandlish just published a review in Annual Reviews of Genomics and Human Genetics. Jan 2019: Congrats to Rhys on his paper in Cell Systems! ! Dec 2018: Congrats to Talitha and Andalus on their paper in eLife ! Oct 2018: Congrats to Wei-Chia and Ammar their paper in Physical Review Letters ! Sept 2018: Congrats to Mandy on her paper in Molecular Cell! Aug 2018: Congrats to Anna on her paper in Genes! July 2018: Congratulations to Talitha on defending her PhD thesis! May 2018: Congrats to Nathan on his paper in PNAS! April 2018: Our density estimation software package, SUFTware , is live! So is our preprint. Congrats to Wei-Chia and Ammar for their excellent and tireless work on this project. April 2018: Anna Posfai has just joined our lab as a postdoc! She will be working jointly with David McCandlish on the quantitative modeling of sequence-function relationships. She comes from Princeton where she did some really baller work with Ned Wingreen.
Selected Publications and Preprints
Click here for a complete list of publications on Google Scholar.
Biophysical models of cis-regulation as interpretable neural networks.
1st Conference on Machine Learning in Computational Biology (MLCB 2019), Vancouver, Canada.
bioRxiv doi:10.1101/835942 .
Logomaker: beautiful sequence logos in Python.
Bioinformatics, btz921, 10 Dec 2019 .
Massively parallel assays and quantitative sequence-function relationships
Annu. Rev. Genomics Hum. Genet. 20:99-127 (2019).
Measuring cis-regulatory energetics in living cells using allelic manifolds
eLife 7:e40618 (2018). Open Access.
Density estimation on small datasets.
Phys. Rev. Lett. 121,160605 (2018). Open Access.
Quantitative activity profile and context dependence of all human 5′ splice sites.
Mol. Cell 71(6):1012-1026.e3 (2018). *Equal contribution. PDF.
Selection for protein stability enriches for epistatic interactions.
Genes 9(9):423 (2018). Open Access.
A systematic approach for dissecting the molecular mechanisms of transcriptional regulation in bacteria.
PNAS 115 (21) E4796-E4805 (2018). Open Access.
Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves.
eLife 2016;5:e23156 (2016). Open Access. *Equal contribution.
Learning quantitative sequence-function relationships from massively parallel experiments.
J Stat Phys 162(5):1203-1243 (2016). Open Access.
Unification of field theory and maximum entropy methods for learning probability densities.
Phys Rev E 92:032107 (2015). Open Access.
Equitability, mutual information, and the maximal information coefficient.
Proc Natl Acad Sci USA 111(9):3354-3359 (2014). Open Access.
Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence.
Proc Natl Acad Sci USA 107(20):9158-9163 (2010). Open Access.