Principal Investigator
Justin B. Kinney
Associate Professor
Simons Center for Quantitative Biology
Cold Spring Harbor Laboratory
PhD, Princeton, 2008
jkinney@cshl.edu
The Kinney Lab aims to decipher biophysical mechanisms of gene regulation through the measurement and modeling of sequence-function relationships. We pursue this goal through a tightly knit combination of theory, computation, and experiment.
Our experimental work focuses on the development and application of multiplex assays of variant effect (MAVEs), a burgeoning class of high-throughput techniques that we have helped to pioneer over the last decade. Specifically, we use MAVEs to measure the effects that a large number of different mutations within specific regulatory sequences of interest have on gene expression. We then characterize the molecular mechanisms of gene regulation by quantitatively modeling the sequence-function relationships measured in these experiments. We primarily pursue this experimental work in two biological contexts: alternative mRNA splicing in humans and transcriptional regulation in bacteria. Our splicing studies are carried out in partnership with Adrian Krainer (CSHL), while our transcription studies are pursued in collaboration with Rob Phillips (Caltech) and Bryce Nickels (Rutgers).
Our theoretical and computational work develops methods for analyzing the data produced by these and other types of MAVEs. We aim to extract biophysically meaningful models of regulatory sequence function, but also to understand the quantitative nature of sequence-function relationships more broadly. These efforts include developing new mathematical and algorithmic strategies, as well as deploying robust software that implements these approaches for use by the larger MAVE community.
Selected Publications and Preprints
Click here for a complete list of publications on Google Scholar.
MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect.
bioRxiv doi:10.1101/2020.07.14.201475 (2020) .
Non-parametric Bayesian density estimation for biological sequence space with applications to pre-mRNA splicing and the karyotypic diversity of human cancer.
bioRxiv doi:10.1101/2020.11.25.399253 (2020) .
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.