Kinney Lab Home
Page last modified 21:04, 31 Jan 2013 by jkinney Kinney Lab Home >
Table of contents
Justin B. Kinney
Quantitative Biology Fellow at Cold Spring Harbor Laboratory
Sequence-function relationships; biophysics; deep sequencing; machine learning; transcriptional regulation; DNA replication
My research combines theory, computation, and experiment in an effort to better understand the relationship between sequence and function in molecular biology. The use of microarray and ultra-high-throughput sequencing technologies plays a central role in this work. Using these technologies, one can assay the activities of tens of thousands to millions of different biological sequences in simple, small-scale experiments. The resulting data can then be used infer precise quantitative modes for how biological sequence dictates function.
Importantly, one does not need a detailed understanding of experimental noise in order to reliably fit quantitative models of sequence-function relationships to such data. This was shown by Kinney et al. (2007) in the context of using ChIP-chip and protein binding microarray data to infer models for the DNA sequence-specificities of transcription factors. The same principle can be used to fit models to any data set comprising a list of sequences and corresponding measurements for whatever biological activity one is interested in.
In Kinney et al. (2010), a combination of fluorescence-activated cell sorting and 454 pyrosequencing was used to measure the transcriptional activities resulting from hundreds of thousands of slightly mutated versions of the Escherichia coli lac promoter. Fitting biophysically-inspired models not only allowed the sequence specificities of regulatory proteins to be determined in their native functional context, it also allowed the interaction energy between a DNA-bound transcription factor and a DNA-bound RNA polymerase holoenzyme to be measured in living cells. This approach — measuring the in vivo activity of a large number of slightly mutated regulatory sequences — provides a new way of interrogating the transcriptional regulatory code, and can likely be applied to a wide range of regulatory sequences in a variety of single-celled organisms as well as in cell culture.
Here is my CV.
Here is my faculty page on CSHL's website.
Kinney JB, Atwal GS (2013). Equitability, mutual information, and the maximal information coefficient. arXiv:1301.7745 [q-bio.QM].
Kinney JB, Atwal GS (2012). Maximally informative models and diffeomorphic modes in the analysis of large data sets. arXiv:1212.3647 [q-bio.QM].
Melnikov A, Murugan A, Zhang X, Tesileanu T, Wang L, Rogov P, Feizi S, Gnirke A, Callan CG, Kinney JB, Kellis M, Lander ES, Mikkelsen TS (2012). Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 30:271-277.
Kinney JB, Murugan A, Callan CG, Cox EC (2010) Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proc Natl Acad Sci USA. 107:9158-9163.
Mustonen V, Kinney JB, Callan CG, Lässig M (2008) Energy-dependent fitness: a quantitative model for the evolution of yeast transcription factor binding sites. Proc Natl Acad Sci USA. 105:12376-12381.
Kinney JB, Tkacik G, Callan CG (2007) Precise physical models of protein-DNA interaction from high-throughput data. Proc Natl Acad Sci USA.104:501-506.