Ultra-high-throughput DNA sequencing has recently made it possible to study sequence-function relationships with the precision needed to infer meaningful quantitative models. Dr. Kinney and his laboratory have played a major role in the development of the massively parallel assays (MPAs) that provide such measurements. Kinney et al. (2010) introduced Sort-Seq, the first MPA for dissecting transcriptional regulatory sequences in living cells. More recently, Adams et al. (2016) described Tite-Seq, the first MPA capable of providing absolute affinity measurements of protein-ligand interactions. The Kinney Lab continues to develop MPAs for studying the quantitative structure of biological sequence-function relationships.
- Adams RM, Mora T*, Walczak AM*, Kinney JB*.
Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves.
eLife 2016;5:e23156 (2016). Open access. *Equal Contribution.
- Kinney JB, Murugan A, Callan CG, Cox EC.
Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence.
PNAS 107(20):9158-9163 (2010). Open access.
Inferring accurate quantitative models of sequence-function relationships from MPA data remains a challenge. The Kinney Lab has worked to establish both mathematical and computational methods for solving this problem. Kinney et al. (2007) showed that mutual-information-based inference allows quantitative models to be accurately inferred without the burdensome requirement of precisely quantifying experimental noise. Subsequent theoretical work of ours has clarified the mathematical relationship between mutual information and likelihood, introduced the concept of “diffeomorphic modes” in parameter space, and established the “self-equitability” of mutual information as a measure of statistical dependence. We have also developed the MPAthic software package, which (among other things) enables the mutual-information-based analysis of MPA data from the command line.
- Ireland WT, Kinney JB.
MPAthic: quantitative modeling of sequence-function relationships for massively parallel assays.
bioRxiv doi: http://dx.doi.org/10.1101/054676 (2016).
- Atwal G, Kinney JB.
Learning quantitative sequence-function relationships from massively parallel experiments.
J Stat Phys 162(5):1203-1243 (2016). Open access.
- Kinney JB, Atwal GS.
Parametric inference in the large data limit using maximally informative models.
Neural Comput 26(4):637-665 (2014). Open access.
- Kinney JB, Atwal GS.
Equitability, mutual information, and the maximal information coefficient.
PNAS 111(9):3354-3359 (2014). Open access.
- Kinney JB, Tkačik G, Callan CG.
Precise physical models of protein–DNA interaction from high-throughput data.
PNAS 104(2):501-506. (2007)
The problem of how to estimate smooth probability distributions from small data sets remains unresolved, even in just one and two dimensions. The Kinney Lab is working to address this fundamental problem in statistics using Bayesian Field Theory (a.k.a. Bayesian nonparametrics). Kinney (2014) and Kinney (2015) described a computationally feasible approach, called Density Estimation using Field Theory (DEFT), that requires no tunable parameters, no boundary conditions, and has close connections to Maximum Entropy estimation. The Kinney Lab continues to develop DEFT as a way to estimate entropy and mutual information, as well as to perform survival analysis on small clinical data sets.
Transcription in all organisms is regulated by large multi-protein-DNA complexes. However, simply writing down well-defined quantitative models describing the biophysics of such systems poses a challenge due to the combinatorial explosion of possible complexes. To address this problem, Kinney and Morrison (2016 preprint) have described Fock space structures and formal diagrammatic methods that allow biophysical models of multi-particle complexes to be defined in a rule-based manner. These diagrammatic methods facilitate both the specification of biophysical models and the analytical calculations of partition functions. The Kinney Lab continues to develop this approach for both analytical and computational studies of biochemical systems that comprise large multi-particle complexes.
- Morrison MJ, Kinney JB.
Modeling multi-particle complexes in stochastic chemical systems.
bioRxiv doi: http://dx.doi.org/10.1101/045435 (2016).