| Analysis of Genotype - Phenotype Relationships |
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A major research focus is
analysis genotype-phenotype relationships using tree-based statistical
models (referred to as decision trees in the machine learning
community) and a recent extension (random forests). Among my
investigations in this area examine how DNA or protein sequence data
can be used to understand and predict variation in basic immune system
functions at the molecular level (Segal et al. 2001), RNA editing in plant
mitochondrial genomes (Cummings and Myers 2004), and drug resistance in
tuberculosis (Cummings and Segal 2004). More generally, the relationships of
genotype to phenotype is a fundamental problem in genetics and through
these investigations it is hoped that deeper understanding of these
relationships will be gained.
Much of our present work in genotype-phenotype relationships includes
consideration of the protein structural context. One example is my
National Science Foundation funded research with Rebecca Gast and David Beaudoin on cold adaptation of
tubulins from protists living in arctic and antarctic environments. Microtubules are highly conserved biological
structures that are essential components of eukaryotic cellular
functions such as cell division, locomotion and maintenance of
cytoskeletal structure. The microtubule is formed by the assembly
(polymerization) of tubulin subunits, along with a heterogeneous
collection of associated proteins (MAPs). Tubulin subunits are
actually composed of two related, but distinct, proteins: alpha
and beta tubulin. Heterodimers of alpha and beta tubulin
associate longitudinally to form protofilaments, with 13 of these
filaments normally coming together in the creation of a microtubule.
Microtubule assembly is mediated largely through hydrophobic
interactions that occur between the carboxy terminus of the
alpha tubulin and the amino terminus of the beta tubulin. A
well-documented characteristics of these microtubules is that they
disassemble (depolymerize) at low temperatures.
Using machine learning methods we have identified changes at specific
residues that are associated with adaptation of tubulins to cold
environments.
Our other current research in genotype-phenotype relationships also
considers ecological influences on phenotype. Studies in this area
include our Department of Energy funded research with Egbert Schwartz and Bruce Hungate using combined data
from microbial genomics and environmental measurements to predict
nitrous oxide fluxes from soil.
Personnel:
Matthew Conte
Michael P. Cummings
Collaborators:
David J. Beaudoin, Woods Hole Oceanographic Institution
Rebecca J. Gast, Woods Hole Oceanographic Institution
Bruce Hungate, Northern Arizona University
Daniel S. Myers, Massachusetts Institute of Technology
Mark R. Segal, University of California, San Francisco
Egbert Schwartz, Northern Arizona University
Further Information:
e-mail Michael P. Cummings
Cummings, M. P., D. S. Myers. 2004. Simple statistical models predict C-to-U edited sites in plant mitochondrial RNA. BMC Bioinformatics 5:132.
Cummings, M. P. , D. S. Myers, and M. Mangelson. 2004. Applying Permutation Tests to Tree-Based Statistical Models: Extending the R Package rpart. Tehnical Report CS-TR-4581, UMIACS-TR-2004-24, Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, University of Maryland.
Cummings, M. P., M. R. Segal. 2004. Few amino acid positions in rpoB are associated with most of the rifampin resistance in Mycobacterium tuberculosis. BMC Bioinformatics 5:137.
Segal, M. R., M. P. Cummings and A. E. Hubbard. 2001. Relating
amino acid sequence to phenotype: analysis of peptide binding data.
Biometrics 57:632-643.
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