Research Topics
| Hunter B FraserSummaryAffiliation: University of California Country: USA Publications
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Detail Information
Publications
Evolutionary rate in the protein interaction networkHunter B Fraser
Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA
Science 296:750-2. 2002..We confirm one predicted outcome of this process-namely, that interacting proteins evolve at similar rates...- A simple dependence between protein evolution rate and the number of protein-protein interactionsHunter B Fraser
Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA
BMC Evol Biol 3:11. 2003..However, the generality of this observation has recently been challenged. Here we examine the problem using protein-protein interaction data from the yeast Saccharomyces cerevisiae and genome sequences from two other yeast species... - Modularity and evolutionary constraint on proteinsHunter B Fraser
Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
Nat Genet 37:351-2. 2005.... - Noise minimization in eukaryotic gene expressionHunter B Fraser
Department of Molecular and Cell Biology, University of California, Berkeley, USA
PLoS Biol 2:e137. 2004..Our results support the hypothesis that noise in gene expression is a biologically important variable, is generally detrimental to organismal fitness, and is subject to natural selection... - Evolutionary rate depends on number of protein-protein interactions independently of gene expression levelHunter B Fraser
Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA
BMC Evol Biol 4:13. 2004..A recent analysis suggested that the observed correlation between number of interactions and evolutionary rate may be due to experimental biases in high-throughput protein interaction data sets... - Coevolution of gene expression among interacting proteinsHunter B Fraser
Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA
Proc Natl Acad Sci U S A 101:9033-8. 2004..Our results also suggest that expression coevolution can be used for computational prediction of protein-protein interactions... - Conservation and evolution of cis-regulatory systems in ascomycete fungiAudrey P Gasch
Genome Sciences Department, Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
PLoS Biol 2:e398. 2004..Our results suggest that the DNA binding specificity of these proteins has coevolved with the sequences found upstream of the Rpn4p target genes and suggest that Rpn4p has a different function in N. crassa... - Aging and gene expression in the primate brainHunter B Fraser
Department of Molecular and Cell Biology, University of California, Berkeley, California, USA
PLoS Biol 3:e274. 2005.... - Confirmation of organized modularity in the yeast interactomeNicolas Bertin
PLoS Biol 5:e153. 2007 - Codon usage and selection on proteinsJoshua B Plotkin
Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
J Mol Evol 63:635-53. 2006..We also discuss several confounding factors, neglected by the Fisher-Wright model, that may limit the applicability of volatility in practice... - Coevolution, modularity and human diseaseHunter B Fraser
Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA
Curr Opin Genet Dev 16:637-44. 2006..Moreover, the combination of these two ideas might have implications for our understanding of many aspects of biology, ranging from the general architecture of living systems to the causes of various human diseases... - Assessing the determinants of evolutionary rates in the presence of noiseJoshua B Plotkin
Department of Biology, The University of Pennsylvania, USA
Mol Biol Evol 24:1113-21. 2007..More accurate measurements or more sophisticated statistical techniques will be required to determine which one, if any, of these factors dominates protein evolution... - Estimating selection pressures from limited comparative dataJoshua B Plotkin
Mol Biol Evol 23:1457-9. 2006..The regression technique does not depend on an underlying population-genetic mechanism. This new approach to estimating selection across a genome should be more powerful and more widely applicable than volatility itself... - Sum1p, the origin recognition complex, and the spreading of a promoter-specific repressor in Saccharomyces cerevisiaePatrick J Lynch
Department of Biochemistry and Institute for Genome Sciences and Policy, Duke University, 101 Science Drive, Box 3382, Durham, North Carolina 27708, USA
Mol Cell Biol 25:5920-32. 2005..Finally, this study uncovered a functional connection between wild-type Sum1p and the origin recognition complex, and this relationship also contributes to mutant Sum1-1p localization... - Functional genomic analysis of the rates of protein evolutionDennis P Wall
Department of Biological Sciences, and Stanford Genome Technology Center, Stanford University, Stanford, CA 94305, USA
Proc Natl Acad Sci U S A 102:5483-8. 2005.... - Adjusting for selection on synonymous sites in estimates of evolutionary distanceAaron E Hirsh
Department of Biological Sciences, Stanford University, Stanford, California, USA
Mol Biol Evol 22:174-7. 2005..Here, we use the relationship between codon bias and synonymous divergence observed in four species of the genus Saccharomyces to provide a simple correction for selection on silent sites... - Detecting selection using a single genome sequence of M. tuberculosis and P. falciparumJoshua B Plotkin
Harvard Society of Fellows and Bauer Center for Genomics Research, 7 Divinity Avenue, Cambridge, Massachusetts 02138, USA
Nature 428:942-5. 2004..Our method of estimating selective pressures requires far fewer data than comparative sequence analysis, and it measures selection across an entire genome; the method can readily be applied to a large range of sequenced organisms... - Using protein complexes to predict phenotypic effects of gene mutationHunter B Fraser
Broad Institute of Harvard and MIT, 320 Charles St, Cambridge, Massachhusetts 02142, USA
Genome Biol 8:R252. 2007..Predicting the phenotypic effects of mutations is a central goal of genetics research; it has important applications in elucidating how genotype determines phenotype and in identifying human disease genes...