Difference between revisions of "Research"
(→Bioinformatics of protein function and interactions) |
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We've discovered a number of features of genomes that allow us to predict functions for proteins that have never been experimentally characterized. Using these techniques and information from over 30 fully sequenced genomes, we were able to calculate the first genome-wide predictions of protein function, finding very preliminary function for over half the 2,500 uncharacterized genes of yeast. Now, with hundreds of genomes in hand, we're extending these techniques, as well as asking fundamental questions about the evolution of protein interactions and the evolution of genomes. | We've discovered a number of features of genomes that allow us to predict functions for proteins that have never been experimentally characterized. Using these techniques and information from over 30 fully sequenced genomes, we were able to calculate the first genome-wide predictions of protein function, finding very preliminary function for over half the 2,500 uncharacterized genes of yeast. Now, with hundreds of genomes in hand, we're extending these techniques, as well as asking fundamental questions about the evolution of protein interactions and the evolution of genomes. | ||
| + | |||
| + | ''Some of our recent papers on gene networks and the systematic discovery of gene function include:''<br> | ||
| + | {{Paper | ||
| + | |title=A critical assessment of ''Mus musculus'' gene function prediction using integrated genomic evidence | ||
| + | |authors=Peña-Castillo ''et al.'' | ||
| + | |journal=Genome Biology | ||
| + | |pub_year=2008 | ||
| + | |volume=9 Suppl 1 | ||
| + | |page=S2 | ||
| + | |pubmed=18613946 | ||
| + | |link=http://genomebiology.com/2008/9/S1/S2 | ||
| + | |comment= | ||
| + | }} | ||
| + | |||
| + | {{Paper | ||
| + | |title=A single gene network accurately predicts phenotypic effects of gene perturbation in ''Caenorhabditis elegans'' | ||
| + | |authors=Lee I, Lehner B, Crombie C, Wong W, Fraser AG, Marcotte EM | ||
| + | |journal=Nature Genetics | ||
| + | |pub_year=2008 | ||
| + | |volume=40(2) | ||
| + | |page=181-8 | ||
| + | |pubmed=18223650 | ||
| + | |link=http://www.nature.com/ng/journal/v40/n2/abs/ng.2007.70.html | ||
| + | |comment= | ||
| + | }} | ||
| + | |||
| + | {{Paper | ||
| + | |title=Broad network-based predictability of ''Saccharomyces cerevisiae'' gene loss-of-function phenotypes | ||
| + | |authors=McGary KL, Lee I, Marcotte EM | ||
| + | |journal=Genome Biology | ||
| + | |pub_year=2007 | ||
| + | |volume=8(12) | ||
| + | |page=R258. | ||
| + | |pubmed=18053250 | ||
| + | |link=http://genomebiology.com/2007/8/12/R258 | ||
| + | |comment= | ||
| + | }} | ||
| + | |||
| + | {{Paper | ||
| + | |title=A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality | ||
| + | |authors=Hart GT, Lee I, Marcotte EM | ||
| + | |journal=BMC Bioinformatics | ||
| + | |pub_year=2007 | ||
| + | |volume=8 | ||
| + | |page=236. | ||
| + | |pubmed=17605818 | ||
| + | |link=http://www.biomedcentral.com/1471-2105/8/236 | ||
| + | |comment= | ||
| + | }} | ||
| + | |||
| + | {{Paper | ||
| + | |title=A Probabilistic functional network of yeast genes | ||
| + | |authors=Lee I, Date SV, Adai AT, Marcotte EM | ||
| + | |journal=Science | ||
| + | |pub_year=2004 | ||
| + | |volume=306(5701) | ||
| + | |page=1555-8. | ||
| + | |pubmed=15567862 | ||
| + | |link=http://www.sciencemag.org/cgi/content/full/306/5701/1555 | ||
| + | |comment= | ||
| + | }} | ||
| + | |||
| + | {{Paper | ||
| + | |title=A probabilistic view of gene function | ||
| + | |authors=Fraser AG, Marcotte EM | ||
| + | |journal=Nature Genetics | ||
| + | |pub_year=2004 | ||
| + | |volume=36(6) | ||
| + | |page=559-64 | ||
| + | |pubmed=15167932 | ||
| + | |link=http://www.nature.com/ng/journal/v36/n6/abs/ng1370.html | ||
| + | |comment= | ||
| + | }} | ||
| + | |||
Link to our large-scale gene networks for yeast, worms, mouse: http://www.functionalnet.org | Link to our large-scale gene networks for yeast, worms, mouse: http://www.functionalnet.org | ||
Revision as of 16:18, 28 July 2009
Our group studies the large-scale organization of proteins, essentially trying to reconstruct the 'wiring diagrams' of cells by learning how all of the proteins encoded by a genome are associated into functional pathways, systems, and networks. We are interested both in discovering the functions of the proteins as well as in learning the underlying organizational principles of the networks. The work is evenly split between computational and experimental approaches, with the latter tending to be high-throughput functional genomics and proteomics approaches for studying thousands of genes/proteins in parallel.
Bioinformatics of protein function and interactions
We've discovered a number of features of genomes that allow us to predict functions for proteins that have never been experimentally characterized. Using these techniques and information from over 30 fully sequenced genomes, we were able to calculate the first genome-wide predictions of protein function, finding very preliminary function for over half the 2,500 uncharacterized genes of yeast. Now, with hundreds of genomes in hand, we're extending these techniques, as well as asking fundamental questions about the evolution of protein interactions and the evolution of genomes.
Some of our recent papers on gene networks and the systematic discovery of gene function include:
Peña-Castillo et al., A critical assessment of Mus musculus gene function prediction using integrated genomic evidence, Genome Biology, 9 Suppl 1:S2 (2008) PubMed Link
Lee I, Lehner B, Crombie C, Wong W, Fraser AG, Marcotte EM, A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans, Nature Genetics, 40(2):181-8 (2008) PubMed Link
McGary KL, Lee I, Marcotte EM, Broad network-based predictability of Saccharomyces cerevisiae gene loss-of-function phenotypes, Genome Biology, 8(12):R258. (2007) PubMed Link
Hart GT, Lee I, Marcotte EM, A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality, BMC Bioinformatics, 8:236. (2007) PubMed Link
Lee I, Date SV, Adai AT, Marcotte EM, A Probabilistic functional network of yeast genes, Science, 306(5701):1555-8. (2004) PubMed Link
Fraser AG, Marcotte EM, A probabilistic view of gene function, Nature Genetics, 36(6):559-64 (2004) PubMed Link
Link to our large-scale gene networks for yeast, worms, mouse: http://www.functionalnet.org
Link to some of our public bioinformatics resources: http://bioinformatics.icmb.utexas.edu
Proteomics: High-throughput protein expression and interaction profiling
From our work and others, it is apparent that proteins in the cell participate in extended protein interaction networks involving thousands of proteins. By defining these networks, we can not only discover the functions of specific proteins based on their connections, but also use these networks as tools to predict the outcome of perturbing the cell. As part of our research efforts in this area, we have been developing high-throughput methods to measure protein abundances in complex biological samples (e.g., by quantitative shotgun proteomics mass spectrometry) and protein localization with cells (e.g., by high-throughput automated fluorescence microcopy, such as of cell microarrays). These sorts of data help us build a catalog of protein, mRNA and metabolite expression from cells grown under many different conditions, forming a quantitative picture of these molecular events inside cells. We expect that data of these sorts will put us on the road to developing predictive, rather than merely descriptive, theories of biology.
Recent papers in this area include:
Narayanaswamy et al., Widespread reorganization of metabolic enzymes into reversible assemblies upon nutrient starvation, Proc Natl Acad Sci U S A, 106(25):10147-52 (2009) PubMed Link
Vogel C, Marcotte EM, Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data, Nature Protocols, 3(9):1444-51. (2008) PubMed Link
Ramani AK, Li Z, Hart GT, Carlson MW, Boutz DR, Marcotte EM, A map of human protein interactions derived from co-expression of human mRNAs and their orthologs, Molecular Systems Biology, 4:180 (2008) PubMed Link
Lu P, Vogel C, Wang R, Yao X, Marcotte EM, Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation, Nature Biotechnology, 25(1):117-24 (2007) PubMed Link
Link to our MS/MS data repository: http://www.marcottelab.org/MSdata/
