Computationally driven, quantitative experiments discover genes required for mitochondrial biogenesis. Export

@article{citeulike:4678387, abstract = {Mitochondria are the proverbial powerhouses of the cell, running the fundamental biochemical processes that produce energy from nutrients using oxygen. These processes are conserved in all eukaryotes, from humans to model organisms such as baker's yeast. In humans, mitochondrial dysfunction plays a role in a variety of diseases, including diabetes, neuromuscular disorders, and aging. In order to better understand fundamental mitochondrial biology, we studied genes involved in mitochondrial biogenesis in the yeast <italic>S. cerevisiae</italic>, discovering over 100 proteins with novel roles in this process. These experiments assigned function to 5\% of the genes whose function was not known. In order to achieve this rapid rate of discovery, we developed a system incorporating highly quantitative experimental assays and an integrated, iterative process of computational protein function prediction. Beginning from relatively little prior knowledge, we found that computational predictions achieved about 60\% accuracy and rapidly guided our laboratory work towards hundreds of promising candidate genes. Thus, in addition to providing a more thorough understanding of mitochondrial biology, this study establishes a framework for successfully integrating computation and experimentation to drive biological discovery. A companion manuscript, published in <italic>PLoS Computational Biology</italic> (doi:10.1371/journal.pcbi.1000322), discusses observations and conclusions important for the computational community.}, author = {Hess, David C. and Myers, Chad L. and Huttenhower, Curtis and Hibbs, Matthew A. and Hayes, Alicia P. and Paw, Jadine and Clore, John J. and Mendoza, Rosa M. and Luis, Bryan S. and Nislow, Corey and Giaever, Guri and Costanzo, Michael and Troyanskaya, Olga G. and Caudy, Amy A.}, citeulike-article-id = {4678387}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pgen.1000407}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19300474}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19300474}, day = {20}, doi = {10.1371/journal.pgen.1000407}, issn = {1553-7404}, journal = {PLoS genetics}, keywords = {analysis, integration, litrev, mitochondria, morphology, parts-list, prediction, ym2008}, month = {March}, number = {3}, pages = {e1000407+}, posted-at = {2009-05-29 14:48:17}, priority = {5}, publisher = {Public Library of Science}, title = {Computationally driven, quantitative experiments discover genes required for mitochondrial biogenesis.}, url = {http://dx.doi.org/10.1371/journal.pgen.1000407}, volume = {5}, year = {2009} }

TY - JOUR ID - citeulike:4678387 L3 - citeulike-article-id:4678387 N2 - Mitochondria are the proverbial powerhouses of the cell, running the fundamental biochemical processes that produce energy from nutrients using oxygen. These processes are conserved in all eukaryotes, from humans to model organisms such as baker's yeast. In humans, mitochondrial dysfunction plays a role in a variety of diseases, including diabetes, neuromuscular disorders, and aging. In order to better understand fundamental mitochondrial biology, we studied genes involved in mitochondrial biogenesis in the yeast <italic>S. cerevisiae</italic>, discovering over 100 proteins with novel roles in this process. These experiments assigned function to 5% of the genes whose function was not known. In order to achieve this rapid rate of discovery, we developed a system incorporating highly quantitative experimental assays and an integrated, iterative process of computational protein function prediction. Beginning from relatively little prior knowledge, we found that computational predictions achieved about 60% accuracy and rapidly guided our laboratory work towards hundreds of promising candidate genes. Thus, in addition to providing a more thorough understanding of mitochondrial biology, this study establishes a framework for successfully integrating computation and experimentation to drive biological discovery. A companion manuscript, published in <italic>PLoS Computational Biology</italic> (doi:10.1371/journal.pcbi.1000322), discusses observations and conclusions important for the computational community. IS - 3 JF - PLoS genetics SN - 1553-7404 TI - Computationally driven, quantitative experiments discover genes required for mitochondrial biogenesis. PB - Public Library of Science VL - 5 SP - e1000407 KW - analysis KW - integration KW - litrev KW - mitochondria KW - morphology KW - parts-list KW - prediction KW - ym2008 AU - Hess, David AU - Myers, Chad AU - Huttenhower, Curtis AU - Hibbs, Matthew AU - Hayes, Alicia AU - Paw, Jadine AU - Clore, John AU - Mendoza, Rosa AU - Luis, Bryan AU - Nislow, Corey AU - Giaever, Guri AU - Costanzo, Michael AU - Troyanskaya, Olga AU - Caudy, Amy PY - 2009/03/20/ UR - http://dx.doi.org/10.1371/journal.pgen.1000407 ER -