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      A Genome-Wide Gene Function Prediction Resource for Drosophila melanogaster

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          Abstract

          Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae.

            Two large-scale yeast two-hybrid screens were undertaken to identify protein-protein interactions between full-length open reading frames predicted from the Saccharomyces cerevisiae genome sequence. In one approach, we constructed a protein array of about 6,000 yeast transformants, with each transformant expressing one of the open reading frames as a fusion to an activation domain. This array was screened by a simple and automated procedure for 192 yeast proteins, with positive responses identified by their positions in the array. In a second approach, we pooled cells expressing one of about 6,000 activation domain fusions to generate a library. We used a high-throughput screening procedure to screen nearly all of the 6,000 predicted yeast proteins, expressed as Gal4 DNA-binding domain fusion proteins, against the library, and characterized positives by sequence analysis. These approaches resulted in the detection of 957 putative interactions involving 1,004 S. cerevisiae proteins. These data reveal interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes. The results of these screens are shown here.
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              Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.

              The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2010
                12 August 2010
                : 5
                : 8
                : e12139
                Affiliations
                [1 ]Department of Cancer Biology, Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
                [2 ]Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
                [3 ]Howard Hughes Medical Institute, Boston, Massachusetts, United States of America
                [4 ]Applied Physics Program, Division of Engineering and Applied Sciences, Graduate School of Arts and Sciences, Harvard University, Cambridge, Massachusetts, United States of America
                [5 ]Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, United States of America
                University of Toronto, Canada
                Author notes

                Conceived and designed the experiments: HY KV FR. Performed the experiments: HY. Analyzed the data: HY KV NP FR MV. Contributed reagents/materials/analysis tools: HY JEB NK MAY TH DEH MEC NP FR MV. Wrote the paper: HY KV DEH MEC FR MV.

                [¤a]

                Current address: Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, United States of America

                [¤b]

                Current address: Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, United States of America

                [¤c]

                Current address: Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America

                Article
                10-PONE-RA-15488R2
                10.1371/journal.pone.0012139
                2920829
                20711346
                b22a643c-9b92-45bb-9ae2-26e32778e433
                Yan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 12 January 2010
                : 14 July 2010
                Page count
                Pages: 11
                Categories
                Research Article
                Computational Biology/Genomics
                Computational Biology/Systems Biology
                Genetics and Genomics/Functional Genomics

                Uncategorized
                Uncategorized

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