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采用生物信息學(xué)和實(shí)驗(yàn)方法聯(lián)合分析轉(zhuǎn)錄組信息
點(diǎn)擊次數(shù)：1067 發(fā)布時(shí)間：2008-12-1
High-throughput protein analysis integrating bioinformaticsexperimental assays.
Nucleic Acids Res. 2004 Feb 3;32(2):742-8
The wealth of transcript information that has been made publicly available in recent years requires the development of high-throughput functional genomicsproteomics approaches for its analysis. Such approaches need suitable data integration proceduresa high level of automation in order to gain maximum benefit from the results generated. We have designed an automatic pipeline to analyse annotated open reading frames (ORFs) stemming from full-length cDNAs produced mainly by the German cDNA Consortium. The ORFs are cloned into expression vectors for use in large-scale assays such as the determination of subcellular protein localization or kinase reaction specificity. Additionally, all identified ORFs undergo exhaustive bioinformatic analysis such as similarity searches, protein domain architecture determinationprediction of physicochemical characteristicssecondary structure, using a wide variety of bioinformatic methods in combination with the most up-to-date public databases (e.g. PRINTS, BLOCKS, INTERPRO, PROSITE SWISSPROT). Data from experimental resultsfrom the bioinformatic analysis are integratedstored in a relational database (MS SQL-Server), which makes it possible for researchers to find answers to biological questions easily, thereby speeding up the selection of targets for further analysis. The designed pipeline constitutes a new automatic approach to obtainingadministrating relevant biological data from high-throughput investigations of cDNAs in order to systematically identifycharacterize novel genes, as well as to comprehensively describe the function of the encoded proteins.