ゲノム情報科学研究教育機構  アブストラクト
Date March 20, 2006
Speaker Assoc. Prof. Yoko Ishino, Graduate School of Science, Hiroshima University
Title Mass Spectrometry-based Expression Proteomics and Gene Annotation of Microbes
Abstract   Mass spectrometry (MS) combined with database searching has become the preferred method for identifying proteins present in cell or tissue samples.  The technique enables us to execute large-scale proteome analyses of microbes.  Searching MS data against protein databases composed of annotated genes deduced from the genome sequences has been widely conducted.  We used cyanobacterium Synechocystis sp. PCC6803 as a sample microbe for knowing the differential of proteins expressed under the different photoenvironment.  As a result, nearly 40% of the annotated genes in the CyanoBase (http://www.kazusa.or.jp/cyanobase/) were identified in total, and it was clarified that light intensity affected the protein expression levels of many genes.
  Although MS-based protein database search is generally powerful way for proteomics, there are some issues there: wrong annotations in the protein databases may cause deterioration in the accuracy of protein identification, and only proteins that have already been annotated can be identified.  We propose mapping MS data directly against genomic DNA sequences instead of protein databases.  This technique can provide primary experimental verification and correction of predicted coding sequences, together with the possibility of identifying novel genes.  This direct mapping to genomic DNA sequences is essentially a probabilistic analysis.  Searching genome sequence for several discriminative DNA patterns in gene control regions, such as Shine-Dalgarno sequence for prokaryote and TATA signal for eukaryote helps to decide whether or not to accept the mapping outcome.  We developed a computational system to meet the requirements; it should first conduct the probabilistic MS data mapping to genome sequence, then execute a DNA pattern search around the regions where the data were mapped, and lastly integrate the facts based on empirical knowledge in a simple knowledge base.  We finally found not a few annotation errors of N-terminus and several new candidate genes in several microbes.
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