Mass spectrometric genomic data mining:: Novel insights into bioenergetic pathways in Chlamydomonas reinhardtii

被引:48
作者
Allmer, Jens [1 ]
Naumann, Bianca [1 ]
Markert, Christine [1 ]
Zhang, Monica [1 ]
Hippler, Michael [1 ]
机构
[1] Univ Penn, Dept Biol, Inst Plant Sci, Philadelphia, PA 19104 USA
关键词
Chlamydomonas reinhardtii; error-tolerant search; genomic data mining; mass spectrometry;
D O I
10.1002/pmic.200600208
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
A new high-throughput computational strategy was established that improves genomic data mining from MS experiments. The MS/MS data were analyzed by the SEQUEST search algorithm and a combination of de novo amino acid sequencing in conjunction with an error-tolerant database search tool, operating on a 256 processor computer cluster. The error-tolerant search tool, previously established as GenomicPeptideFinder (GPF), enables detection of intron-split and/or alternatively spliced peptides from MS/MS data when deduced from genomic DNA. Isolated thylakoid membranes from the eukaryotic green alga Chlamydomonas reinhardtii were separated by 1-D SDS gel electrophoresis, protein bands were excised from the gel, digested in gel with trypsin and analyzed by coupling nano-flow LC with MS/MS. The concerted action of SEQUEST and GPF allowed identification of 2622 distinct peptides. In total 448 peptides were identified by GPF analysis alone, including 98 intron-split peptides, resulting in the identification of novel proteins, improved annotation of gene models, and evidence of alternative splicing.
引用
收藏
页码:6207 / 6220
页数:14
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