Abstract:
:Selected reaction monitoring (SRM)-based proteomics approaches enable highly sensitive and reproducible assays for profiling of thousands of peptides in one experiment. The development of such assays involves the determination of retention time, detectability and fragmentation properties of peptides, followed by an optimal selection of transitions. If those properties have to be identified experimentally, the assay development becomes a time-consuming task. We introduce a computational framework for the optimal selection of transitions for a given set of proteins based on their sequence information alone or in conjunction with already existing transition databases. The presented method enables the rapid and fully automated initial development of assays for targeted proteomics. We introduce the relevant methods, report and discuss a step-wise and generic protocol and we also show that we can reach an ad hoc coverage of 80 % of the targeted proteins. The presented algorithmic procedure is implemented in the open-source software package OpenMS/TOPP.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Nahnsen S,Kohlbacher Odoi
10.1186/1471-2105-13-S16-S8subject
Has Abstractpub_date
2012-01-01 00:00:00pages
S8issn
1471-2105pii
1471-2105-13-S16-S8journal_volume
13 Suppl 16pub_type
杂志文章abstract:BACKGROUND:Recent technological advances in DNA sequencing and genotyping have led to the accumulation of a remarkable quantity of data on genetic polymorphisms. However, the development of new statistical and computational tools for effective processing of these data has not been equally as fast. In particular, Machin...
journal_title:BMC bioinformatics
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journal_title:BMC bioinformatics
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journal_title:BMC bioinformatics
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更新日期:2006-10-02 00:00:00
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journal_title:BMC bioinformatics
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journal_title:BMC bioinformatics
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abstract:BACKGROUND:Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions. RESULTS:Here, we weighted edges of a human protein interaction network and a transcriptional r...
journal_title:BMC bioinformatics
pub_type: 杂志文章
doi:10.1186/1471-2105-11-392
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journal_title:BMC bioinformatics
pub_type: 杂志文章
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journal_title:BMC bioinformatics
pub_type: 杂志文章
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更新日期:2018-12-22 00:00:00
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journal_title:BMC bioinformatics
pub_type: 杂志文章
doi:10.1186/1471-2105-13-260
更新日期:2012-10-09 00:00:00
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journal_title:BMC bioinformatics
pub_type: 杂志文章
doi:10.1186/1471-2105-9-S9-S6
更新日期:2008-08-12 00:00:00
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journal_title:BMC bioinformatics
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journal_title:BMC bioinformatics
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journal_title:BMC bioinformatics
pub_type: 杂志文章
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journal_title:BMC bioinformatics
pub_type: 杂志文章
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更新日期:2006-03-08 00:00:00
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journal_title:BMC bioinformatics
pub_type: 杂志文章
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journal_title:BMC bioinformatics
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