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In silico design of targeted SRM-based experiments

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...

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Detalles Bibliográficos
Autores principales: Nahnsen, Sven, Kohlbacher, Oliver
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3489541/
https://www.ncbi.nlm.nih.gov/pubmed/23176520
http://dx.doi.org/10.1186/1471-2105-13-S16-S8
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author Nahnsen, Sven
Kohlbacher, Oliver
author_facet Nahnsen, Sven
Kohlbacher, Oliver
author_sort Nahnsen, Sven
collection PubMed
description 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.
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spelling pubmed-34895412012-11-08 In silico design of targeted SRM-based experiments Nahnsen, Sven Kohlbacher, Oliver BMC Bioinformatics Review 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. BioMed Central 2012-11-05 /pmc/articles/PMC3489541/ /pubmed/23176520 http://dx.doi.org/10.1186/1471-2105-13-S16-S8 Text en Copyright ©2012 Nahnsen and Kohlbacher; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Nahnsen, Sven
Kohlbacher, Oliver
In silico design of targeted SRM-based experiments
title In silico design of targeted SRM-based experiments
title_full In silico design of targeted SRM-based experiments
title_fullStr In silico design of targeted SRM-based experiments
title_full_unstemmed In silico design of targeted SRM-based experiments
title_short In silico design of targeted SRM-based experiments
title_sort in silico design of targeted srm-based experiments
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3489541/
https://www.ncbi.nlm.nih.gov/pubmed/23176520
http://dx.doi.org/10.1186/1471-2105-13-S16-S8
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