Gast ::
| Hauptseite > Publikationsdatenbank > Scientific Workflow Optimization for Improved Peptide and Protein Identification > print |
| Hauptseite > Publikationsdatenbank > Scientific Workflow Optimization for Improved Peptide and Protein Identification > print |
| 001 | 280903 | ||
| 005 | 20210129221437.0 | ||
| 024 | 7 | _ | |a 10.1186/s12859-015-0714-x |2 doi |
| 024 | 7 | _ | |a 2128/9723 |2 Handle |
| 024 | 7 | _ | |a WOS:000360426000008 |2 WOS |
| 024 | 7 | _ | |a altmetric:4467684 |2 altmetric |
| 024 | 7 | _ | |a pmid:26335531 |2 pmid |
| 037 | _ | _ | |a FZJ-2016-00614 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 004 |
| 100 | 1 | _ | |a Holl, Sonja |0 P:(DE-Juel1)132139 |b 0 |u fzj |
| 245 | _ | _ | |a Scientific Workflow Optimization for Improved Peptide and Protein Identification |
| 260 | _ | _ | |a London |c 2015 |b BioMed Central |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1453206659_25282 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a article |2 DRIVER |
| 520 | _ | _ | |a Background: Peptide-spectrum matching is a common step in most data processing workflows for massspectrometry-based proteomics. Many algorithms and software packages, both free and commercial, have beendeveloped to address this task. However, these algorithms typically require the user to select instrument- andsample-dependent parameters, such as mass measurement error tolerances and number of missed enzymaticcleavages. In order to select the best algorithm and parameter set for a particular dataset, in-depth knowledgeabout the data as well as the algorithms themselves is needed. Most researchers therefore tend to use defaultparameters, which are not necessarily optimal.Results: We have applied a new optimization framework for the Taverna scientific workflow management system(http://ms-utils.org/Taverna_Optimization.pdf) to find the best combination of parameters for a given scientificworkflow to perform peptide-spectrum matching. The optimizations themselves are non-trivial, as demonstrated byseveral phenomena that can be observed when allowing for larger mass measurement errors in sequence databasesearches. On-the-fly parameter optimization embedded in scientific workflow management systems enables expertsand non-experts alike to extract the maximum amount of information from the data. The same workflows could beused for exploring the parameter space and compare algorithms, not only for peptide-spectrum matching, but alsofor other tasks, such as retention time prediction.Conclusion: Using the optimization framework, we were able to learn about how the data was acquired as well asthe explored algorithms. We observed a phenomenon identifying many ammonia-loss b-ion spectra as peptideswith N-terminal pyroglutamate and a large precursor mass measurement error. These insights could only be gainedwith the extension of the common range for the mass measurement error tolerance parameters explored by theoptimization framework. |
| 536 | _ | _ | |a 511 - Computational Science and Mathematical Methods (POF3-511) |0 G:(DE-HGF)POF3-511 |c POF3-511 |f POF III |x 0 |
| 536 | _ | _ | |a 512 - Data-Intensive Science and Federated Computing (POF3-512) |0 G:(DE-HGF)POF3-512 |c POF3-512 |f POF III |x 1 |
| 700 | 1 | _ | |a Mohammed, Yassene |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Zimmermann, Olav |0 P:(DE-Juel1)132307 |b 2 |u fzj |
| 700 | 1 | _ | |a Palmblad, Magnus |0 P:(DE-HGF)0 |b 3 |e Corresponding author |
| 773 | _ | _ | |a 10.1186/s12859-015-0714-x |0 PERI:(DE-600)2041484-5 |p 284 |t BMC bioinformatics |v 16 |y 2015 |x 1471-2105 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/280903/files/art%253A10.1186%252Fs12859-015-0714-x.pdf |
| 856 | 4 | _ | |y OpenAccess |x icon |u https://juser.fz-juelich.de/record/280903/files/art%253A10.1186%252Fs12859-015-0714-x.gif?subformat=icon |
| 856 | 4 | _ | |y OpenAccess |x icon-1440 |u https://juser.fz-juelich.de/record/280903/files/art%253A10.1186%252Fs12859-015-0714-x.jpg?subformat=icon-1440 |
| 856 | 4 | _ | |y OpenAccess |x icon-180 |u https://juser.fz-juelich.de/record/280903/files/art%253A10.1186%252Fs12859-015-0714-x.jpg?subformat=icon-180 |
| 856 | 4 | _ | |y OpenAccess |x icon-640 |u https://juser.fz-juelich.de/record/280903/files/art%253A10.1186%252Fs12859-015-0714-x.jpg?subformat=icon-640 |
| 856 | 4 | _ | |y OpenAccess |x pdfa |u https://juser.fz-juelich.de/record/280903/files/art%253A10.1186%252Fs12859-015-0714-x.pdf?subformat=pdfa |
| 909 | C | O | |o oai:juser.fz-juelich.de:280903 |p openaire |p open_access |p driver |p VDB |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich GmbH |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)132139 |
| 910 | 1 | _ | |a Forschungszentrum Jülich GmbH |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)132307 |
| 910 | 1 | _ | |a External Institute |0 I:(DE-HGF)0 |k Extern |b 3 |6 P:(DE-HGF)0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-511 |2 G:(DE-HGF)POF3-500 |v Computational Science and Mathematical Methods |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |l Supercomputing & Big Data |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-512 |2 G:(DE-HGF)POF3-500 |v Data-Intensive Science and Federated Computing |x 1 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |l Supercomputing & Big Data |
| 914 | 1 | _ | |y 2015 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b BMC BIOINFORMATICS : 2014 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0310 |2 StatID |b NCBI Molecular Biology Database |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Thomson Reuters Master Journal List |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | 1 | _ | |a UNRESTRICTED |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|