Efficient visualization of high-throughput targeted proteomics experiments: TAPIR

Motivation: Targeted mass spectrometry comprises a set of powerful methods to obtain accurate and consistent protein quantification in complex samples. To fully exploit these techniques, a cross-platform and open-source software stack based on standardized data exchange formats is required. Results:...

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Vydané v:Bioinformatics Ročník 31; číslo 14; s. 2415 - 2417
Hlavní autori: Röst, Hannes L., Rosenberger, George, Aebersold, Ruedi, Malmström, Lars
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England 15.07.2015
Predmet:
Bioinformatics
Computer Graphics
Computer programs
Format
Licenses
Mass Spectrometry
Proteins
Proteomics
Proteomics - methods
Software
Visualization
ISSN:1367-4803, 1367-4811, 1460-2059
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Shrnutí:Motivation: Targeted mass spectrometry comprises a set of powerful methods to obtain accurate and consistent protein quantification in complex samples. To fully exploit these techniques, a cross-platform and open-source software stack based on standardized data exchange formats is required. Results: We present TAPIR, a fast and efficient Python visualization software for chromatograms and peaks identified in targeted proteomics experiments. The input formats are open, community-driven standardized data formats (mzML for raw data storage and TraML encoding the hierarchical relationships between transitions, peptides and proteins). TAPIR is scalable to proteome-wide targeted proteomics studies (as enabled by SWATH-MS), allowing researchers to visualize high-throughput datasets. The framework integrates well with existing automated analysis pipelines and can be extended beyond targeted proteomics to other types of analyses. Availability and implementation: TAPIR is available for all computing platforms under the 3-clause BSD license at https://github.com/msproteomicstools/msproteomicstools. Contact:  lars@imsb.biol.ethz.ch Supplementary information:  Supplementary data are available at Bioinformatics online.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1367-4803
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btv152