DIAproteomics: A Multifunctional Data Analysis Pipeline for Data-Independent Acquisition Proteomics and Peptidomics
| Autor: | Leon Bichmann, George Rosenberger, Phil Ewels, Hannes L. Röst, Timo Sachsenberg, Oliver Kohlbacher, Leon Kuchenbecker, Shubham Gupta, Julianus Pfeuffer, Oliver Alka |
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| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Data Analysis Proteomics Computer science Cloud computing computer.software_genre Biochemistry Mass Spectrometry 03 medical and health sciences Software data-independent acquisition Data-independent acquisition automation Data processing 030102 biochemistry & molecular biology business.industry cloud computing peptidomics Reproducibility of Results General Chemistry 500 Naturwissenschaften und Mathematik::570 Biowissenschaften Biologie::570 Biowissenschaften Biologie Pipeline (software) Automation 030104 developmental biology spectral library generation Pairwise comparison Data mining business computer Workflow management system data processing |
| Zdroj: | Journal of proteome research. 20(7) |
| ISSN: | 1535-3907 |
| Popis: | Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. The main advantages include greater reproducibility and sensitivity and a greater dynamic range compared with data-dependent acquisition (DDA). However, the data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics, a multifunctional, automated, high-throughput pipeline implemented in the Nextflow workflow management system that allows one to easily process proteomics and peptidomics DIA data sets on diverse compute infrastructures. The central components are well-established tools such as the OpenSwathWorkflow for the DIA spectral library search and PyProphet for the false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and to carry out the retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from the statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is well documented open-source software and is available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/. |
| Databáze: | OpenAIRE |
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