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Simple, efficient and thorough shotgun proteomic analysis with PatternLab V.
- Source :
-
Nature protocols [Nat Protoc] 2022 Jul; Vol. 17 (7), pp. 1553-1578. Date of Electronic Publication: 2022 Apr 11. - Publication Year :
- 2022
-
Abstract
- Shotgun proteomics aims to identify and quantify the thousands of proteins in complex mixtures such as cell and tissue lysates and biological fluids. This approach uses liquid chromatography coupled with tandem mass spectrometry and typically generates hundreds of thousands of mass spectra that require specialized computational environments for data analysis. PatternLab for proteomics is a unified computational environment for analyzing shotgun proteomic data. PatternLab V (PLV) is the most comprehensive and crucial update so far, the result of intensive interaction with the proteomics community over several years. All PLV modules have been optimized and its graphical user interface has been completely updated for improved user experience. Major improvements were made to all aspects of the software, ranging from boosting the number of protein identifications to faster extraction of ion chromatograms. PLV provides modules for preparing sequence databases, protein identification, statistical filtering and in-depth result browsing for both labeled and label-free quantitation. The PepExplorer module can even pinpoint de novo sequenced peptides not already present in the database. PLV is of broad applicability and therefore suitable for challenging experimental setups, such as time-course experiments and data handling from unsequenced organisms. PLV interfaces with widely adopted software and community initiatives, e.g., Comet, Skyline, PEAKS and PRIDE. It is freely available at http://www.patternlabforproteomics.org .<br /> (© 2022. Springer Nature Limited.)
Details
- Language :
- English
- ISSN :
- 1750-2799
- Volume :
- 17
- Issue :
- 7
- Database :
- MEDLINE
- Journal :
- Nature protocols
- Publication Type :
- Academic Journal
- Accession number :
- 35411045
- Full Text :
- https://doi.org/10.1038/s41596-022-00690-x