Back to Search Start Over

Simple, efficient and thorough shotgun proteomic analysis with PatternLab V.

Authors :
Santos MDM
Lima DB
Fischer JSG
Clasen MA
Kurt LU
Camillo-Andrade AC
Monteiro LC
de Aquino PF
Neves-Ferreira AGC
Valente RH
Trugilho MRO
Brunoro GVF
Souza TACB
Santos RM
Batista M
Gozzo FC
Durán R
Yates JR 3rd
Barbosa VC
Carvalho PC
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