DDASSQ: An open-source, multiple peptide sequencing strategy for label free quantification based on an OpenMS pipeline in the KNIME analytics platform.
LFQ
proteomics, search engine, workflow
Journal
Proteomics
ISSN: 1615-9861
Titre abrégé: Proteomics
Pays: Germany
ID NLM: 101092707
Informations de publication
Date de publication:
08 2021
08 2021
Historique:
revised:
08
07
2021
received:
21
12
2020
accepted:
12
07
2021
pubmed:
28
7
2021
medline:
26
10
2021
entrez:
27
7
2021
Statut:
ppublish
Résumé
In this study we investigated the performance of a computational pipeline for protein identification and label free quantification (LFQ) of LC-MS/MS data sets from experimental animal tissue samples, as well as the impact of its specific peptide search combinatorial approach. The full pipeline workflow was composed of peptide search engine adapters based on different identification algorithms, in the frame of the open-source OpenMS software running within the KNIME analytics platform. Two different in silico tryptic digestion, database-search assisted approaches (X!Tandem and MS-GF+), de novo peptide sequencing based on Novor and consensus library search (SpectraST), were tested for the processing of LC-MS/MS raw data files obtained from proteomic LC-MS experiments done on proteolytic extracts from mouse ex vivo liver samples. The results from proteomic LFQ were compared to those based on the application of the two software tools MaxQuant and Proteome Discoverer for protein inference and label-free data analysis in shotgun proteomics. Data are available via ProteomeXchange with identifier PXD025097.
Identifiants
pubmed: 34312990
doi: 10.1002/pmic.202000319
pmc: PMC8459258
doi:
Substances chimiques
Peptides
0
Proteome
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2000319Informations de copyright
© 2021 The Authors. Proteomics published by Wiley-VCH GmbH.
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