In this study we investigated the performance of a computational pipeline for protein identification and label free quantification 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 datafiles 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 DiscovererTM for protein inference and label-free data analysis in shotgun proteomics. Data are available via ProteomeXchange with identifier PXD025097.

DDASSQ: an open‐source, multiple peptide sequencing strategy for label free quantification based on an OpenMS pipeline in the KNIME analytics platform / M. Svecla, G. Garrone, F. Fare', G. Aletti, G.D. Norata, G. Beretta. - In: PROTEOMICS. - ISSN 1615-9853. - 21:16(2021 Aug), pp. e2000319.1-e2000319.12. [10.1002/pmic.202000319]

DDASSQ: an open‐source, multiple peptide sequencing strategy for label free quantification based on an OpenMS pipeline in the KNIME analytics platform

Monika Svecla;Giulia Garrone;Fiorenza Faré;Giacomo Aletti;Giuseppe Danilo Norata;Giangiacomo Beretta
2021-08

Abstract

In this study we investigated the performance of a computational pipeline for protein identification and label free quantification 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 datafiles 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 DiscovererTM for protein inference and label-free data analysis in shotgun proteomics. Data are available via ProteomeXchange with identifier PXD025097.
LFQ; proteomics; search engine; workflow
Settore CHIM/08 - Chimica Farmaceutica
Settore BIO/14 - Farmacologia
Settore CHIM/01 - Chimica Analitica
27-lug-2021
PROTEOMICS
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/860241
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