ArticleProteomics2021
DDASSQ: An open-source, multiple peptide sequencing strategy for label free quantification based on an OpenMS pipeline in the KNIME analytics platform.
Article in Proteomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- ASGR1 deficiency improves atherosclerosis but alters liver metabolism in ApoECardiovascular diabetology · 2024Article
- Identification of regulatory networks and crosstalk factors in brown adipose tissue and liver of a cold-exposed cardiometabolic mouse model.Cardiovascular diabetology · 2024Article
- Regional Differences in the Small Intestinal Proteome of Control Mice and of Mice Lacking Lysosomal Acid Lipase.Journal of proteome research · 2024Article
- ASGR1 deficiency diverts lipids toward adipose tissue but results in liver damage during obesity.Cardiovascular diabetology · 2024Article
- Genetic deletion of MMP12 ameliorates cardiometabolic disease by improving insulin sensitivity, systemic inflammation, and atherosclerotic features in mice.Cardiovascular diabetology · 2023Article
- Hepatic SREBP signaling requires SPRING to govern systemic lipid metabolism in mice and humans.Nature communications · 2023Article
- Mannose Receptor Deficiency Impacts Bone Marrow and Circulating Immune Cells during High Fat Diet Induced Obesity.Metabolites · 2022Article
- The low-density lipoprotein receptor-mTORC1 axis coordinates CD8+ T cell activation.The Journal of cell biology · 2022Article
- DDASSQ: An open-source, multiple peptide sequencing strategy for label free quantification based on an OpenMS pipeline in the KNIME analytics platform.Proteomics · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.