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A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics
- Source :
- Scientific Data, SCIENTIFIC DATA
- Publication Year :
- 2021
- Publisher :
- Cold Spring Harbor Laboratory, 2021.
-
Abstract
- In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition. Here, we present a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset acquired using several of the most commonly used current day instrumental platforms. The dataset consists of over 700 LC-MS runs, including adequate replicates allowing robust statistics and covering over nearly 10 different data formats, including scanning quadrupole and ion mobility enabled acquisitions. Datasets are available via ProteomeXchange (PXD028735).
- Subjects :
- Proteomics
Statistics and Probability
Proteome
Panorama
Computer science
Robust statistics
Library and Information Sciences
computer.software_genre
Mass Spectrometry
Education
Data acquisition
Medicine and Health Sciences
Animals
Humans
Computer. Automation
Skyline
Statistics
Biology and Life Sciences
Computer Science Applications
Benchmarking
Benchmark (computing)
Probability and Uncertainty
Data mining
Statistics, Probability and Uncertainty
Engineering sciences. Technology
computer
Chromatography, Liquid
Information Systems
Subjects
Details
- ISSN :
- 20524463
- Database :
- OpenAIRE
- Journal :
- Scientific Data, SCIENTIFIC DATA
- Accession number :
- edsair.doi.dedup.....3625dab82d9b6fd7d919c0bb78e5a255
- Full Text :
- https://doi.org/10.1101/2021.11.24.469852