Guiding the choice of informatics software and tools for lipidomics research applications
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, přehledy, Research Support, N.I.H., Extramural, práce podpořená grantem
Grantová podpora
203014/Z/16/Z
Wellcome Trust - United Kingdom
R35 GM130385
NIGMS NIH HHS - United States
U01 HL148860
NHLBI NIH HHS - United States
P 33298
Austrian Science Fund FWF - Austria
U01 CA235493
NCI NIH HHS - United States
PubMed
36543939
PubMed Central
PMC10263382
DOI
10.1038/s41592-022-01710-0
PII: 10.1038/s41592-022-01710-0
Knihovny.cz E-zdroje
- MeSH
- informatika MeSH
- lipidomika * MeSH
- lipidy chemie MeSH
- software MeSH
- výpočetní biologie * metody MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
- Research Support, N.I.H., Extramural MeSH
- Názvy látek
- lipidy MeSH
Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.
Babraham Institute Babraham Research Campus Cambridge UK
Biological Science Division Pacific Northwest National Laboratory Richland WA USA
Bruker Daltonics GmbH and Co KG Bremen Germany
Center for Biotechnology University of Bielefeld Bielefeld Germany
Department of Analytical Chemistry University of Vienna Vienna Austria
Department of Bioengineering University of California San Diego CA USA
Department of Bioinformatics BiGCaT NUTRIM Maastricht University Maastricht The Netherlands
Department of Chemistry Biology and Biotechnology University of Perugia Perugia Italy
Field of Excellence BioHealthe University of Graz Graz Austria
Graduate School of Medical Life Science Yokohama City University Yokohama Japan
Institute of Parasitology McGill University Montreal Canada
Institute of Pharmaceutical Sciences University of Graz Graz Austria
Maastricht Centre for Systems Biology Maastricht University Maastricht The Netherlands
Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany
RIKEN Center for Integrative Medical Sciences Yokohama Japan
RIKEN Center for Sustainable Resource Science Yokohama Japan
Scripps Center for Metabolomics and Mass Spectrometry The Scripps Research Institute La Jolla CA USA
Structural and Computational Biology Unit European Molecular Biology Laboratory Heidelberg Germany
Systems Immunity Research Institute School of Medicine Cardiff University Cardiff UK
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