TY - JOUR
T1 - Guiding the choice of informatics software and tools for lipidomics research applications
AU - Ni, Zhixu
AU - Wölk, Michele
AU - Jukes, Geoff
AU - Mendivelso espinosa, Karla
AU - Ahrends, Robert
AU - Aimo, Lucila
AU - Alvarez-Jarreta, Jorge
AU - Andrews, Simon
AU - Andrews, Robert
AU - Bridge, Alan
AU - Clair, Geremy C.
AU - Conroy, Matthew J.
AU - Fahy, Eoin
AU - Gaud, Caroline
AU - Goracci, Laura
AU - Hartler, Jürgen
AU - Hoffmann, Nils
AU - Kopczyinki, Dominik
AU - Korf, Ansgar
AU - Lopez-Clavijo, Andrea F.
AU - Malik, Adnan
AU - Ackerman, Jacobo Miranda
AU - Molenaar, Martijn R.
AU - O’donovan, Claire
AU - Pluskal, Tomáš
AU - Shevchenko, Andrej
AU - Slenter, Denise
AU - Siuzdak, Gary
AU - Kutmon, Martina
AU - Tsugawa, Hiroshi
AU - Willighagen, Egon L.
AU - Xia, Jianguo
AU - O’donnell, Valerie B.
AU - Fedorova, Maria
N1 - Funding Information:
This publication is based upon work from COST Action EpiLipidNET, Pan-European Network in Lipidomics and Epilipidomics (CA19105; https://www.epilipid.net), supported by COST (European Cooperation in Science and Technology). Funding from the Wellcome Trust is gratefully acknowledged for LIPID MAPS (203014/Z/16/Z). LIPID MAPS is grateful for sponsorship from Cayman Chemical, Merck and Avanti Polar Lipids. T.P. is supported by the Czech Science Foundation Grant 21-11563M. Funding from the FWF P33298-B and Human Frontiers Science Progam RGP0002/2022 is gratefully acknowledged. ‘Sonderzuweisung zur Unterstützung profilbestimmender Struktureinheiten 2021’ by the SMWK and Deutsche Forschungsgemeinschaft (FE 1236/5-1 to M.F.) are gratefully acknowledged. JSPS KAKENHI (21K18216 to H.T.), the National Cancer Center Research and Development Fund (2020-A-9, H.T.), AMED Japan Program for Infectious Diseases Research and Infrastructure (21wm0325036h0001 to H.T.), JST National Bioscience Database Center (NBDC to H.T.), JST ERATO ‘Arita Lipidome Atlas Project’ (JPMJER2101 to H.T.).
Funding Information:
This publication is based upon work from COST Action EpiLipidNET, Pan-European Network in Lipidomics and Epilipidomics (CA19105; https://www.epilipid.net ), supported by COST (European Cooperation in Science and Technology). Funding from the Wellcome Trust is gratefully acknowledged for LIPID MAPS (203014/Z/16/Z). LIPID MAPS is grateful for sponsorship from Cayman Chemical, Merck and Avanti Polar Lipids. T.P. is supported by the Czech Science Foundation Grant 21-11563M. Funding from the FWF P33298-B and Human Frontiers Science Progam RGP0002/2022 is gratefully acknowledged. ‘Sonderzuweisung zur Unterstützung profilbestimmender Struktureinheiten 2021’ by the SMWK and Deutsche Forschungsgemeinschaft (FE 1236/5-1 to M.F.) are gratefully acknowledged. JSPS KAKENHI (21K18216 to H.T.), the National Cancer Center Research and Development Fund (2020-A-9, H.T.), AMED Japan Program for Infectious Diseases Research and Infrastructure (21wm0325036h0001 to H.T.), JST National Bioscience Database Center (NBDC to H.T.), JST ERATO ‘Arita Lipidome Atlas Project’ (JPMJER2101 to H.T.).
Publisher Copyright:
© 2022, Springer Nature America, Inc.
PY - 2023/2
Y1 - 2023/2
N2 - 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.
AB - 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.
KW - Annotation
KW - Identification
KW - Knowledgebase
KW - Lc-ms
KW - Lipids
KW - Metabolite
KW - Sex
KW - Sphingolipids
U2 - 10.1038/s41592-022-01710-0
DO - 10.1038/s41592-022-01710-0
M3 - Article
C2 - 36543939
SN - 1548-7091
VL - 20
SP - 193
EP - 204
JO - Nature Methods
JF - Nature Methods
IS - 2
ER -