Cargando…
MRM-DIFF: data processing strategy for differential analysis in large scale MRM-based lipidomics studies
Based on theoretically calculated comprehensive lipid libraries, in lipidomics as many as 1000 multiple reaction monitoring (MRM) transitions can be monitored for each single run. On the other hand, lipid analysis from each MRM chromatogram requires tremendous manual efforts to identify and quantify...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4311682/ https://www.ncbi.nlm.nih.gov/pubmed/25688256 http://dx.doi.org/10.3389/fgene.2014.00471 |
_version_ | 1782355040104611840 |
---|---|
author | Tsugawa, Hiroshi Ohta, Erika Izumi, Yoshihiro Ogiwara, Atsushi Yukihira, Daichi Bamba, Takeshi Fukusaki, Eiichiro Arita, Masanori |
author_facet | Tsugawa, Hiroshi Ohta, Erika Izumi, Yoshihiro Ogiwara, Atsushi Yukihira, Daichi Bamba, Takeshi Fukusaki, Eiichiro Arita, Masanori |
author_sort | Tsugawa, Hiroshi |
collection | PubMed |
description | Based on theoretically calculated comprehensive lipid libraries, in lipidomics as many as 1000 multiple reaction monitoring (MRM) transitions can be monitored for each single run. On the other hand, lipid analysis from each MRM chromatogram requires tremendous manual efforts to identify and quantify lipid species. Isotopic peaks differing by up to a few atomic masses further complicate analysis. To accelerate the identification and quantification process we developed novel software, MRM-DIFF, for the differential analysis of large-scale MRM assays. It supports a correlation optimized warping (COW) algorithm to align MRM chromatograms and utilizes quality control (QC) sample datasets to automatically adjust the alignment parameters. Moreover, user-defined reference libraries that include the molecular formula, retention time, and MRM transition can be used to identify target lipids and to correct peak abundances by considering isotopic peaks. Here, we demonstrate the software pipeline and introduce key points for MRM-based lipidomics research to reduce the mis-identification and overestimation of lipid profiles. The MRM-DIFF program, example data set and the tutorials are downloadable at the “Standalone software” section of the PRIMe (Platform for RIKEN Metabolomics, http://prime.psc.riken.jp/) database website. |
format | Online Article Text |
id | pubmed-4311682 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-43116822015-02-16 MRM-DIFF: data processing strategy for differential analysis in large scale MRM-based lipidomics studies Tsugawa, Hiroshi Ohta, Erika Izumi, Yoshihiro Ogiwara, Atsushi Yukihira, Daichi Bamba, Takeshi Fukusaki, Eiichiro Arita, Masanori Front Genet Genetics Based on theoretically calculated comprehensive lipid libraries, in lipidomics as many as 1000 multiple reaction monitoring (MRM) transitions can be monitored for each single run. On the other hand, lipid analysis from each MRM chromatogram requires tremendous manual efforts to identify and quantify lipid species. Isotopic peaks differing by up to a few atomic masses further complicate analysis. To accelerate the identification and quantification process we developed novel software, MRM-DIFF, for the differential analysis of large-scale MRM assays. It supports a correlation optimized warping (COW) algorithm to align MRM chromatograms and utilizes quality control (QC) sample datasets to automatically adjust the alignment parameters. Moreover, user-defined reference libraries that include the molecular formula, retention time, and MRM transition can be used to identify target lipids and to correct peak abundances by considering isotopic peaks. Here, we demonstrate the software pipeline and introduce key points for MRM-based lipidomics research to reduce the mis-identification and overestimation of lipid profiles. The MRM-DIFF program, example data set and the tutorials are downloadable at the “Standalone software” section of the PRIMe (Platform for RIKEN Metabolomics, http://prime.psc.riken.jp/) database website. Frontiers Media S.A. 2015-01-30 /pmc/articles/PMC4311682/ /pubmed/25688256 http://dx.doi.org/10.3389/fgene.2014.00471 Text en Copyright © 2015 Tsugawa, Ohta, Izumi, Ogiwara, Yukihira, Bamba, Fukusaki and Arita. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Tsugawa, Hiroshi Ohta, Erika Izumi, Yoshihiro Ogiwara, Atsushi Yukihira, Daichi Bamba, Takeshi Fukusaki, Eiichiro Arita, Masanori MRM-DIFF: data processing strategy for differential analysis in large scale MRM-based lipidomics studies |
title | MRM-DIFF: data processing strategy for differential analysis in large scale MRM-based lipidomics studies |
title_full | MRM-DIFF: data processing strategy for differential analysis in large scale MRM-based lipidomics studies |
title_fullStr | MRM-DIFF: data processing strategy for differential analysis in large scale MRM-based lipidomics studies |
title_full_unstemmed | MRM-DIFF: data processing strategy for differential analysis in large scale MRM-based lipidomics studies |
title_short | MRM-DIFF: data processing strategy for differential analysis in large scale MRM-based lipidomics studies |
title_sort | mrm-diff: data processing strategy for differential analysis in large scale mrm-based lipidomics studies |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4311682/ https://www.ncbi.nlm.nih.gov/pubmed/25688256 http://dx.doi.org/10.3389/fgene.2014.00471 |
work_keys_str_mv | AT tsugawahiroshi mrmdiffdataprocessingstrategyfordifferentialanalysisinlargescalemrmbasedlipidomicsstudies AT ohtaerika mrmdiffdataprocessingstrategyfordifferentialanalysisinlargescalemrmbasedlipidomicsstudies AT izumiyoshihiro mrmdiffdataprocessingstrategyfordifferentialanalysisinlargescalemrmbasedlipidomicsstudies AT ogiwaraatsushi mrmdiffdataprocessingstrategyfordifferentialanalysisinlargescalemrmbasedlipidomicsstudies AT yukihiradaichi mrmdiffdataprocessingstrategyfordifferentialanalysisinlargescalemrmbasedlipidomicsstudies AT bambatakeshi mrmdiffdataprocessingstrategyfordifferentialanalysisinlargescalemrmbasedlipidomicsstudies AT fukusakieiichiro mrmdiffdataprocessingstrategyfordifferentialanalysisinlargescalemrmbasedlipidomicsstudies AT aritamasanori mrmdiffdataprocessingstrategyfordifferentialanalysisinlargescalemrmbasedlipidomicsstudies |