Frontiers in Genetics | 卷:5 |
MRM-DIFF: Data Processing Strategy for Differential Analysis in Large Scale MRM-based Lipidomics Studies | |
Masanori eArita1  Hiroshi eTsugawa2  Takeshi eBamba2  Erika eOhta2  Yoshihiro eIzumi2  Eiichiro eFukusaki2  Atsushi eOgiwara4  Daichi eYukihira4  | |
[1] National Institute of Genetics; | |
[2] Osaka University; | |
[3] RIKEN Center for Sustainable Resource Science; | |
[4] Reifycs Inc.; | |
关键词: lipidomics; Differential Analysis; multiple reaction monitoring; Compound identification; isotopic peak estimation; | |
DOI : 10.3389/fgene.2014.00471 | |
来源: DOAJ |
【 摘 要 】
Based on theoretically calculated comprehensive lipid libraries, in lipidomics as many as 1,000 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 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.
【 授权许可】
Unknown