SE60:/DS01
From Metabolonote
Sample Set Information
| ID | SE60 |
|---|---|
| Title | Metabolome-genome-wide association study dissects genetic architecture for generating natural variation in rice secondary metabolism |
| Description | genome-wide association studies (GWAS) were conducted to investigate the genetic architecture behind the natural variation of rice secondary metabolites. GWAS using the metabolome data of 175 rice accessions successfully identified 323 associations among 143 single nucleotide polymorphisms (SNPs) and 89 metabolites. The data analysis highlighted that levels of many metabolites are tightly associated with a small number of strong quantitative trait loci (QTLs). The tight association may be a mechanism generating strains with distinct metabolic composition through the crossing of two different strains. The results indicate that one plant species produces more diverse phytochemicals than previously expected, and plants still contain many useful compounds for human applications. |
| Authors | Fumio Matsuda, Ryo Nakabayashi, Zhigang Yang, Yozo Okazaki, Jun-ichi Yonemaru, Kaworu Ebana, Masahiro Yano, Kazuki Saito |
| Reference | Matsuda F et al. (2014) The Plant Journal Jan;81(1):13-23 |
| Comment |
The raw data files are available at DROP Met web site in PRIMe database of RIKEN.
Data Analysis Details Information
| ID | DS01 |
|---|---|
| Title | Metabolite annotation |
| Description | For structural elucidation of metabolite signals, MS/MS spectral tag (MS2T) libraries were constructed (Matsuda et al., 2011). The extracts of 14–15 cultivars were mixed and utilized for MS/MS spectra data acquisition. Analyses were repeated for 12 mixtures using automated data acquisition methods as previously described (Matsuda et al., 2009). Each MS2T entry was assigned a unique code, OSAXXpXXXXX, indicating the library name (OSAXXp) and entry number. MS2T libraries containing 164 051 entries were constructed. MS2Ts were added to metabolite signals, from which the structure of each metabolite signal was elucidated by searching the ReSpect (RIKEN MS/MS spectra database for phytochemicals) (Sawada et al., 2012), MassBank (Horai et al., 2010), KNApSAcK (Afendi et al., 2012), and PRIMe standard compound database (Sakurai et al., 2013). The two spectra were considered to be similar when the similarity score of the ReSpect search was greater than 0.6. Thresholds were set at m/z Δ0.05 and 0.15 min, respectively, for the molecular formula search on the KNApSAcK database and comparison of retention times. Based on the criteria proposed by the metabolome standard initiative (MSI) (Sumner et al., 2007), metabolite signals were ‘characterized’ when parts of a structure were deduced from mass data. Metabolite signals were‘annotated’ when a common metabolite was observed in the outputs from both the ReSpect and KNApSAcK searches. Metabolite signals were considered to be ‘identified’ when three distinct pieces of information, including the MS/MS spectra, exact mass number, and chromatographic behavior, were matched to identical metabolites (Table S4). Data obtained in this study are available on the PRIMe website (http://prime.psc.riken.jp/) (Sakurai et al., 2013). Isolation and structural determination of rice flavones has been reported previously (Yang et al., 2014). |
| Comment_of_details |
The peak data files of this analysis are available at MS2T.
The raw data files are available at DROP Met web site in PRIMe database of RIKEN.
