Bioinformatic Tools in Crop Improvement - Crop Improvement: New Approaches and Modern Techniques - page 94

ATTED II Database
Co-expression data of the Arabidopsis transcriptome has been provided by the AT-
TED-II database. Investigation of key genes involved in specific metabolic path-
ways, and metabolome analysis was used with mutant lines (Obayashi et al. 2009 ).
The ATTED-II database has identified genes involved in lipid metabolism, and
UDP-glucose pyrophosphorylase 3 (UGP3) as an essential requirement in the first
step of sulfolipid biosynthesis (Okazaki et al. 2009 ). Co-expression analysis was
used to identify genes related to flavonoid biosynthesis, and the role of two key and
important flavonoid pathway genes UGT78D3 and RHM1 (Yonekura-Sakakibara
et al. 2008 ).
Results in Metabolite Profiling
The metabolic pathways that act in response to cold and dehydration conditions
in Arabidopsis were investigated by metabolome analysis using MS and microar-
ray analysis of overexpressors in genes encoding transcriptional factors (Maruyama
et al. 2009 ). Metabolomic profiling was also used to investigate chemical pheno-
typic changes between wild-type Arabidopsis and a knockout mutant of the NCED3
gene under dehydration. The metabolic data was integrated with transcriptome data
to reveal ABA-dependent regulatory pathways (Urano et al. 2009 ). Metabolome
profiling has also been used to evaluate chemical phenotypes of natural variations
and/or segregating populations in plant ecology and plant breeding. Analysis be-
tween metabolic expression and genomic diversity will enable the discovery of
more key genes involved in differences between metabolic and phenotype expres-
sion of plants (Schauer et al. 2008 ; Fu et al. 2009 ).
Metabolite QTL (mQTL)
Metabolite QTL (mQTL) analysis using segregated populations has been applied to
various plant species such as Arabidopsis , poplar and tomato in a popular 'forward
genetics' approach (Morreel et al. 2006 ; Schauer et al. 2006 ; Lisec et al. 2008 ;
Rowe et al. 2008 ; Schauer et al. 2008 ).
Metabolic and Genomic Diversities
The recent availability of data for genome-wide variation acquired by high-through-
put genotyping methods, including high volume resequencing, has provided some
details of genes association with nucleotide variation and phenotypic changes; es-
pecially ibn relation to the identification of key genes that play significant roles
in evolutionary histories and phylogeny (Sect. 2.5). Attempts to mine correlative
patterns between metabolic and genomic diversities have recently been applied to