TY  - JOUR
AU  - Vasilevski, A.
AU  - F.M. Giorgi, F.M.
AU  - Bertinetti, L.
AU  - Usadel, B.
TI  - LASSO modeling of the Arabidopsis thaliana seed/seedling Transcriptome: a model case for detection of novel mucilage and pectin metabolism genes
JO  - Molecular BioSystems
VL  - 8
SN  - 1742-206X
CY  - Cambridge
PB  - Royal Society of Chemistry
M1  - PreJuSER-21041
SP  - 2566 - 2574
PY  - 2012
N1  - Record converted from VDB: 12.11.2012
AB  - Whole genome transcript correlation-based approaches have been shown to be enormously useful for candidate gene detection. Consequently, simple Pearson correlation has been widely applied in several web based tools. That said, several more sophisticated methods based on e.g. mutual information or Bayesian network inference have been developed and have been shown to be theoretically superior but are not yet commonly applied. Here, we propose the application of a recently developed statistical regression technique, the LASSO, to detect novel candidates from high throughput transcriptomic datasets. We apply the LASSO to a tissue specific dataset in the model plant Arabidopsis thaliana to identify novel players in Arabidopsis thaliana seed coat mucilage synthesis. We built LASSO models based on a list of genes known to be involved in a sub-pathway of Arabidopsis mucilage synthesis. After identifying a putative transcription factor, we verified its involvement in mucilage synthesis by obtaining knock-out mutants for this gene. We show that a loss of function of this putative transcription factor leads to a significant decrease in mucilage pectin.
LB  - PUB:(DE-HGF)16
C6  - pmid:22735692
UR  - <Go to ISI:>//WOS:000308098600013
DO  - DOI:10.1039/c2mb25096a
UR  - https://juser.fz-juelich.de/record/21041
ER  -