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Willow Genome Sequencing & Bioinformatics Integration Project

Reference Number
BBS/E/T/000GP017
Title
Willow Genome Sequencing & Bioinformatics Integration Project
Status
Completed
Energy Categories
Renewable Energy Sources(Bio-Energy, Production of other biomass-derived fuels (incl. Production from wastes))
Research Types
Basic and strategic applied research
Science and Technology Fields
BIOLOGICAL AND AGRICULTURAL SCIENCES (Biological Sciences)
UKERC Cross Cutting Characterisation
Not Cross-cutting
Principal Investigator
Dr M Caccamo
Bioinformatics
The Genome Analysis Centre (TGAC)
Award Type
Institute Project
Funding Source
BBSRC
Start Date
01 November 2012
End Date
31 October 2014
Duration
24 months
Total Grant Value
£25,000
Industrial Sectors
Transport Systems and Vehicles
Region
East of England
Programme
Investigators
Principal Investigator
Dr M Caccamo, Bioinformatics, The Genome Analysis Centre (TGAC)
Web Site
Objectives
Objectives not supplied
Abstract
BSBEC-BioMASS (the Perennial Energy Crops programme within BSBEC) is providing the underpinning science needed to improve short rotation coppice (SRC) willow as a key UK biomass feedstock for renewable fuels and bioproducts. This project builds on unique genetic resources in SRC willow available at Rothamsted Research. Initial targets will be growth traits associated with increased yield and increased conversion of the biomass to usable products.
In order to aid the BSBEC-BioMASS consortium to bridge the gap between phenotype and genotype in relation to the key target traits under pursuit, a draft reference sequence for Salix viminalis will be generated. The reference will be assembled, annotated and made publicly available via a genome browser and through deposition of the sequence into the appropriate public archives. In addition the genomes of ten parents of willow mapping populations and a further 22 members of an association mapping diversity panel will be resequenced. Variations within these 32 accessions will be identified and made publicly available, which will enable fine mapping of quantitative trait loci (QTL). Candidate mutations within genes contained in QTL regions can then be readily identified and selected for downstream investigation, improving the efficiency of gene discovery work based on QTL.
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Added to Database
15/12/14