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Date: Saturday, January 14, 2012
Time: 2:30 PM
Time: 2:30 PM
Room: California
Compilation of genetic maps combined to QTL meta-analysis has proven to be a powerful approach contributing to the identification of candidate genes underlying quantitative traits. One of the most interesting properties of meta-QTL (or consensus QTL) is its confidence interval (IC) often shorter than IC of corresponding QTLs, decreasing the number of candidate genes to consider. As map compilation and QTL meta-analysis do not rely on genotyping raw data or trait measure, they can be easily achieved even if user holds maps from the literature or genetic databases. BioMercator was the first software offering a complete set of algorithms and visualization tool covering all steps required to perform QTL meta-analysis.
Genome sequence and annotation are now available for an increasing number of organisms. Structural annotation is the physical positions of features (genes, mRNAs, introns, exons) over the genome and is distributed as GFF files by every genome consortium; functional annotation may be available as a list of genes associated to Gene Ontology terms. Mining structural and functional data in QTL and metaQTL region would be of great benefit to help derive hypothesis about candidate genes.
The new version of this software, BioMercatorV4 allows such approach by integrating genetic maps, annotations and anchor files (describing anchoring of genetic map and genomic sequence, often provided by genome projects), providing the user with a genetic and physical maps alignment. Additional tools for GO functional annotation analysis within QTL region are also available . (All results are exportable to GFF3 format)
Genome sequence and annotation are now available for an increasing number of organisms. Structural annotation is the physical positions of features (genes, mRNAs, introns, exons) over the genome and is distributed as GFF files by every genome consortium; functional annotation may be available as a list of genes associated to Gene Ontology terms. Mining structural and functional data in QTL and metaQTL region would be of great benefit to help derive hypothesis about candidate genes.