DArTseq Overview

At the core of DArT technology is a genome complexity reduction concept.

 

Many methods have been developed to reduce genome complexity, however the DArT methods provide a significant advantage via an intelligent selection of genome fraction corresponding predominantly to active genes. This selection is achieved through the use of a combination of Restriction Enzymes which separate low copy sequences (most informative for marker discovery and typing) from the repetitive fraction of the genome.

While the initial DArT implementation on the microarray platform involves fluorescent labeling of representations and hybridization to dedicated DArT arrays. The DArTseq method deploys sequencing of the representations on the Next Generation Sequencing (NGS) platforms.

 

The advantage of DArTseq over the array version of DArT is currently limited to applications requiring very high marker densities (tens of thousands of markers). This technology is therefore positioned in the area of high resolution mapping and detailed genetic dissection of traits.

 

As modern breeding, moves rapidly in this direction, especially in larger organisations, DArTseq is increasingly used in crop improvement applications.

 

Please contact us if you are interested how DArTseq can be effectively deployed in your research or your breeding program.

 

DArTseq for a new organism starts with optimization of complexity reduction method(s). While the choice of restriction enzyme combinations is large, DArT PL has invested considerable effort in testing various combinations on a significant number of organisms and has developed sets of complexity reduction methods (representations) that are performing quite well compared to other methods.

 

The optimisation process usually selects one dominant method of complexity reduction for the crop, but in many cases several methods were identified which offer application-specific advantages. The difference between the methods can be both quantitative (different number of unique fragments in the representation) as well as compositional (different sets of fragments captured in the representations).

 

These differences in representation size and composition translate to different efficiencies in marker detection rate and quality (call rate and reproducibility) and can be further optimized for performance in different applications.