Distributed Computing Blast Implementation

Genomic selection (GS) is an alternative to marker-assisted selection, which can accelerate the genetic gain through shorter breeding cycles. Genetwister is implementing new breeding schemes and methods that integrate GS for crop improvement.

Improved genomic selection and breeding utilizing high quality reference genomes

With increasing sample numbers comes the need for automating the most valuable and applied protocols at Genetwister.

Using the state-of-the-art technologies such as long read sequencing technologies, Genetwister is able to build high quality genome assemblies of novel species, and obtain long range genomic information such as haplotypes and structural variants.

With the increasing demands for DNA molecule size and purity on the newest long read sequencing platforms, comes the need for developing new protocols for DNA isolation from different biological sources in the laboratory at Genetwister.

Advanced statistical models are needed to increase the power to discover (new) associations between genetic and phenotypic variation

Acceleration and cost reduction in plant breeding with improved genotype/phenotype relations, utilizing linked variants.

With increasing sample numbers and marker panel sizes, it is valuable to increase the throughput of sequencing and genotyping procedures. Genetwister develops cost-effective methods and applies available methods for allowing high-throughput genomics.

At Genetwister, tools are developed using distributed computing models and technologies like Spark and Hadoop.

To interconnect different knowledge domains, we use different semantic web technologies.