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Genetwister investigates new methods to integrate phenotypic data into genetic models to support breeding

Improved genomic selection and breeding utilizing high quality reference genomes

High parallel computing version of a sequence alignment application.

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.

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

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.

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

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

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.

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