Distributed Analysis Computing
Distributed Analysis Computing
At Genetwister, tools are developed using distributed computing models and technologies like Spark and Hadoop.
At Genetwister, tools are developed using distributed computing models and technologies like Spark and Hadoop.
High-quality reference genome assemblies improve Genomic Selection and breeding
To interconnect different knowledge domains, we use different semantic web technologies.
High performance BLAST implementation adapted to run in Spark clusters.
Acceleration and cost reduction in plant breeding with improved genotype/phenotype relations, utilizing linked variants.
Genomic selection (GS) accelerates achieving genetic gain through shorter breeding cycles and is an alternative to marker-assisted selection. Genetwister implements new breeding schemes and methods that integrate GS for crop improvement.
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.
Tailored marker selection allowing for genotyping regardless of species, genome size and ploidy level.
With increasing sample numbers comes the need for automating the most valuable and most often applied protocols at Genetwister.
Advanced statistical models are needed to increase the power to discover (new) associations between genetic and phenotypic variation.