Distributed Computing Blast Implementation

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

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.

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

Tailored marker selection allowing for genotyping regardless of species, genome size and ploidy level.

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

High-quality reference genome assemblies improve Genomic Selection and breeding

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

Appropriate experimental design and data analysis of breeding trials are essential. Genetwister offers assistance in the experimental design, taking into account all practical considerations of the breeding program.

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.