We are an innovative biotechnology company, specialized in molecular breeding and bioinformatics of agri-, horticultural crops and ornamentals. We develop independent technologies and new tools for molecular breeding to create a competitive advantage for our shareholders. Genetwister develops its own proprietary software for database management, data-analysis and data-mining. Genetwister is proud to have five internationally renowned breeding companies as its shareholders.
Advanced statistical models are needed to increase the power to discover (new) associations between genetic and phenotypic variation
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.
Tailored marker selection allowing for genotyping regardless of species, genome size and ploidy level.
Discovery of markers in all plant species by next-generation sequencing for accelerated plant breeding
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.
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.
To interconnect different knowledge domains, we use different semantic web technologies.
CROPaware – A web based software platform for breeding and genomics data management.
Appropriate experimental design and data analysis of breeding trials are essential. Genetwister offers assistance in the designing of experiments, taking into account the breeding program’s practical considerations.
Acceleration and cost reduction in plant breeding with improved genotype/phenotype relations, utilizing linked variants.