Computomics Reviews & Product Details

Computomics builds AI-powered predictive breeding software for commercial crop breeding programs. Our machine learning models forecast how a given genotype will perform in a given environment, so breeders can decide which candidates to advance years before multi-location field trials could tell them. The core platform, xSeedScore®, combines genomic data (SNP marker panels, sequence data, pedigree) with environmental data (temperature, precipitation, soil characteristics) and historical field trial measurements. It delivers genomic prediction and genomic selection at the scale of an entire breeding program. Typical questions it answers: which lines hold yield under heat and drought stress, which candidates are stable across environments rather than winning in one, and where a variety should be positioned commercially. CropCompass and BreedScope extend this into variety placement and breeding program analytics. Who we work with: plant breeders and R&D teams in field crops, vegetables, forage crops, and specialty crops, plus agricultural biotech and microbiome research groups. Use cases include genomic selection, genotype-by-environment (GxE) interaction modeling, phenotype prediction, multi-environment trial analysis, hybrid performance prediction, trait discovery, marker-assisted selection support, and variety placement. Why breeders choose us: our machine learning is interpretable, so results can be inspected and defended, not just consumed. We work with your existing data, including messy historical trial records, and we do not require you to restructure your breeding program to get value. Every setup is delivered by a team of machine learning, plant genomics, and bioinformatics specialists rather than handed to a generic data science group. Computomics was founded in 2012 in Tübingen, Germany, as a spin-off from the Max Planck Institute for Biology and the University of Tübingen, with a US entity in Madison, Wisconsin. We have completed more than 200 projects for customers in 22 countries. Keywords: predictive breeding software, genomic prediction, genomic selection, AI for plant breeding, crop performance prediction, GxE modeling, breeding program analytics, agricultural machine learning, bioinformatics services, climate-smart breeding.