The machine learning technology behind all future crops.
Innovative plant breeding with ⨉SeedScore®,
our unique machine learning-based technology.
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Computomics' machine learning not only answers your toughest plant breeding questions, but empowers you to ask questions you never had the audacity to pose. Computomics’ ⨉SeedScore® technology provides you with the answers, putting you in control of your plant breeding program:
Whether your plant breeding goals are commercial, consumer or environmental, Computomics’ team can help move your program way beyond current limitations of hybrid performance prediction, available land for testing and complex trait planning.
Get the answers you need to accelerate your plant breeding program, today and tomorrow.
We are happy to support you on your way to a new commercial product by providing customized technical support adjusted to your specific plant breeding needs.
Benefit from our machine learning-based regularized kernel methods to predict phenotypes from genome-wide markers. These methods model heterosis and genetic gain. We store the trained predictors to reproducibly analyze next season’s data to make results directly comparable.
We work with you — from desired trait to improved plant. In cooperation with experts in the field of plant gene editing, we offer a full range of services to deliver your desired crop variety. Our comprehensive consultancy applies optimal technologies for your individual goals. We leverage machine learning-based genome analysis to identify the best genome editing targets, then employ a high-quality genome editing service accompanied by extensive quality controls to ensure optimal results. You will gain plant varieties, optimized with your traits of interest, to advance into the commercial pipeline and/or introduce into your breeding programs.
We help you to identify genetic markers for your traits of interest from sequencing data, including single nucleotide polymorphisms (SNPs), insertions and deletions (InDels), copy number variations (CNVs) or structural variants (SVs). By relying on sequencing-based genotyping we ensure an unbiased view of the variance of a population, as it does not rely on previous knowledge.
Depending on your project and specific needs, we set up a tailor-made sequencing-based genotyping pipeline which will take into account optimal sequencing technologies, parameters tuned to arrive at the marker resolution required for your goals and the possibility to impute missing data.
In addition to traditional plant breeding, new breeding techniques such as CRISPR offer novel possibilities to optimize and improve plant traits. Editing enables targeted, flexible and rapid engineering of plant genomes.
Despite the fact that these breakthrough technologies are paving the way to design plants for the future, there are still numerous challenges to overcome. One of them is precise detection and identification of the relevant editing targets that can make a positive impact on a desirable trait. This can be challenging, time-consuming, and labor-intensive, especially for complex traits like yield or diseases resistance.
We work with you – from desired impact to improved plant: In partnership with Hudson River Biotechnology (HRB), we offer the most comprehensive end-to-end-solution for your breeding program, to develop varieties that are more resilient, nutritious and capable of delivering great yield even in a changing climate.
Accelerate your plant breeding with our joint solution AccelATrait™ and benefit from the combined experience of experts in bioinformatics, plant breeding and genome editing. Using the latest machine learning and CRISPR genome editing technologies, we bring new plant varieties to market 3-4 times faster than traditional breeding.
Benefits of Computomics x HRB AccelATrait™ solution:
xSeedScore Information Download
Predict virtual hybrids from a male and female double-haploid population and predict hybrid phenotypes that exceed their parents and testers
Multi-trait optimization in malting barley for specific climates
Advancing rice breeding by predicting actual phenotypic values in specific environments