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Computational Technologies

The world of plant genomics has witnessed an explosion of genomic and related data in the past decade. More complex layers of information, such as gene interaction networks and small-size pieces of information are continuously being added and expanded, creating a gold mine of information on the one hand, and a major data-processing bottleneck on the other.

Evogene has developed innovative computational analysis tools that allow processing immense amounts of public and proprietary data in sophisticated and unconventional ways. Our computational technologies allow us to handle increasing levels of data complexity with alacrity and precision, transforming them into meaningful knowledge. Continuous updating and streamlining of our computational tools help us to continue and define new frontiers in plant genomics. 


Discovery of Plant Genes

ATHLETE™ is Evogene's proprietary computational technology for plant gene discovery.  ATHLETE™ uses vast amounts of genomic and additional data types from various plant species for a rapid discovery and prioritization of hundreds of plant genes linked to a target trait of choice. After introduction into desired corps by biotechnology means, the genes discovered by ATHLETE™ are predicted to confer the required traits, such as yield and drought tolerance.

ATHLETE™ is designed for addressing Evogene's evolving demand and capacity for gene discovery and harnessing the data explosion of the ‘omics’ era for the development of improved seeds. ATHLETE™ is comprised of unique algorithmic tools and novel data mining concepts, resulting in a rapid and reliable list of genes relevant to a target trait. The technology is applicable for different traits and crops, according to the needs of our various internal programs and partnerships.

As of today, ATHLETE™ yielded over 4,000 novel genes relating to key plant traits, all under patents granted & pending


Improve Product Development Success

Gene2Product™ technology consists of a number of proprietary computational genomic components to improve trait efficacy and the probability of successful development of biotechnology seed products.

Gene2Product™ is designed to improve trait efficacy through optimizing gene 'mode of use' utilizing various computational technologies and approaches , including: optimizing gene expression and regulation (Repack); multiple gene stacking (PlaNet); improving trait efficacy through genes stable to different environments and genetic backgrounds (GeneDex); and selection of preferred allele for each gene (GeneSpec).


Breeding Enhancement

EvoBreed™ is Evogene's proprietary computational technology for discovery of molecular markers to enhance plant breeding. EvoBreed™ offers plant breeders the opportunity to accelerate the breeding process by making insightful and logical breeding decisions based on the abundant genomic, phenotypic and additional "omic" information becoming available today. The technology is aimed at eventually allowing plant breeders to design optimal crosses between breeds, in order to enhance a desired set of traits without compromising others.

The technology relies on Evogene's unique computational tools that offer a more reliable correlation between genetic data and plant phenotype. EvoBreed™ utilizes many of the ATHLETE™ features and allows a comprehensive cross analysis with independent data sources available through Evogene's ongoing gene discovery programs, resulting in a more credible gene-trait association prediction. Optimal cross-breeding schemes are based on such associations, yielding better performing crop varieties.


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Novel Target Discovery

PoinTarTM is Evogene’s proprietary computational platform for target discovery, aimed at identifying proteins that are important to plant function and performance. The targets being sought are those that, when inhibited (for instance by a chemical), lead to plant or weed death. The platform is the linchpin of Evogene’s herbicide discovery program, initiated in 2012 within the Company’s ag-chemical operations.  

An important feature of PoinTarTM is the ability to prioritize the resulting predictions for proteins on the basis of structural information such as ‘drugability’, that is the ability of the predicted target to interact with a chemical molecule. In addition, PoinTarTM is able to identify non-coding RNAs as potential targets, adding a substantial new class of overlooked molecules to the discovery process. The combination of these two computationally based features is anticipated to provide a unique and effective target discovery capability, with the potential to address the unmet need in the industry today for new herbicide “modes of action”.