Bringing a range of expertise to uncover the details
Evogene is unique in its multi-disciplinary scientific approach.
Teams of leading scientists with diverse scientific expertise ranging from computational fields such as computer science, computational biology and computational chemistry; to biological driven fields such as microbiology, plant genetics, plant physiology and entomology, comprise the core of Evogene and are the key to innovation.
Evogene’s scientific team, which includes more than 60 PhD researchers from the world’s top research institutions, works together to identify key biological challenges in crop productivity. The combined knowledge of researchers coming from different scientific fields offers innovative solutions for key challenges.
Collecting massive data to create the big picture
Biological data is now available as never before.
Recent technological breakthroughs in the ability to sequence the genetic makeup of living organisms, plants, microbes and human, have revolutionized the availability of biological data.
This newfound availability of biological data provides the groundwork for expanding our knowledge of the world around us, presenting the opportunity to create new information.
Evogene collects and generates large amounts of relevant multi -OMICS big-data for specific biological questions. The Company’s data production pipelines complement the available public resources to create a comprehensive catalog of relevant information.
Transforming data into information
All data is carefully curated and only high-quality data is maintained.
Utilizing proprietary data processing pipelines, data is transformed into information and integrated in a comprehensive information network, which bridges unrelated biological and chemical information types.
Evogene’s information network incorporates data from the fields of plant biology, microbes and chemistry and is allocated to four interconnected data hubs:
- Plant database: ~14 million plant genes from 200 plant species
- Bacterial gene database: over 250 million bacterial genes
- Bacterial database: tens of thousands of bacterial isolates
- Chemical compound database: over 100 million purchasable agro-like and drug-like compounds
Evogene’s interconnected data hub is a source for innovative cross-discipline solutions. Each data hub provides a wide range of information about plants / bacteria / chemical compounds, and possible correlations between the data, when addressing a scientific challenge.
The interplay between these data hubs creates a synergy that elevates data to information. This interplay allows Evogene’s researchers to employ a multidisciplinary approach for complex challenges that require solutions outside of a single life-science discipline. For example, combining plant data with bacterial gene data to achieve novel insect resistance solution in crops.
Discovering correlations to connect the dots
Evogene’s computational analysis platform spans across biology and chemistry and incorporates over 60 computational sub-areas, such as comparative genomics, predictive analysis, structural 3D modelling, artificial intelligence and machine learning, encompassing over 100 development-years’ worth of algorithms and software code.
The analysis platform is tailored to query Evogene’s information network, facilitating innovative biological discoveries for the improvement of crop productivity and health. Evogene’s analysis platform is composed of several computational platforms for the discovery and optimization of genes, protein targets and chemistry, including:
BiomeMiner – A machine learning based approach for the discovery of insecticidal protein toxins from the bacterial sources integrating multiple information types
Gene2Product – This technology consists of a number of proprietary computational genomic components to improve trait efficacy and the probability of successful development of biotechnology seed products.
MicrobeMiner – The platform integrates phenotypic and genomic information on bacterial isolates to identify single or consortia of bacteria for improvement of plant health and productivity
PlaNet NG & ATHLETE – A gene network based approach for the discovery and optimization of genes and gene combinations for crop improvement
PointTar – A platform for identification of herbicidal protein targets which integrates both structural and functional considerations
PointHit – A robust virtual screening platform for the discovery of small molecules. The platform combines the physiochemical requirements for binding a specific protein target (identified by PointTar) with the prediction of small molecule interactions with plants and insects