Allelic Variation Explorer
AL
If you are studying how single nucleotide polymorphisms are clustered in genomic samples, then the Allelic Variation Explorer can help you visualize them.
Updated 22 months ago
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Image: International Maize and Wheat Improvement Center (CC License)
Food demand is projected to increase by 50% in 2030. One way to tackle this challenge is by breeding new crops to ensure food security; crops, for example, that are more resistant to drought. Farmers have known for centuries that crossbreeding of animals and plants could favor certain desirable traits such as faster growth, larger seeds or sweeter fruits. While early farmers made some considerable achievements in plant breeding, they did not have any knowledge of inheritance and genetics.
It wasn’t until 1865 that Gregor Mendel published his scientific discoveries on genetics and plant breeding. Although Mendel’s findings were ignored for a long time, they were rediscovered at the beginning of the 20th century, marking the starting point of a dedicated plant breeding sector. As our understanding of genetics improved, plant breeding activities became much more advanced. Modern chemistry, biology, genetics, and also information technology have been advancing plant breeding activities in the last century.
Our understanding of the genetic basis of important plant traits have radically changed and are leading to completely new approaches in plant research and breeding. Genomics-based technologies in life sciences are revolutionizing academic and industrial innovation in the agro-food and green life sciences domain. This will enable a more efficient and less trial and error based exploitation and protection of these plant traits in a classical – non-genetically modified – plant breeding context.
To effectively utilize these technologies in plant science and plant breeding, adequate eScience environments for storage, retrieval, security, analysis, manipulation and use of data are needed, creating challenges in data production but foremost in data handling, high-performance computing, data management, standardization, statistics, design for experimentation, visualization, and multidisciplinary collaboration. Although most issues have a large technical component, there are profound conceptual, methodological and even social components to consider.
Any individual company or organization can hardly meet these challenges. Therefore in 2009 a pre-competitive initiative, the Virtual lab for Plant Breeding (VLPB) was initiated. Starting with 4 companies and 3 academic institutes, now over 15 organizations and companies are participating.
VLPB tackles eScience challenges in the green life sciences across several key areas: proper design for experimentation, good bioinformatics methodology, functional problem-solving environments, reliable e-infrastructure and adequate e-bioscience support. In addition to this much attention is taken to support the VLPB community and secure its continuity.