Individuals of a species differ from one another at the genetic level to various degrees. These differences represent different genotypes, or genetic constitutions, within a species. To better understand the genetic content of each individual genome, it is important to understand similarities and differences of gene sequences and their sub-components when compared across genomes. Therefore, the Seeker is looking for a methodology to accurately identify similar gene sequences across genomes from individuals of a single species.
This is a Reduction-to-Practice Challenge that requires written documentation and output from the data analysis algorithm, and submission of source code and executable if requested by the Seeker.
Individuals of a species differ from one another at the genetic level to various degrees. To deeply characterize the genetic content for each individual genome, it is important to understand which sequences of common ancestry have been inherited, possibly in a modified form, across the genomes. Existing knowledge about a gene variant from a well-characterized genome can be applied to better understand other variants, or alleles, of the same gene in different, uncharacterized genomes. Knowledge of which sequences represent the same genes in different individuals is necessary to understand the impact of similarities or any differences that may exist in the gene sequences of individuals from different genetic backgrounds.
The difficulty lies in determining which gene-derived sequences in the genomes are allelic. Transcription of a gene may produce many alternative transcript representations which differ in sequence composition. Finding the best mapping between transcripts of different genomes is a difficult and time-consuming task. Current methods rely on a combination of common software and proprietary techniques, but the reliability and accuracy of the processed results could be improved. Therefore, the Seeker is interested in a better methodology, with algorithms and/or best selections of existing software/programs, able to relate transcript sets of two genotypes within a species quickly and accurately to identify the allelic relationships.
The submitted proposal should include the following:
The Challenge award is contingent upon theoretical evaluation of the method/algorithm by the Seeker, and validation by the Seeker of the submitted software/algorithm/package.
To receive an award, the Solvers will not have to transfer their exclusive IP rights to the Seeker. Instead, Solvers will grant to the Seeker a non-exclusive license to practice their solutions.
Submissions to this Challenge must be received by 11:59 PM (US Eastern Time) on (UPDATED) December 31, 2019. Late submissions will not be considered.
ABOUT THE SEEKER
Corteva Agriscience™ is a publicly traded, global pure-play agriculture company that provides farmers around the world with the most complete portfolio in the industry - including a balanced and diverse mix of seed, crop protection and digital solutions focused on maximizing productivity to enhance yield and profitability. With some of the most recognized brands in agriculture and an industry-leading product and technology pipeline well positioned to drive growth, the company is committed to working with stakeholders throughout the food system as it fulfills its promise to enrich the lives of those who produce and those who consume, ensuring progress for generations to come. Corteva Agriscience became an independent public company on June 1, 2019 and was previously the Agriculture Division of DowDuPont. More information can be found at www.corteva.com.
What is an RTP Challenge?
An InnoCentive RTP (Reduction to Practice) Challenge is a prototype that proves an idea, and is similar to an InnoCentive Theoretical Challenge in its high level of detail. However, an RTP requires the Solver to submit a validated solution, either in the form of original data or a physical sample. Also the Seeker is allowed to test the proposed solution. For details about treatment of IP rights, please see the Challenge-Specific Agreement.