Who We Are
We are a team of developers, molecular breeders, statisticians, bioinformaticists and breeders with a rich experience in managing data and applying genomic technologies to improve breeding. We are the first large-scale public-sector effort with the goal to systematically apply high-density genotypic information to the breeding of staple crops in the developing world. Initially the focus is on rice, wheat, maize, sorghum and chickpea in South Asia and Sub-Saharan Africa. The genomic data management systems will include databases, imputation systems, and decision support tools for plant breeders.
Our Mission
To build open-source genomic data management and analysis tools to enable breeders to implement genomic and marker-assisted selection as part of their routine breeding programs.
Latest News
GOBii director Liz Jones visits Bharadwaj Chellapilla and his team at the Indian Agricultural Research Institute (IARI), Delhi.
Bharadwaj Chellapilla’s team are collaborating with GOBii and Kelly Robbins’s team at Cornell University to…
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Mate and Check: A Practical Approach to Identifying F1
Carlos Ignacio from International Rice Research Institute will present Mate and Check: A Practical Approach to Identifying F1…
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EiB Plaform Module 5 Roadmap meeting
Left front to right: Yaw Nti-Addae (Cornell,BTI), Moria Sheehan (Cornell), Iain Milne (JHI), Pete Selby…
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Our Products
GOBii Genomics Data Manager (GDM)
GOBii Genomics Data Manager (GDM) consists of a data loader, a database, file systems, data extract, and email notification. Communication between the layers occurs through web services.
GOBii DArT Tools
The GOBii-QC (quality control) module was developed with Diversity Arrays Technology Pty Ltd using their open-source analysis platform KDCompute. GOBii-QC run by KDCompute is fully integrated with the GOBii-GDM system.
GOBii JHI Tools
Marker-Assisted Back Crossing (MABC) module: is an open source platform developed by GOBii with the James Hutton Institute (JHI) for conducting marker-assisted backcross visualization and selection analysis.
