Join us for our next monthly webinar on Sept 3, 12PM CDT. Dr. Sonia Balyan of the Indian Biological Data Centre will be presenting a talk entitled “Indian Crop Phenome Database: Advancing Crop Research Through Open Phenomic Data”
| 1P ET | 12P CT | 11A MT | 10A PT |
Find your local time here
Join Zoom Meeting
https://us06web.zoom.us/j/82038356125?pwd=YVFMRElMdEpHZmtObXFvZlA4QVFXQT09
Meeting ID: 820 3835 6125
Passcode: 160683
Abstract
The Indian Crop Phenome Database (ICPD), developed at the Indian Biological Data Centre (IBDC), isa pioneering national initiative designed to address the challenges of managing large-scale phenotypic and associated datasets in agriculture. India generates vast volumes of phenotypic data from diverse crop species through field trials, breeding programs, and research projects; however, the absence of standardized formats and dedicated repositories has often left this wealth of information underutilized.
ICPD addresses these gaps by fully embracing the FAIR principles — ensuring data are Findable, Accessible, Interoperable, and Re-usable. As the designated data hub for major mission-mode programs on Characterization of Genetic Resources supported by the Department of Biotechnology (DBT), India, ICPD offers a robust framework for digitization, curation, and sharing of crop phenotyping data, fostering seamless knowledge exchange across the scientific community. Each dataset receives a unique IBDC accession, ensuring traceability, proper citation, and long-term preservation.
Supporting over 30 crop species, ICPD adopts international ontology standards for traits, tissues, developmental stages, and methodologies, while also allowing the creation of new ontology terms with temporary accessions that undergo expert curation. This generic framework enables the submission of any crop phenome data, providing both flexibility and standardization.
By serving as a centralized, standards-driven, and FAIR-compliant repository, ICPD is poised to transform phenomics research in India — accelerating the development of climate-resilient, high-yield, and pest-resistant cultivars, and strengthening the scientific foundation for global food and nutritional security.
Hi everybody,
Join us for our monthly webinar TOMORROW, *August 6th, at 12 PM CDT*.
Trupti Joshi (Marshall University)
will talk about translational bioinformatics resources and AI solutions for
multiomics research.
I have included more details about the webinar and the Zoom link to attend
the webinar below.
I hope you will join us.
Best,
Annarita
:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:
*Abstracts:*
*Translational Bioinformatics Resources and AI Solutions for Multiomics
Research*
Next generation sequencing and multiomics data (bulk and single-cell)
capturing molecular changes from genomics all the way to phenomics, have
become an integral part of research in all domains including biomedical
sciences, plants sciences, and others. This rapid revolution in the
multiomics has posed a growing need for translational tools that can handle
large amounts of data, are easily expandable, provide interpretable results
and can be readily applied to any species. To address such translational
needs, we have developed Soybean Knowledge Base (SoyKB) and Knowledge Base
Commons (KBCommons) web-based frameworks, both fully equipped to handle the
entire multiomics landscape for all organisms. Our developed tools, such as
Allele Catalog, GenVarX, AccuTool, and MaDis, are specifically designed to
provide the plant community with efficient data-driven solutions for better
breeding strategies. Additionally, our G2PDeep, deep learning method,
provides a comprehensive web-based resource for phenotype predictions using
multiomics data for all organisms.
*| 1P ET | 12P CT | 11A MT | 10A PT |*
Find your local time here
<https://www.timeanddate.com/worldclock/fixedtime.html?msg=AgBioData+Monthly…>
*Join Zoom
Meetinghttps://us06web.zoom.us/j/82038356125?pwd=YVFMRElMdEpHZmtObXFvZlA4QVFXQT09
<https://us06web.zoom.us/j/82038356125?pwd=YVFMRElMdEpHZmtObXFvZlA4QVFXQT09>*
Meeting ID: 820 3835 6125
Passcode: 160683
