Integrated genetic and metabolic characterization of Latin American cassava (Manihot esculenta) germplasm

May 6, 2023·
Laura Perez-Fons
,
Tatiana Maria Ovalle
,
Margit Drapal
,
Maria Alejandra Ospina
Anestis Gkanogiannis
Anestis Gkanogiannis
,
Adriana Bohorquez-Chaux
,
Luis Augusto Becerra Lopez-Lavalle
,
Paul David Fraser
· 0 min read
Abstract
Cassava (Manihot esculenta Crantz) is an important staple crop for food security in Africa and South America. The present study describes an integrated genomic and metabolomic approach to the characterization of Latin American cassava germplasm. Classification based on genotyping correlated with the leaf metabolome and indicated a key finding of adaption to specific eco-geographical environments. In contrast, the root metabolome did not relate to genotypic clustering, suggesting the different spatial regulation of this tissue’s metabolome. The data were used to generate pan-metabolomes for specific tissues, and the inclusion of phenotypic data enabled the identification of metabolic sectors underlying traits of interest. For example, tolerance to whiteflies (Aleurotrachelus socialis) was not linked directly to cyanide content but to cell wall–related phenylpropanoid or apocarotenoid content. Collectively, these data advance the community resources and provide valuable insight into new candidate parental breeding materials with traits of interest directly related to combating food security.
Type
Publication
Plant Physiology
publication
Anestis Gkanogiannis
Authors
Senior AI/ML and genomics practitioner

Senior AI/ML and genomics practitioner with ~15 years building open-source, production-grade tools for large-scale biological data. Maintainer of multiple Bioconductor packages (fastreeR, metabinR, jvecfor), and author of agentic, LLM-driven tooling that runs reproducible bioinformatics workflows from natural-language requests.

Broad multi-omics background spanning genome assembly and annotation, population genomics, large-scale NGS and functional-genomics analysis, and metagenomics, backed by reproducible HPC software and end-to-end program leadership. Currently focused on bringing modern AI — embeddings, deep learning, and LLM-based agents — to making complex omics datasets faster and easier to interrogate.