Chromosome-level genome assembly and functional annotation of *Citrullus colocynthis*: unlocking genetic resources for drought-resilient crop development

Oct 23, 2024·
Anestis Gkanogiannis
Anestis Gkanogiannis
,
Hifzur Rahman
,
Rakesh Kumar Singh
,
Augusto Becerra Lopez-Lavalle
· 0 min read
Abstract
This study presents the first comprehensive genome assembly and annotation of Citrullus colocynthis, a drought-tolerant wild close relative of cultivated watermelon, highlighting its potential for enhancing agricultural resilience to climate change. The study achieved a chromosome-level assembly using advanced sequencing technologies, including PacBio HiFi and Hi-C, revealing a genome size of approximately 366 Mb with low heterozygosity and substantial repetitive content. Our analysis identified 23,327 gene models, that could encode stress response mechanisms for species’ adaptation to arid environments. Comparative genomics with closely related species illuminated the evolutionary dynamics within the Cucurbitaceae family. In addition, resequencing of 27 accessions from the United Arab Emirates (UAE) identified genetic diversity, suggesting a foundation for future breeding programs. This genomic resource opens new avenues for the de novo domestication of C. colocynthis, offering a blueprint for developing crops with enhanced drought tolerance, disease resistance, and nutritional profiles, crucial for sustaining future food security in the face of escalating climate challenges.
Type
Publication
Planta, 260(6), 124
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.