Davoud Torkamaneh

This study characterizes volatile organic compounds (VOCs) in 165 cannabis cultivars, identifying three chemotypes based on VOC profiles and their odor activity.

The paper presents chromosome-scale genome assemblies of four duckweed species, revealing genomic plasticity and adaptations to aquatic environments through gene loss and pathway retention.

The paper identifies candidate trichome development regulators in Cannabis sativa by comparing gene expression across different floral phenotypes and sexes, revealing a sexually dimorphic regulatory program.

This study performs a sex chromosome-aware GWAS on cannabis, identifying 16 genome-wide significant markers on ChrX and ChrY that influence agronomic traits.

This study uses machine learning to classify flowering time in Cannabis landraces by integrating genomic and morphophysiological data, achieving 96.6% accuracy.

The paper investigates the role of gibberellin signaling in ethylene-driven sex reversal in Cannabis sativa, identifying key genes involved in GA regulation and their differential expression during sex changes.

This study identifies 91 genomic regions associated with 40 traits in Cannabis landraces using GWAS, revealing pleiotropic loci and highlighting their potential for genetic improvement.

DESeq2-MultiBatch corrects batch effects in multifactorial RNA-seq experiments by leveraging DESeq2's internal model, preserving biological differences and improving data analysis.

This study identifies sex-specific ethylene-responsive genes driving floral sex plasticity in Cannabis through multi-omic analysis of hormonally induced sex change.

This study uses an ethephon-based assay to examine ethylene responses in Cannabis sativa seedlings, revealing a unique triple-response-like phenotype and conserved ethylene signaling pathways.

This study compares sexual expression, biomass, cannabinoid content, and seed production in XXX and XXY triploid cannabis plants to understand Y chromosome role in sex determination.

This study performs GWAS on drug-type Cannabis to identify genetic markers associated with agronomic and morphological traits using HD-GBS and statistical methods.

This study optimizes ex-vitro one-step hairy root transformation in Cannabis using the RUBY system, finding higher transformation efficiency with A4 strain and drug-type seedlings.

This study compares conventional and AI-based variant callers using short and long reads from the GIAB project, finding AI tools outperform conventional ones in accuracy for SNVs and INDELs.

SNPLift converts genetic variant coordinates between genome assemblies efficiently, achieving high accuracy across multiple species.

The paper combines Oxford Nanopore and Illumina sequencing to analyze structural variation in soybean cultivars, showing that long-read data can be effectively used with short-read data for population-scale SV analysis.

This study identifies 726 differentially expressed genes in apple abscission zones treated with hexanal, showing reduced ethylene and ABA biosynthesis and cell-wall remodelling, leading to improved fruit retention.