The first step in a genetic study program is to have a reference genome. This represents the basis on which we are going to measure genetic variability and to study the characteristics that our strains have at the molecular level and how to maximize their potential. We apply state-of-the-art sequencing technologies in short and long read mode (Illumina, Oxford Nanopore, Pacbio) to ensure the highest quality assemblies.
Genome annotation with transcriptomics data allows us to understand the structure of each genome.

Many references are better than one, so we want to expand the knowledge base in genomics and build a Cannabis pangenome. This will allow us to compare and study more detailed genetics and map more than 80% of the reads, revealing 1-nucleotide level strains (SNPs) within the whole genome or structural variations (CNVs, Indels). We can also track the information of each allele (phasing) as more informative markers.

Understanding the genetic content of germplasm allows us to maximize diversity and focus development efforts on key strains with potential. We can target whole genomes or specific regions that make each strain unique and keep an up-to-date, homogenous, and secure inventory.

The collection of genotypic data at different depth levels is a very powerful tool in the process of plant breeding and improvement.
Genotyping enables the tracking of specific molecular markers to select desirable traits (MAS, marker assisted selection) or to develop pure or elite lines (MABC, marker assisted backcross).
We can identify strains with unique molecular codes (fingerprinting) as an identification and security tool or enable the intellectual protection of developments in Cannabis genetics.

Genomic scanning, from the collection of millions of data points scattered throughout the genome, enables the identification of quantitative trait loci (QTL mapping) to associate genomic regions to valuable and desired traits of a crop. Knowing where to scan allows identifying and aggregating attributes such as resistance, performance, and specific cannabinoid profiles.
As we increase the amount of information associated with the different Cannabis genomes it is possible to use machine learning tools to predict the results of the molecular breeding process, assisting in the improvement of strains through genome-wide association studies (GWAS) and genomic selection (GS).

The integration of all the information obtained allows us to lay the foundations of a breeding program to expand the genetic capabilities of a germplasm and to make decisions about which lines to keep and which elements to cross for the best results in the development of tailor-made Cannabis genetics.