Rev Genomics

Technology

Technology Portfolio

 
Visualization of genomic data. Red is positively correlated gene clusters. Yellow is negatively correlated gene clusters. These data feed directly into Rev's custom breeding platform.

Visualization of genomic data. Red is positively correlated gene clusters. Yellow is negatively correlated gene clusters. These data feed directly into Rev's custom breeding platform.

High Throughput Sequencing

This is the process of isolating DNA samples and reading the information contained digitally, usually producing millions to billions of “reads” representing short regions of the genome. This will be applied to Rev’s novel strains to computationally reconstruct the full genome sequences.

RNA-seq

DNA contains the information in genes that are transcribed into messenger RNA and then translated into proteins to perform tasks within the cell. RNA-seq is the process of sequencing and quantifying the messenger RNA for all genes expressed in a tissue for a given genotype, time point, or environmental growth conditions. Detailed knowledge of messenger RNA abundance across strains provides developmental stage specific genome editing and breeding targets.

 

Rev Genomics combines state of the art statistical and machine learning methods with expert knowledge for product creation. Shown are QTL mapping simulations based on literature review and genetic map data combined from published sources for client. The height of the peak (LOD score) in this graph indicates the strength of the correlation between that region of the genome and the trait of interest. In this case, the trait of interest is a major effect for a metabolite on chromosome 6 of one of our organisms of interest. This QTL can be detected with as few as 50 individuals in the population that meet the significance threshold (red line). However, smaller population sizes fail to detect smaller effect size QTL on chromosome 1.

Molecular Markers

Single Nucleotide Polymorphisms (SNPs)- RNA-seq data can also be used to genotype plants across many locations in the genome for single nucleotide polymorphisms (SNPs). Knowing the genotype of thousands of SNPs across the genome provides a “molecular fingerprint” for each strain. This information is crucial for intellectual property protection of new strains.

Quantitative Trait Loci (QTL) Mapping

Many traits such as yield, growth, and disease resistance are not controlled by just a single gene, but rather a suite of genes scattered throughout the genome. These traits are considered “quantitative”. The SNP information from each strain will be statistically associated with the trait information from each strain thereby indicating genomic regions of interest. Once the region of the genome is narrowed, RNA-seq data will be used to identify gene candidates for genome editing as part of Rev’s molecular breeding pipeline.