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Whole exome sequencing (WES) is a popular and cost-effective method for capturing high quality genotype calls at the protein coding portion of the genome, and has been key in facilitating the discovery of functionally relevant variants across a range of research and clinical settings. Despite this, WES is designed to only capture exonic regions, meaning that the yield of on-target calls obtained through this technology is typically restricted to only around 1% of the genome in total. This has the potential to hamper discovery efforts that solely rely on WES, as it means that the intronic and intergenic fraction of the genome, which comprise the majority, remain un-assayed. Notably, over 90% of signals discovered through Genome Wide Association Studies (GWAS) fall within non-coding regions of the genome, and many analytical methods in the field of genomics rely on dense sampling of genotypes across samples genome-wide for statistical inference. Consequently, researchers who choose to employ WES for large-scale discovery efforts often have to supplement exome sequencing with array-based genotyping technologies in order to capture a more complete picture of their study population. 

RNA-sequencing has revolutionized human genomics by offering insights into gene expression and the broader transcriptomic landscape. This powerful technology is particularly impactful in fields such as oncology, drug development, and personalized medicine, where it allows researchers to identify biomarkers and develop targeted therapies by measuring gene expression levels and detecting RNA transcript mutations. 

Dogs were domesticated from wolves around 20,000 years ago. In the last 150 years, we’ve gone even further; with selective breeding programs, humans have slowly changed the genome of our canine companions. These genomic changes are the foundation of the behavioral and health traits that we know and love, and with genomic technologies, we have the opportunity to directly map those changes. Now that this technology is affordable, we at Gencove believe that every pet deserves to have its genome inform all aspects of their care, from breeding to nutrition to preventative wellness. 

The tools available to scientists and clinicians in the field of genetics are evolving rapidly. While genotyping microarrays once dominated the field, next-generation sequencing (NGS) technologies are becoming increasingly popular. This is particularly true among researchers studying pharmacogenomics (PGx). In recent years, the number of variants linked to an altered drug response has grown rapidly and consequently put a spotlight on the critical failings of microarrays.

In this blog we will demonstrate how to use Gencove Explorer to perform genomic prediction in maize. Specifically, we will be building a genomic prediction model for maize grain yield. Genomic prediction is a technique from statistical genetics that uses genome-wide genetic markers to estimate the breeding value (sometimes referred to as genetic merit) of plants or animals in modern breeding programs. This estimated breeding value (EBV) can be used to identify and selectively breed the best individuals in the population in order to improve agronomically relevant traits.

In the following blog, we will demonstrate how to perform analyses of population scale genomic data using Gencove Explorer. Broadly, we will explore fine-scale population structure in the form of rare variant sharing between samples deriving from the high-depth whole genome sequencing (WGS) dataset of the combined 1000 Genomes Project (1KG) and Human Genome Diversity Panel (HGDP) released as part of gnomAD v3.2.1.

In recent years, the scientific community has made remarkable strides in genomics, transforming our understanding of the genetic makeup of various organisms. Among these, honey bees (Apis mellifera) hold a special place due to their crucial role in agriculture. Despite this progress, decoding the genomic variation in honey bees presents unique challenges, with high recombination rates and large genomic variation.

How we think about AI strategy and products, while attempting to separate utility from hype.

The recent release of Whole Genome Sequencing (WGS) data for 490,640 participants in the UK Biobank (UKBB) has presented researchers with opportunities for comprehensive assessment of genomic risk factors underlying disease at an unprecedented resolution and scale. With the release of this data, we wanted to explore two traits of interest, Body Mass Index (BMI) and Type II Diabetes (T2D), to understand how increased resolution on genome-wide associations for these traits.

Gencove Explorer provides robust support for several data export methods, making use of the Explorer SDK to facilitate seamless and efficient data transfer across various environments and storage solutions. In this blog, we’ll explore various methods of exporting data from Gencove's systems, covering how to generate pre-signed URLs for sample deliverables, and how to export data deliverables to AWS S3, Microsoft Azure, and Google Cloud Storage (GCP).

Investigating the genetic basis of complex human traits is a pivotal endeavor in modern biomedical research. In a recent study, researchers at the Weizmann Institute of Science delved into a deep repository of clinical and genetic data to unravel the genetic associations across a range of disease phenotypes.

Dog research is at a crossroads. Like in humans, genetic tools could revolutionize veterinary care and improve the health and welfare of all dogs, but widely used genetic testing services don’t provide the information needed by researchers to achieve this vision. A collaboration between Gencove, genomic scientists, and dog breeders has developed a new solution that combines high-accuracy genetic testing with comprehensive whole genome genotyping. This dual-purpose product supports breed ancestry analysis, Mendelian disease testing, and large-scale genomics.