Blog

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.

Sequencing projects come in many different forms. At Gencove, we’ve seen and supported a broad range of species and sample types. Occasionally, we receive projects that our partner service labs can't support using standard procedures, necessitating bespoke and innovative solutions.

Detecting structural variants in the human genome remains a substantial challenge for most sequencing projects. Most DNA sequencing projects use short-read NGS platforms, making it challenging to accurately resolve long, complex structural mutations. While long-read sequencing platforms are both available and well-suited for structural variant detection, the significant cost of using these platforms has prevented their widespread use.

Effectively we are rapidly approaching a world where sequencing costs per se are irrelevant to companies looking to implement genetic testing at massive scale. The scientific implications of this are fun to consider, but it’s also worth thinking about the overall business implications.