Explorer articles

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.

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).

At Gencove, we have repeatedly seen the complexity and insufficiency of existing tools limit research teams from fully unlocking the value of their genetic data. Gencove Explorer analysis cloud represents a full-stack solution that addresses the challenges that exist with currently data solutions, in a single integrated platform and with a minimal learning curve. In doing so, Explorer accelerates the rate at which scientists can answer critical biological questions, and furthers our goal of delivering insights that advance global health and sustainability.