Category Archives: Best practices in genetic analysis

One Track to Rule Them All: Close but not quite from the 1000 Genomes Project

I recently curated the latest population frequency catalog from the 1000 Genomes Project onto our annotation servers, and I had very high hopes for this track. First of all, I applaud 1000 Genomes for the amount of effort they have put in to providing the community with the largest set of high-quality whole genome controls available. My high hopes are… Read more »

Why You Should Care About Segmental Duplications

My work in the GHI analytical services department gives me the opportunity to handle data from a variety of sources.  I have learned over time that every genotyping platform has its own personality.  Every time we get data from a new chip, I tend to learn something new about the quirks of genotyping technology.  I usually discover these quirks the… Read more »

DNA Variant Analysis of Complete Genomics’ Next-Generation Sequencing Data

As I’ve mentioned in previous blog posts, one of the great aspects of our scientific community is the sharing of public data. With a mission of providing powerful and accurate tools to researchers, we at at Golden Helix especially appreciate the value of having rich and extensive public data to test and calibrate those tools. Public data allow us to… Read more »

Best Practices for Incorporating Public Genotype Data in Your Study

The Golden Helix sales team recently came to me for recommendations regarding best practices for incorporating public controls in SNP GWAS.  It seems that there has been a surge of questions regarding this practice over the past few weeks from our customers.  Initially, I laughed at the irony of being asked to outline the best practices for what I see… Read more »

Stop Ignoring Experimental Design (or my head will explode)

Stop Ignoring Experimental Design (or my head will explode)

Download this blog post as a PDF. Over the past 3 years, Golden Helix has analyzed dozens of public and customer whole-genome and candidate gene datasets for a host of studies.  Though genetic research certainly has a number of complexities and challenges, the number one problem we encounter, which also has the greatest repercussions, is born of problematic experimental design…. Read more »