Recently, I have been thinking a lot about Human Genome Variation Society (HGVS) notation — you know “G dot”, “P dot”, and “C dot”. HGVS has quickly become one of the most common ways to represent variants. It’s no wonder that HGVS nomenclature is used so widely. It provides an easily readable, compact representation of a variant. Since it is so prevalent, it’s helpful to take a step back and consider the scenarios where using HGVS nomenclature succeeds and where it fails.
The first version of HGVS was published in 2000 and really preceded the establishment of a human reference sequence. Thus, the nomenclature has the very desirable feature that it can describe variants (in C dot and P dot form) with no knowledge of the reference sequence–only the RNA or amino acid sequence must be known. For some portions of the genome we are lucky enough to have a stable coordinate system called a Locus Reference Genomic (LRG) segment. However, if the variant being examined is outside an LRG the notation is once again implicitly dependent on the reference genome. As I previously wrote, if the transcript is mis-mapped, the C dot notation will be incorrect as well. Continue reading
It’s that time of year again. The mornings are chilly, the leaves are falling, and ASHG is right around the corner. This year will mark my very first ASHG and I am really looking forward to meeting some of the Golden Helix community!
The team has been hard at work preparing for a great conference and I wanted to give you an update with specifics on the Golden Helix events that you won’t want to miss.
First, you will find us in booth 422. Continue reading
The 64th annual ASHG meeting is coming up in just a few short weeks in San Diego. This year’s event will be an exciting one for Golden Helix as we present VarSeq, with the first demonstration on Sunday at 11:30 am in booth 422. After the demonstration we will have some great VarSeq t-shirts to give away – you will not want to miss it! Continue reading
Last week, our CEO Andreas Scherer announced our entrance into the clinical testing market with VarSeq. This week, I will be giving a webcast on Wednesday introducing this new tool and demonstrating its capabilities. (Register for the webcast)
VarSeq’s focused purpose is making NGS gene testing and variant discovery efficient, scalable and accessible to users with a broad range of backgrounds and specialties.
In this blog post, we will examine the use cases that VarSeq supports in more detail, but first I want to place this product launch in the context of Golden Helix’s own evolution as well as the adoption of this technology in our industry.
Although VarSeq is entering a market which has existing solutions, Golden Helix’s years of experience gives VarSeq the advantage of the deepest technological roots.
The adoption of genetic services is key to our ability to provide personalized medicine in the future. The goal is to better diagnose diseases, predict their outcomes, and to choose the best possible care option for a patient. Our part here at Golden Helix is to essentially build the equivalent of an MRI for the genome. In this process the latest research on disorders is combined with our understanding of the best treatment option at any given time.
Next week, on October 1st, we will launch VarSeq in a live webcast.
With ASHG only four weeks away, the hype has only continued to grow. The 64th Annual Meeting of the American Society of Human Genetics is shaping up to be one of the best with some amazing abstracts, including one from our very own Greta Linse Peterson. Greta will be presenting Monday, October 20th in room 20A at 6:15 PM in the Statistical Methods for Population Based Studies session on “A logistic mixed model approach to obtain a reduced model score for KBAC to adjust for population structure and relatedness between samples.” Continue reading
Late last month I had the opportunity to attend one of my favorite events: the annual meeting of the International Genetic Epidemiology Society (IGES). This year’s conference was held in Vienna, in conjunction with the Genetic Analysis Workshop (GAW) and the International Society for Clinical Biostatistics (ICBS). The program at IGES this year was very diverse, with content ranging from Pharmacogenomics to risk prediction to microbiomics and beyond.
The session on risk prediction, held jointly with ICBS on August 28th set the theme of the conference for me. Two talks in particular, by Joan Bailey-Wilson and Bertram Müller-Myhsok, really made me think about what elements are required to successfully implement a predictive model based on genetic data, and I listened to the rest of the conference with this theme in mind. Continue reading
Our 2 SNPs is typically dedicated to informing our customers and the community on the latest in analysis methods, best practices, and the future of the industry. But for this blog post we thought it would be nice to give you the insider’s scoop on our company with a few things you probably didn’t know about us. Continue reading
Tutorials are ever-present in the world today, and for good reason. Why struggle through a complicated process yourself, when there is already a guide established to assist? While no one would suggest that a tutorial is the only way to complete a project, it is certainly a nice starting point.
This rings true with genetic software as well. There are many ways to analyze DNA-Seq and SNP data, but a starting point is helpful. With that in mind, Golden Helix has curated tutorials to help researchers with their analysis, on levels varying from beginner to advanced. Continue reading
SVS offers options for performing many different QC functions on genomic data. This blog takes you through some of the most commonly applied filters for various analysis types.
Filters for GWAS data vary depending on the type of association tests you are performing. A typical GWAS for a common variant usually requires filters to remove problematic or poorly called variants, and also to eliminate rare variants, as they have limited statistical power. The default minor allele frequency (MAF) threshold in SVS is set at 5%, but users may often wish to use lower thresholds (1% or less), especially with larger numbers of samples. The default call rate threshold in SVS is 0.95, but might be adjusted to reflect the call rate which would be considered an outlier in your data. LD pruning to remove correlated SNPs is a good practice prior to running principal components analysis, IBD analysis, or other population-level functions that might be biased by large blocks of redundant SNPs. Most of these functions, together with many others, can be found under the Genotype menu in any SVS spreadsheet. Continue reading