Automating Carrier Screening Analysis Using VarSeq
VarSeq, when used with VSPipeline, automates carrier screening analysis, allowing labs to run them in a parallelized, economical fashion.
Category Archives: Clinical Genetics
VarSeq for Rare Disease Analysis
Streamline rare and undiagnosed disease analysis with VarSeq—NGS software designed to simplify variant interpretation and diagnosis insights.
Exploring Publicly Available NGS Data Sources
Publicly available datasets play a crucial role in research and offer resources for validation and benchmarking of workflows. In this blog, I would like to point out and briefly discuss several notable, publicly available sources of NGS sequencing data. Each of these sources provides validated datasets that are invaluable for laboratories and institutions processing NGS samples. EPI2ME is an advanced… Read more »
The Power of Phased Genotypes in Variant Analysis
Phased genotypes provide crucial information that allows us to separate variants into distinct haplotypes, representing the sequence of alleles inherited together from a single parent. This information can offer profound insights into inheritance patterns, the combined functional effects of variants, and the identification of specific genetic profiles such as pharmacogenomic diplotypes. In this blog post we describe how phased genotypes… Read more »
CNV Specific Updates to VarSeq 2.6.0
The recent release of VarSeq 2.6.0 was filled with so many customer-requested features (for example, our long-awaited PGx workflow!) that some of our other new features have not yet had their time in the spotlight. For this blog, we are thrilled to announce that with the release of VarSeq 2.6.0, we have made WGS CNV calling more user-friendly than ever… Read more »
Computed Fields in VarSeq: The Ultimate Customization Tool
Bioinformatic freedom is core to the user experience with Golden Helix software, a topic with which you will be well-versed if you’ve kept up on our recent blogs. We are constantly endeavoring to provide our users with powerful tools to tackle complex and impactful next-generation sequencing (NGS) workflows while maintaining transparency and the ability to customize and augment each component… Read more »
Using Liftover to Curate GnomAD v4 for GRCh37
We are excited to announce the release of our gnomAD v4 annotation tracks for VarSeq. This version of the GnomAD database represents a significant leap forward, including data from an impressive cohort of over 800,000 individuals — a remarkable 5x expansion compared to the previous releases. Notably, this dataset is comprised of two distinct callsets: exome sequencing data from 730,947… Read more »
Evolution of Multi-Allelic Calls in VarSeq: Before and After Version 2.5.0
It may come as a surprise to our long-standing users that the multi-allelic import and representation in VarSeq is slated to have different import options with the 2.5.0 upgrade. As the software grows and evolves, we strive to meet the changing needs of our users, and this is one area where an update was sorely needed. Before going into an… Read more »
Evaluating DeepMind’s AlphaMissense Classifier
Last month, the researchers at Google DeepMind announced the release of AlphaMissense, a new missense prediction algorithm that leverages the protein structure prediction model AlphaFold to distinguish between benign and pathogenic missense variants (Cheng et al., 2023). AlphaFold is a model for the prediction of protein structures from amino acid sequences. During the development of AlphaMissense, the AlphaFold model was… Read more »
Using Compute Functions to Validate Variant Assessments
Recently, a Golden Helix customer reached out to support for advice on how to validate the variant scoring between two different VarSeq users. Here we break down one possible solution as an opportunity to showcase the utility of both the Latest Sample Assessment function and an alternative way to leverage the Compute Fields function. To set up this validation project,… Read more »
Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer
While the analysis of gene fusions is crucial for understanding the genetic basis of cancer, the process of interpreting these mutations can be challenging. One important component of fusion interpretation is the identification of relevant publications. To aid researchers in the search for publications related to specific gene fusions, Felix Mitelman, and colleagues have created the Mitelman Database of Chromosome… Read more »
Optimizing the Capture of Tier II Evidence in VSClinical AMP
VarSeq 2.3.0 unleashed a whole new way to select, process, analyze, and report cancer variants through complete workflow automation, application of evaluation scripts, and enhanced annotation. Single nucleotide variants, copy number variants, structural variants, and genomic signatures could be added to a single patient evaluation, and Golden Helix CancerKB came packed with new report-ready interpretations to support them! Soon after,… Read more »
When Negative Findings are a Good Thing to Report: Leveraging VarSeq’s VSClinical AMP
Explore the importance of negative findings in genomic medicine through the lens of VarSeq’s VSClinical AMP. When analyzing a somatic sample in VarSeq, users have the option to report on several types of biomarkers with VSClinical AMP. In addition to your usual mutational biomarkers such as small variants, your copy number variants, and structural variants, we support the analysis of… Read more »
Transitioning to Broad-Scale Genomics: From Gene Panels to Whole Genome Sequencing
Explore the evolving landscape of genomic analysis, transitioning from targeted gene panels to whole genome sequencing. A recent trend with our customers has been to expand their workflows from small panel sequencing analyses to larger whole exome and genome sequencing analyses. The decreasing cost of sequencing has made this a rather common request. Although more data allows for a greater… Read more »
Managing the Scope of Somatic Variants Reported in VSClinical
Discover the power of VSClinical’s Interpretation Match Behavior options for managing the scope of somatic variants in cancer reporting, enabling clinical teams to make informed treatment decisions. Multiple interpretations can apply to a single biomarker or tumor type. In some circumstances, a clinical team may only want to report the most relevant and significant biomarker, treatment, diagnosis, or prognosis interpretations… Read more »
Maximizing Structural Variant Detection with Soft Clip Visualization
Discover how soft clip visualization can help you identify structural variants in your research and improve the accuracy of your findings. Soft clipping is a common technique in sequence alignment used to remove bases from the ends of reads that do not align with the reference sequence. Removing these bases typically improves alignment accuracy. However, when multiple reads are soft… Read more »
Decoding Complex Fusion Representations: A Deep Dive into VCF Breakend Notation and its Impact on Gene Analysis
Unlocking the intricacies of fusion representations is crucial for understanding the impact of complex genetic rearrangements on gene function and disease development. In the most recent release of VarSeq, we added support for the import of complex rearrangements from VCF files, which typically encode rearrangements using breakend notation. This powerful notation is capable of describing the full spectrum of structural… Read more »
Unlocking Clinical Evidence: A Deep Dive into the Supporting Data Sources of VSClinical AMP Workflow
Discover the innovative clinical evidence search of the VSClinical AMP workflow, where patient biomarkers and tumor types are matched to the most relevant data from top sources like CIViC, DrugBank, and Clinical Trials. One of the core features of the VSClinical AMP interpretation workflow is the ability to search third-party data sources to find clinical evidence that matches the patient’s… Read more »
Exploring the CRAM File Format in VarSeq 2.3.0
Unlocking the Potential of CRAM Files: The New VarSeq 2.3.0 Release for Enhanced Plotting, Coverage Analysis, and CNV Detection The CRAM (Compressed Reference-oriented Alignment Map) file format was conceived in 2011 as a more space-efficient way to store alignment data. It saves space over the previous standard BAM (Binary Alignment Map) by only storing the differences between each read and… Read more »
Streamlining Variant Analysis for Large Genetic Cohorts: Part 2
The last blog in this series covered streamlining variant analysis for large genetic cohorts, namely case-control studies, on a single-project basis. The reality when dealing with big data is that you often do not handle a high volume project all at once. Therefore, we will follow up on the topic of cohort analysis by discussing Golden Helix’s solution for streamlining… Read more »