NGS methodologies like WGS and WES allow us to search for clinically significant gene variations and access detailed genetic information. This vast increase in genetic data from NGS poses new challenges, though. Classifying, annotating and understanding the clinical relevance of all this genetic information is time-consuming and requires expertise.
Undoubtedly, automatic data collection tools that machine learning and artificial intelligence can aid in retrieving variant evidence from literature. But the presence of false positives, incompletely described variants and alternative variant nomenclature, among other variability, make it hard for automated text mining approaches to parse and translate accurately. Consequently, additional scrutiny and caution in the form of expert manual curation is essential.
Drawing from Stanford University-sponsored literature, this whitepaper explores Stanford’s comparison of the data quality from their Automatic Variant evidence Database (AVADA) to the Human Gene Mutation Database (HGMD).
When a family has a child with a rare undiagnosed condition or a couple is planning their next chapter, they want assurance that their doctors are considering every peer-reviewed paper and all available evidence in their quest for an answer.
HGMD Professional remains the largest, manually curated resource for finding disease-causing mutations. Founded and maintained by the Institute of Medical Genetics at Cardiff University, the database attempts to collate all known (published) gene lesions responsible for human inherited disease, giving you the best possible chance of reaching a diagnosis. Learn more >
All data is based on published, peer-reviewed literature that has been manually curated and evaluated for accuracy.
Every quarter, HGMD Professional content and functionality is updated to ensure you remain informed on the latest findings.
For certain mutations, HGMD Professional includes summaries of disease-associated/functional polymorphisms.
HGMD is powered by a team of expert curators at Cardiff University. Data are collected weekly by a combination of manual and computerized search procedures. In excess of 250 journals are scanned for articles describing germline mutations causing human genetic disease. The required data are extracted from the original articles and augmented with the necessary supporting data.
The number of disease-associated germline mutations published per year has more than doubled in the past decade (Figure 1). As rare and novel genetic mutations continue to be uncovered, having access to the latest scientific evidence is critical for timely interpretations of next-generation sequencing (NGS) data.
Figure 1. Mutation entries in HGMD Professional. The number of germline mutations published per year has more than doubled since 2010.
In 2008, Christian Millare had a severe seizure and died. He was two years old. His mother was convinced that based on his medical records, the opinions of experts, and the published literature, her son’s life didn’t have to come to such a premature end.
And she was right. In 2017, Christian had a battery of tests, including the sequencing of the SCN1A gene. The lab that performed this genetic test reported that he had a variant of unknown significance (VUS) there.
However, that same SCN1A mutation had been identified in an Australian family in 2006. The lab had failed to find the one publication that could have saved Christian’s life.
Numerous free or open source variant annotation tools are available today to extract, annotate and analyze the genomes and their identified variants coming from NGS methods.
However, the value derived from variant annotation is directly related to the information resource selected for annotation. In this technical note, we provide a guide for using HGMD data with three tools: ANNOVAR, snpEff, and VariantAnnotation (Bioconductor).
If you currently use the public version of HGMD, there is a lot of content that you are missing. Not only is the public version of the database three years behind in terms of published mutation entries, it lacks a multitude of search features critical to elucidating clinically significant associations.
For example, only in HGMD Professional can you search for a mutation by chromosome location, gene ontology or phenotype. But that’s not all. See the full comparison between the public and professional versions of HGMD below.
For clinical labs looking to expand into hereditary disease testing, QIAGEN Clinical Insight (QCI) Interpret reproducibly translates highly complex NGS data into standardized reports using current clinical evidence from the QIAGEN Knowledge Base, which consists of over 40 public and proprietary databases, including HGMD Professional.
QCI Interpret for Hereditary Diseases delivers manually curated evidence directly to your pipeline. You receive links to all articles, auto-computed ACMG/AMP classifications, and access to over 1 million unpublished variant-phenotype relationships from the QIAGEN Knowledge Base.
Download this brochure to learn more about HGMD Professional, with detailed information on use-cases, applications, and customer testimonials.
Find out how scientists at Rockefeller University are using HGMD to rapidly sort through exome data and find disease-causing mutations.
Through step-by-step tutorials, we show you how to use a multitude of search functions only available in HGMD Professional.
Expand your clinical interpretation with expert-curated software for variant classification of any assay, covering any indication, on your sequencing platform
Reimagine your clinical interpretation with same day, expert variant classification services tailored to your oncology panel
Leverage the benefits of automation and expert support to improve test turnaround times and clinical reporting capabilities