A common classification framework for histone sequence alterations in tumours: an expert consensus proposal.

The description of genetic alterations in tumours is of increasing importance. In human genetics, and in pathology reports, sequence alterations are given using the human genome variation society (HGVS) guidelines for the description of such variants. However, there is less adherence to these guidelines for sequence variations in histone genes. Due to early cleavage of the N-terminal methionine in most histones, the description of histone sequence alterations follows their own nomenclature and differs from the HGVS-compliant numbering by omitting this first amino acid. Next generation sequencing reports, however, follow the HGVS guidelines and as a result, an unambiguous description of sequence variants in histones cannot be provided. The coexistence of these two nomenclatures leads to confusions for pathologists, oncologists, and researchers. This review provides an overview of tumour entities with sequence alterations of the H3-3A gene (HGNC ID = HGNC:4764), highlights the problems associated with the coexistence of these two nomenclatures, and proposes a standard for the reporting of histone sequence variants that allows an unambiguous description of these variants according to HGVS principles. We hope that scientific journals will adopt the new notation, and that both geneticists and pathologists will include it in their reports. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Verifying nomenclature of DNA variants in submitted manuscripts: Guidance for journals.

Documenting variation in our genomes is important for research and clinical care. Accuracy in the description of DNA variants is therefore essential. To address this issue, the Human Variome Project convened a committee to evaluate the feasibility of requiring authors to verify that all variants submitted for publication complied with a widely accepted standard for description. After a pilot study of two journals, the committee agreed that requiring authors to verify that variants complied with Human Genome Variation Society nomenclature is a reasonable step toward standardizing the worldwide inventory of human variation.

VariantValidator: Accurate validation, mapping, and formatting of sequence variation descriptions.

The Human Genome Variation Society (HGVS) variant nomenclature is widely used to describe sequence variants in scientific publications, clinical reports, and databases. However, the HGVS recommendations are complex and this often results in inaccurate variant descriptions being reported. The open-source hgvs Python package (https://github.com/biocommons/hgvs) provides a programmatic interface for parsing, manipulating, formatting, and validating of variants according to the HGVS recommendations, but does not provide a user-friendly Web interface. We have developed a Web-based variant validation tool, VariantValidator (https://variantvalidator.org/), which utilizes the hgvs Python package and provides additional functionality to assist users who wish to accurately describe and report sequence-level variations that are compliant with the HGVS recommendations. VariantValidator was designed to ensure that users are guided through the intricacies of the HGVS nomenclature, for example, if the user makes a mistake, VariantValidator automatically corrects the mistake if it can, or provides helpful guidance if it cannot. In addition, VariantValidator has the facility to interconvert genomic variant descriptions in HGVS and Variant Call Format with a degree of accuracy that surpasses most competing solutions.

hgvs: A Python package for manipulating sequence variants using HGVS nomenclature: 2018 Update.

The Human Genome Variation Society (HGVS) nomenclature guidelines encourage the accurate and standard description of DNA, RNA, and protein sequence variants in public variant databases and the scientific literature. Inconsistent application of the HGVS guidelines can lead to misinterpretation of variants in clinical settings. Reliable software tools are essential to ensure consistent application of the HGVS guidelines when reporting and interpreting variants. We present the hgvs Python package, a comprehensive tool for manipulating sequence variants according to the HGVS nomenclature guidelines. Distinguishing features of the hgvs package include: (1) parsing, formatting, validating, and normalizing variants on genome, transcript, and protein sequences; (2) projecting variants between aligned sequences, including those with gapped alignments; (3) flexible installation using remote or local data (fully local installations eliminate network dependencies); (4) extensive automated tests; and (5) open source development by a community from eight organizations worldwide. This report summarizes recent and significant updates to the hgvs package since its original release in 2014, and presents results of extensive validation using clinical relevant variants from ClinVar and HGMD.

LSDBs and How They Have Evolved.

Locus specific databases (LSDBs) make a key contribution to our understanding of heritable and acquired human disorders, disease susceptibility, and adverse drug reactions. As data have accumulated in LSDBs, a greater reliance on their use has arisen in clinical practice. Even though LSDBs have existed in recognizable form for only a quarter of a century, their origin lies in the manual cataloging of data that began around 50 years ago. Analysis and recording of sequence variation in the globin genes, and the proteins which they encode, can confidently be said to be the foundation for what we now refer to as LSDBs. Their growth over the years has primarily been underpinned by software developments and the advent of the World Wide Web. However, it is also important to recognize the evolution of reporting standards and reference sequences, without which accurate and consistent reporting of sequence variants would be impossible. Nowadays, LSDBs exist for many human protein-coding genes and the focus of efforts has moved toward minor tidying up of the variant reporting nomenclature and processes for assuring the completeness, correctness, and consistency of the data. The next 25 years will doubtless witness further developments in the evolution of LSDBs.

HGVS Recommendations for the Description of Sequence Variants: 2016 Update.

The consistent and unambiguous description of sequence variants is essential to report and exchange information on the analysis of a genome. In particular, DNA diagnostics critically depends on accurate and standardized description and sharing of the variants detected. The sequence variant nomenclature system proposed in 2000 by the Human Genome Variation Society has been widely adopted and has developed into an internationally accepted standard. The recommendations are currently commissioned through a Sequence Variant Description Working Group (SVD-WG) operating under the auspices of three international organizations: the Human Genome Variation Society (HGVS), the Human Variome Project (HVP), and the Human Genome Organization (HUGO). Requests for modifications and extensions go through the SVD-WG following a standard procedure including a community consultation step. Version numbers are assigned to the nomenclature system to allow users to specify the version used in their variant descriptions. Here, we present the current recommendations, HGVS version 15.11, and briefly summarize the changes that were made since the 2000 publication. Most focus has been on removing inconsistencies and tightening definitions allowing automatic data processing. An extensive version of the recommendations is available online, at http://www.HGVS.org/varnomen.

Locus Reference Genomic: reference sequences for the reporting of clinically relevant sequence variants.

Locus Reference Genomic (LRG; http://www.lrg-sequence.org/) records contain internationally recognized stable reference sequences designed specifically for reporting clinically relevant sequence variants. Each LRG is contained within a single file consisting of a stable 'fixed' section and a regularly updated 'updatable' section. The fixed section contains stable genomic DNA sequence for a genomic region, essential transcripts and proteins for variant reporting and an exon numbering system. The updatable section contains mapping information, annotation of all transcripts and overlapping genes in the region and legacy exon and amino acid numbering systems. LRGs provide a stable framework that is vital for reporting variants, according to Human Genome Variation Society (HGVS) conventions, in genomic DNA, transcript or protein coordinates. To enable translation of information between LRG and genomic coordinates, LRGs include mapping to the human genome assembly. LRGs are compiled and maintained by the National Center for Biotechnology Information (NCBI) and European Bioinformatics Institute (EBI). LRG reference sequences are selected in collaboration with the diagnostic and research communities, locus-specific database curators and mutation consortia. Currently >700 LRGs have been created, of which >400 are publicly available. The aim is to create an LRG for every locus with clinical implications.

Cafe Variome: general-purpose software for making genotype-phenotype data discoverable in restricted or open access contexts.

Biomedical data sharing is desirable, but problematic. Data "discovery" approaches-which establish the existence rather than the substance of data-precisely connect data owners with data seekers, and thereby promote data sharing. Cafe Variome (http://www.cafevariome.org) was therefore designed to provide a general-purpose, Web-based, data discovery tool that can be quickly installed by any genotype-phenotype data owner, or network of data owners, to make safe or sensitive content appropriately discoverable. Data fields or content of any type can be accommodated, from simple ID and label fields through to extensive genotype and phenotype details based on ontologies. The system provides a "shop window" in front of data, with main interfaces being a simple search box and a powerful "query-builder" that enable very elaborate queries to be formulated. After a successful search, counts of records are reported grouped by "openAccess" (data may be directly accessed), "linkedAccess" (a source link is provided), and "restrictedAccess" (facilitated data requests and subsequent provision of approved records). An administrator interface provides a wide range of options for system configuration, enabling highly customized single-site or federated networks to be established. Current uses include rare disease data discovery, patient matchmaking, and a Beacon Web service.

The Human Variome Project: ensuring the quality of DNA variant databases in inherited renal disease.

A recent review identified 60 common inherited renal diseases caused by DNA variants in 132 different genes. These diseases can be diagnosed with DNA sequencing, but each gene probably also has a thousand normal variants. Many more normal variants have been characterised by individual laboratories than are reported in the literature or found in publicly accessible collections. At present, testing laboratories must assess each novel change they identify for pathogenicity, even when this has been done elsewhere previously, and the distinction between normal and disease-associated variants is particularly an issue with the recent surge in exomic sequencing and gene discovery projects. The Human Variome Project recommends the establishment of gene-specific DNA variant databases to facilitate the sharing of DNA variants and decisions about likely disease causation. Databases improve diagnostic accuracy and testing efficiency, and reduce costs. They also help with genotype-phenotype correlations and predictive algorithms. The Human Variome Project advocates databases that use standardised descriptions, are up-to-date, include clinical information and are freely available. Currently, the genes affected in the most common inherited renal diseases correspond to 350 different variant databases, many of which are incomplete or have insufficient clinical details for genotype-phenotype correlations. Assistance is needed from nephrologists to maximise the usefulness of these databases for the diagnosis and management of inherited renal disease.

Variobox: automatic detection and annotation of human genetic variants.

Triggered by the sequencing of the human genome, personalized medicine has been one of the fastest growing research areas in the last decade. Multiple software and hardware technologies have been developed by several projects, culminating in the exponential growth of genetic data. Considering the technological developments in this field, it is now fairly easy and inexpensive to obtain genetic profiles for unique individuals, such as those performed by several genetic analysis companies. The availability of computational tools that simplify genetic data analysis and the disclosure of biomedical evidences are of utmost importance. We present Variobox, a desktop tool to annotate, analyze, and compare human genes. Variobox obtains variant annotation data from WAVe, protein metadata annotations from Protein Data Bank, and sequences are obtained from Locus Reference Genomic or RefSeq databases. To explore the data, Variobox provides an advanced sequence visualization that enables agile navigation through genetic regions. DNA sequencing data can be compared with reference sequences retrieved from LRG or RefSeq records, identifying and automatically annotating new potential variants. These features and data, ranging from patient sequences to HGVS-compliant variant descriptions, are combined in an intuitive interface to analyze genes and variants. Variobox is a Java application, available at http://bioinformatics.ua.pt/variobox.

A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery.

The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present phenopacket-store. Version 0.1.12 of phenopacket-store includes 4916 phenopackets representing 277 Mendelian and chromosomal diseases associated with 236 genes, and 2872 unique pathogenic alleles curated from 605 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.

VarioML framework for comprehensive variation data representation and exchange.

BACKGROUND: Sharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement. RESULTS: The GEN2PHEN project addressed these difficulties by developing a comprehensive data model for capturing biomedical observations, Observ-OM, and building the VarioML format around it. VarioML pairs a simplified open specification for describing variants, with a toolkit for adapting the specification into one's own research workflow. Straightforward variant data can be captured, federated, and exchanged with no overhead; more complex data can be described, without loss of compatibility. The open specification enables push-button submission to gene variant databases (LSDBs) e.g., the Leiden Open Variation Database, using the Cafe Variome data publishing service, while VarioML bidirectionally transforms data between XML and web-application code formats, opening up new possibilities for open source web applications building on shared data. A Java implementation toolkit makes VarioML easily integrated into biomedical applications. VarioML is designed primarily for LSDB data submission and transfer scenarios, but can also be used as a standard variation data format for JSON and XML document databases and user interface components. CONCLUSIONS: VarioML is a set of tools and practices improving the availability, quality, and comprehensibility of human variation information. It enables researchers, diagnostic laboratories, and clinics to share that information with ease, clarity, and without ambiguity.

Guidelines for establishing locus specific databases.

Information about genetic variation has been collected for some 20 years into registries, known as locus specific databases (LSDBs), which nowadays often contain information in addition to the actual genetic variation. Several issues have to be taken into account when considering establishing and maintaining LSDBs and these have been discussed previously in a number of articles describing guidelines and recommendations. This information is widely scattered and, for a newcomer, it would be difficult to obtain the latest information and guidance. Here, a sequence of steps essential for establishing an LSDB is discussed together with guidelines for each step. Curators need to collect information from various sources, code it in systematic way, and distribute to the research and clinical communities. In doing this, ethical issues have to be taken into account. To facilitate integration of information to, for example, analyze genotype-phenotype correlations, systematic data representation using established nomenclatures, data models, and ontologies is essential. LSDB curation and maintenance comprises a number of tasks that can be managed by following logical steps. These resources are becoming ever more important and new curators are essential to ensure that we will have expertly curated databases for all disease-related genes in the near future.

Curating gene variant databases (LSDBs): toward a universal standard.

Gene variant databases or Locus-Specific DataBases (LSDBs) are used to collect and display information on sequence variants on a gene-by-gene basis. Their most frequent use is in relation to DNA-based diagnostics, giving clinicians and scientists easy access to an up-to-date overview of all gene variants identified worldwide and whether they influence the function of the gene ("pathogenic or not"). While literature on gene variant databases is extensive, little has been published on the process of database curation itself. Based on our extensive experience as LSDB curators and our contributions to database curation courses, we discuss the subject of database curation. We describe the tasks involved, the steps to take, and the issues that might occur. Our overview is a first step toward establishing overall guidelines for database curation and ultimately covers one aspect of establishing quality-assured gene variant databases.

Solving bottlenecks in data sharing in the life sciences.

The joint Open PHACTS/GEN2PHEN workshop on "Solving Bottlenecks in Data Sharing in the Life Sciences" was held in Volendam, the Netherlands, on September 19 and 20, 2011, and was attended by representatives from academia, industry, publishing, and funding agencies. The aim of the workshop was to explore the issues that influence the extent to which data in the life sciences are shared, and to explore sustainability scenarios that would enable and promote "open" data sharing. Several key challenges were identified and solutions to each of these were proposed.

Dissecting the phenotypic variability of osteogenesis imperfecta.

Osteogenesis imperfecta (OI) is a heterogeneous family of collagen type I-related diseases characterized by bone fragility. OI is most commonly caused by single-nucleotide substitutions that replace glycine residues or exon splicing defects in the COL1A1 and COL1A2 genes that encode the α1(I) and α2(I) collagen chains. Mutant collagen is partially retained intracellularly, impairing cell homeostasis. Upon secretion, it assembles in disorganized fibrils, altering mineralization. OI is characterized by a wide range of clinical outcomes, even in the presence of identical sequence variants. Given the heterotrimeric nature of collagen I, its amino acid composition and the peculiarity of its folding, several causes may underlie the phenotypic variability of OI. A deep analysis of entries regarding glycine and splice site collagen substitution of the largest publicly available patient database reveals a higher risk of lethal phenotype for carriers of variants in α1(I) than in α2(I) chain. However, splice site variants are predominantly associated with lethal phenotype when they occur in COL1A2. In addition, lethality is increased when mutations occur in regions of importance for extracellular matrix interactions. Both extracellular and intracellular determinants of OI clinical severity are discussed in light of the findings from in vitro and in vivo OI models. Combined with meticulous tracking of clinical cases via a publicly available database, the available OI animal models have proven to be a unique tool to shed light on new modulators of phenotype determination for this rare heterogeneous disease.

WAVe: web analysis of the variome.

DNA sequence variation is the underlying basis of common human traits and rarer single-gene disorders. Understanding the variome, the variants in an individual's genome, is essential to enable the ultimate goals of personalized medicine. This critical research field has grown dramatically in recent years, mostly due to the spread and development of genotyping technologies. Despite these activities being promoted by the Human Genome Variation Society and projects such as the Human Variome Project or the European GEN2PHEN Project, variome data-integration systems are far from being widely used in the research community workflow. Most of ongoing research is focused on improving locus-specific databases. Although the quality and manual curation of LSDBs adds true value to this domain, they are often narrow, heterogeneous, and independent systems. This hampers data harmonization and interoperability between systems, stifling the aggregation of data from LSDBs and related data sources. A new platform entitled Web Analysis of the Variome, WAVe, is introduced. It offers direct and programmatic access to multiple locus-specific databases, with the integration of genetic variation datasets and enrichment with relevant information. WAVe's agile and innovative Web interface is accessible at http://bioinformatics.ua.pt/WAVe.

HUGO Gene Nomenclature Committee (HGNC) recommendations for the designation of gene fusions.

Gene fusions have been discussed in the scientific literature since they were first detected in cancer cells in the early 1980s. There is currently no standardized way to denote the genes involved in fusions, but in the majority of publications the gene symbols in question are listed either separated by a hyphen (-) or by a forward slash (/). Both types of designation suffer from important shortcomings. HGNC has worked with the scientific community to determine a new, instantly recognizable and unique separator-a double colon (::)-to be used in the description of fusion genes, and advocates its usage in all databases and articles describing gene fusions.

The Human Variome Project (HVP) 2009 Forum "Towards Establishing Standards".

The May 2009 Human Variome Project (HVP) Forum "Towards Establishing Standards" was a round table discussion attended by delegates from groups representing international efforts aimed at standardizing several aspects of the HVP: mutation nomenclature, description and annotation, clinical ontology, means to better characterize unclassified variants (UVs), and methods to capture mutations from diagnostic laboratories for broader distribution to the medical genetics research community. Methods for researchers to receive credit for their effort at mutation detection were also discussed.