A Python package for parsing, validating, mapping and formatting sequence variants using HGVS nomenclature

SeqCAT: Sequence Conversion and Analysis Toolbox

Dealing with sequence coordinates in different formats and reference genomes is challenging in genetic research. This complexity arises from the need to convert and harmonize datasets of different sources using alternating nomenclatures. Since manual processing is time-consuming and requires specialized knowledge, the Sequence Conversion and Analysis Toolbox (SeqCAT) was developed for daily work with genetic datasets. Our tool provides a range of functions designed to standardize and convert gene variant coordinates based on various sequence types. Its user-friendly web interface provides easy access to all functionalities, while the Application Programming Interface (API) enables automation within pipelines. SeqCAT provides access to human genomic, protein and transcript data, utilizing various data resources and packages and extending them with its own unique features. The platform covers a wide range of genetic research needs with its 14 different applications and 3 info points, including search for transcript and gene information, transition between reference genomes, variant mapping, and genetic event review. Notable examples are 'Convert Protein to DNA Position' for translation of amino acid changes into genomic single nucleotide variants, or 'Fusion Check' for frameshift determination in gene fusions. SeqCAT is an excellent resource for converting sequence coordinate data into the required formats and is available at: https://mtb.bioinf.med.uni-goettingen.de/SeqCAT/.

REEV: review, evaluate and explain variants

In the era of high throughput sequencing, special software is required for the clinical evaluation of genetic variants. We developed REEV (Review, Evaluate and Explain Variants), a user-friendly platform for clinicians and researchers in the field of rare disease genetics. Supporting data was aggregated from public data sources. We compared REEV with seven other tools for clinical variant evaluation. REEV (semi-)automatically fills individual ACMG criteria facilitating variant interpretation. REEV can store disease and phenotype data related to a case to use these for phenotype similarity measures. Users can create public permanent links for individual variants that can be saved as browser bookmarks and shared. REEV may help in the fast diagnostic assessment of genetic variants in a clinical as well as in a research context. REEV (https://reev.bihealth.org/) is free and open to all users and there is no login requirement.

Systematic errors in annotations of truncations, loss-of-function and synonymous variants

Description of genetic phenomena and variations requires exact language and concepts. Vast amounts of variation data are produced with next-generation sequencing pipelines. The obtained variations are automatically annotated, e.g., for their functional consequences. These tools and pipelines, along with systematic nomenclature, mainly work well, but there are still some problems in nomenclature, organization of some databases, misuse of concepts and certain practices. Therefore, systematic errors prevent correct annotation and often preclude further analysis of certain variation types. Problems and solutions are described for presumed protein truncations, variants that are claimed to be of loss-of-function based on the type of variation, and synonymous variants that are not synonymous and lead to sequence changes or to missing protein.

When a Synonymous Variant Is Nonsynonymous

Term synonymous variation is widely used, but frequently in a wrong or misleading meaning and context. Twenty three point eight % of possible nucleotide substitution types in the universal genetic code are for synonymous amino acid changes, but when these variants have a phenotype and functional effect, they are very seldom synonymous. Such variants may manifest changes at DNA, RNA and/or protein levels. Large numbers of variations are erroneously annotated as synonymous, which causes problems e.g., in clinical genetics and diagnosis of diseases. To facilitate precise communication, novel systematics and nomenclature are introduced for variants that when looking only at the genetic code seem like synonymous, but which have phenotypes. A new term, unsense variant is defined as a substitution in the mRNA coding region that affects gene expression and protein production without introducing a stop codon in the variation site. Such variants are common and need to be correctly annotated. Proper naming and annotation are important also to increase awareness of these variants and their consequences.

A performance evaluation study: Variant annotation tools - The enigma of clinical next generation sequencing (NGS) based genetic testing

Dramatically expanding our ability for clinical genetic testing for inherited conditions and complex diseases such as cancer, next generation sequencing (NGS) technologies are allowing for rapid interrogation of thousands of genes and identification of millions of variants. Variant annotation, the process of assigning functional information to DNA variants based on the standardized Human Genome Variation Society (HGVS) nomenclature, is a fundamental challenge in the analysis of NGS data that has led to the development of many bioinformatic algorithms. In this study, we evaluated the performance of 3 variant annotation tools: Alamut® Batch, Ensembl Variant Effect Predictor (VEP), and ANNOVAR, benchmarked by a manually curated ground-truth set of 298 variants from the medical exome database at the Molecular Diagnostics Laboratory at Lurie Children's Hospital. Of the 3 tools, VEP produces the most accurate variant annotations (HGVS nomenclature for 297 of the 298 variants) due to usage of updated gene transcript versions within the algorithm. Alamut® Batch called 296 of the 298 variants correctly; strikingly, ANNOVAR exhibited the greatest number of discrepancies (20 of the 298 variants, 93.3% concordance with ground-truth set). Adoption of validated methods of variant annotation is critical in post-analytical phases of clinical testing.

VariCarta: A Comprehensive Database of Harmonized Genomic Variants Found in Autism Spectrum Disorder Sequencing Studies

Recent years have seen a boom in the application of the next-generation sequencing technology to the study of human disorders, including Autism Spectrum Disorder (ASD), where the focus has been on identifying rare, possibly causative genomic variants in ASD individuals. Because of the high genetic heterogeneity of ASD, a large number of subjects is needed to establish evidence for a variant or gene ASD-association, thus aggregating data across cohorts and studies is necessary. However, methodological inconsistencies and subject overlap across studies complicate data aggregation. Here we present VariCarta, a web-based database developed to address these challenges by collecting, reconciling, and consistently cataloging literature-derived genomic variants found in ASD subjects using ongoing semi-manual curation. The careful manual curation combined with a robust data import pipeline rectifies errors, converts variants into a standardized format, identifies and harmonizes cohort overlaps, and documents data provenance. The harmonization aspect is especially important since it prevents the potential double counting of variants, which can lead to inflation of gene-based evidence for ASD-association. The database currently contains 170,416 variant events from 10,893 subjects, collected across 61 publications, and reconciles 16,202 variants that have been reported in literature multiple times. VariCarta is freely accessible at http://varicarta.msl.ubc.ca. Autism Res 2019, 12: 1728-1736. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: The search for genetic factors underlying Autism Spectrum Disorder (ASD) yielded numerous studies reporting potentially causative genomic variants found in ASD individuals. However, methodological differences and subject overlap across studies complicate the assembly of these data, diminishing its utility and accessibility. We developed VariCarta, a web-based database that aggregates carefully curated, annotated, and harmonized literature-derived variants identified in individuals with ASD using ongoing semi-manual curation.

Pathogenic Abnormal Splicing Due to Intronic Deletions that Induce Biophysical Space Constraint for Spliceosome Assembly

A precise genetic diagnosis is the single most important step for families with genetic disorders to enable personalized and preventative medicine. In addition to genetic variants in coding regions (exons) that can change a protein sequence, abnormal pre-mRNA splicing can be devastating for the encoded protein, inducing a frameshift or in-frame deletion/insertion of multiple residues. Non-coding variants that disrupt splicing are extremely challenging to identify. Stemming from an initial clinical discovery in two index Australian families, we define 25 families with genetic disorders caused by a class of pathogenic non-coding splice variant due to intronic deletions. These pathogenic intronic deletions spare all consensus splice motifs, though they critically shorten the minimal distance between the 5' splice-site (5'SS) and branchpoint. The mechanistic basis for abnormal splicing is due to biophysical constraint precluding U1/U2 spliceosome assembly, which stalls in A-complexes (that bridge the 5'SS and branchpoint). Substitution of deleted nucleotides with non-specific sequences restores spliceosome assembly and normal splicing, arguing against loss of an intronic element as the primary causal basis. Incremental lengthening of 5'SS-branchpoint length in our index EMD case subject defines 45-47 nt as the critical elongation enabling (inefficient) spliceosome assembly for EMD intron 5. The 5'SS-branchpoint space constraint mechanism, not currently factored by genomic informatics pipelines, is relevant to diagnosis and precision medicine across the breadth of Mendelian disorders and cancer genomics.

[Detection and interpretation of somatic variants in molecular pathology]

BACKGROUND

Due to the increasing amount of data and sources of information, data evaluation is a crucial step in parallel sequencing.

OBJECTIVES

Illustration of pitfalls in evaluating the variant list of parallel sequencing and recommendations regarding software tools and databases.

METHODS

Description of filtering steps used, demonstration of criteria and recommendations for annotation by examples from everyday work, comparative analysis of databases with somatic variants, description of the installation of an individualized database.

RESULTS

Variant filtering is a multistep process using information from different databases. The plausibility of variant calling should be verified using the Integrative Genomics Viewer and variants should be described according to the Human Genome Variation Society (HGVS) recommendations. Different databases, which all show advantages and disadvantages, are available for variant interpretation. An individualized database can be built up with the open-source tool cBioPortal.

CONCLUSIONS

Different tools and databases might be used for the analysis of parallel sequencing data. The application depends on, amongst other things, the local situation and has to be extensively validated.

GLIS Rearrangement is a Genomic Hallmark of Hyalinizing Trabecular Tumor of the Thyroid Gland

BACKGROUND

Hyalinizing trabecular tumor (HTT) is a rare thyroid neoplasm with a characteristic trabecular growth pattern and hyalinization. This lesion has been the subject of long-term controversy surrounding its genetic mechanisms, relationship to papillary thyroid carcinoma (PTC), and malignant potential. Due to the presence of nuclear features shared with PTC, HTT frequently contributes to a false-positive cytology, which hampers patient management. The goal of this study was to apply genome-wide sequencing analyses to elucidate the genetic mechanisms of HTT and its relationship to PTC.

METHODS

Whole-exome, RNA-Seq, and targeted next-generation sequencing analyses were performed to discover and characterize driver mutations in HTT. RNA-Seq results were used for pathway analysis. Tissue expression of GLIS3 and other proteins was detected by immunohistochemistry. The prevalence of GLIS fusions was studied in 17 tumors initially diagnosed as HTT, 220 PTC, and 10,165 thyroid fine-needle aspiration samples.

RESULTS

Using whole-exome and RNA-Seq analyses of the initial three HTT, no known thyroid tumor mutations were identified, while in-frame gene fusion between PAX8 exon 2 and GLIS3 exon 3 was detected in all tumors. Further analysis identified PAX8-GLIS3 in 13/14 (93%) and PAX8-GLIS1 in 1/14 (7%) of HTT confirmed after blind pathology review. The fusions were validated by Sanger sequencing and FISH. The fusions resulted in overexpression of the 3'-portion of GLIS3 and GLIS1 mRNA containing intact DNA-binding domains of these transcription factors and upregulation of extracellular matrix genes including collagen IV. Immunohistochemistry confirmed upregulation and deposition of collagen IV and pan-collagen in HTT. The analysis of 220 PTC revealed no PAX8-GLIS3 and one PAX8-GLIS1 fusion. PAX8-GLIS3 was prospectively identified in 8/10,165 (0.1%) indeterminate cytology fine-needle aspiration samples; 5/5 resected fusion-positive nodules were HTT on surgical pathology.

CONCLUSIONS

This study demonstrates that GLIS rearrangements, particularly PAX8-GLIS3, are highly prevalent in HTT but not in PTC. The fusions lead to overexpression of GLIS, upregulation of extracellular matrix genes, and deposition of collagens, which is a characteristic histopathologic feature of HTT. Due to unique genetic mechanisms and an indolent behavior, it is proposed to rename this tumor as "GLIS-rearranged hyalinizing trabecular adenoma."

BRCA Challenge: BRCA Exchange as a global resource for variants in BRCA1 and BRCA2

The BRCA Challenge is a long-term data-sharing project initiated within the Global Alliance for Genomics and Health (GA4GH) to aggregate BRCA1 and BRCA2 data to support highly collaborative research activities. Its goal is to generate an informed and current understanding of the impact of genetic variation on cancer risk across the iconic cancer predisposition genes, BRCA1 and BRCA2. Initially, reported variants in BRCA1 and BRCA2 available from public databases were integrated into a single, newly created site, www.brcaexchange.org. The purpose of the BRCA Exchange is to provide the community with a reliable and easily accessible record of variants interpreted for a high-penetrance phenotype. More than 20,000 variants have been aggregated, three times the number found in the next-largest public database at the project’s outset, of which approximately 7,250 have expert classifications. The data set is based on shared information from existing clinical databases—Breast Cancer Information Core (BIC), ClinVar, and the Leiden Open Variation Database (LOVD)—as well as population databases, all linked to a single point of access. The BRCA Challenge has brought together the existing international Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium expert panel, along with expert clinicians, diagnosticians, researchers, and database providers, all with a common goal of advancing our understanding of BRCA1 and BRCA2 variation. Ongoing work includes direct contact with national centers with access to BRCA1 and BRCA2 diagnostic data to encourage data sharing, development of methods suitable for extraction of genetic variation at the level of individual laboratory reports, and engagement with participant communities to enable a more comprehensive understanding of the clinical significance of genetic variation in BRCA1 and BRCA2.

The goal of this study and paper has been to develop an international resource to generate an informed and current understanding of the impact of genetic variation on cancer risk across the cancer predisposition genes, BRCA1 and BRCA2. Reported variants in BRCA1 and BRCA2 available from public databases were integrated into a single, newly created site, www.brcaexchange.org, to provide a reliable and easily accessible record of variants interpreted for a high-penetrance phenotype.

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.

Clinical Implementation and Validation of Automated Human Genome Variation Society (HGVS) Nomenclature System for Next-Generation Sequencing-Based Assays for Cancer

Human Genome Variation Society (HGVS) nomenclature is a de facto clinical standard for reporting DNA sequence variants. With increasing use of high-throughput sequencing, manual generation of HGVS nomenclatures for all variants is impractical and error-prone. It is therefore beneficial to include one or more HGVS generator tools in next-generation sequencing (NGS) bioinformatics pipelines to enable automated, consistent, and accurate generation of HGVS nomenclature after appropriate validation. The authors implemented an HGVS nomenclature tool, the hgvs package, by integrating it into their custom-developed NGS variant management and reporting software. Use of Docker containers provided a strategic advantage to the integration process. Clinical implementation of the hgvs package was validated using a cohort of 330 variants that appropriately represented cancer-related genes and clinically important variant types. The hgvs package was able to generate HGVS-compliant variant nomenclature (both c. and p.) for 308 of the 330 (93.3%) variants, including all those in the coding and untranslated regions, and 32 of 35 (91.4%) in the consensus splice site region. Discrepant HGVS nomenclature involved variants in the intronic (16 of 40) and consensus splice site (3 of 35) regions with repeat sequences. Overall, implementation of the hgvs package in the clinical NGS workflow improved consistency and accuracy of reporting HGVS nomenclature.

Loss of Chromatin-Remodeling Proteins and/or CDKN2A Associates With Metastasis of Pancreatic Neuroendocrine Tumors and Reduced Patient Survival Times

Despite prognostic grading and staging systems, it is a challenge to predict outcomes for patients with pancreatic neuroendocrine tumors (PanNETs). Sequencing studies of PanNETs have identified alterations in death domain-associated protein (DAXX) and alpha-thalassemia/mental retardation X-linked chromatin remodeler (ATRX). In tumors, mutations in DAXX or ATRX and corresponding loss of protein expression correlate with shorter times of disease-free survival and disease-specific survival of patients. However, DAXX or ATRX proteins were lost in only 50% of distant metastases analyzed. We performed whole-exome sequencing analyses of 20 distant metastases from 20 patients with a single nonsyndrome, nonfunctional PanNET. We found distant metastases contained alterations in multiple endocrine neoplasia type 1 (MEN1) (n = 8), ATRX (n = 5), DAXX (n = 5), TSC2 (n = 3), and DEP domain containing 5 (DEPDC5) (n = 3). We found copy number loss of cyclin dependent kinase inhibitor 2A (CDKN2A) in 15 metastases (75%) and alterations in genes that regulate chromatin remodeling, including set domain containing 2 (SETD2) (n = 4), AT-rich interaction domain 1A (ARID1A) (n = 2), chromodomain helicase DNA binding protein 8 (CHD8) (n = 2), and DNA methyl transferase 1 (DNMT1) (n = 2). In a separate analysis of 347 primary PanNETs, we found loss or deletion of DAXX and ATRX, disruption of SETD2 function (based on loss of H3 lysine 36 trimethylation), loss of ARID1A expression or deletions in CDKN2A in 81% of primary PanNETs with distant metastases. Among patients with loss or deletion of at least 1 of these proteins or genes, 39% survived disease-free for 5 years and 44% had disease-specific survival times of 10 years. Among patients without any of these alterations, 98% survived disease-free for 5 years and 95% had disease-specific survival times of 10 years. Therefore, primary PanNETs with loss of DAXX, ATRX, H3 lysine 36 trimethylation, ARID1A, and/or CDKN2A associate with shorter survival times of patients. Our findings indicate that alterations in chromatin-remodeling genes and CDKN2A contribute to metastasis of PanNETs.

KRAS mutation is predictive of outcome in patients with pulmonary sarcomatoid carcinoma

AIMS

Pulmonary sarcomatoid carcinoma (PSC) is a poorly differentiated non-small-cell lung carcinoma (NSCLC) with aggressive behaviour. This study aimed to evaluate the prognostic clinicopathological and genetic characteristics of PSCs.

METHODS AND RESULTS

Fifty-three cases of surgically treated PSCs were selected, 23 of which were subjected to mutation and copy number variation analysis using the 50-gene Ion AmpliSeq Cancer Panel. The majority of the patients were male (32 of 53, 60.3%) and smokers (51 of 53, 96.2%). Overall, 25 (47.1%) patients died within 2-105 months (mean = 22.7 months, median = 15 months) after diagnosis, and 28 were alive 3-141 months (mean = 38.7 months, median = 21.5 months) after diagnosis. Five-year overall survival was 12.5%. KRAS codon 12/13 mutation in adenocarcinomas (P = 0.01), age more than 70 years (P = 0.008) and tumour size ≥4.0 cm (P = 0.02) were associated strongly with worse outcome. TP53 (17 of 23, 74.0%) and KRAS codon 12 of 13 mutations (10 of 23, 43.4%) were the most common genetic alterations. Potentially actionable variants were identified including ATM (four of 23, 17.3%), MET, FBXW7 and EGFR (two of 23, 8.7%), AKT1, KIT, PDGFRA, HRAS, JAK3 and SMAD4 (one of 23, 4.3%). MET exon 14 skipping and missense mutations were identified in two (11.1%) cases with adenocarcinoma histology. Copy number analysis showed loss of RB1 (three of 23, 13%) and ATM (two of 23, 8.7%). Copy number gains were seen in EGFR (two of 23, 13.0%) and in one (4.3%) of each PIK3CA, KRAS, MET and STK11.

CONCLUSIONS

Potentially targetable mutations can be identified in a subset of PSC, although most tumours harbour currently untargetable prognostically adverse TP53 and KRAS mutations.

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.

Towards precision medicine

There is great potential for genome sequencing to enhance patient care through improved diagnostic sensitivity and more precise therapeutic targeting. To maximize this potential, genomics strategies that have been developed for genetic discovery - including DNA-sequencing technologies and analysis algorithms - need to be adapted to fit clinical needs. This will require the optimization of alignment algorithms, attention to quality-coverage metrics, tailored solutions for paralogous or low-complexity areas of the genome, and the adoption of consensus standards for variant calling and interpretation. Global sharing of this more accurate genotypic and phenotypic data will accelerate the determination of causality for novel genes or variants. Thus, a deeper understanding of disease will be realized that will allow its targeting with much greater therapeutic precision.

Principles and Recommendations for Standardizing the Use of the Next-Generation Sequencing Variant File in Clinical Settings

A national workgroup convened by the Centers for Disease Control and Prevention identified principles and made recommendations for standardizing the description of sequence data contained within the variant file generated during the course of clinical next-generation sequence analysis for diagnosing human heritable conditions. The specifications for variant files were initially developed to be flexible with regard to content representation to support a variety of research applications. This flexibility permits variation with regard to how sequence findings are described and this depends, in part, on the conventions used. For clinical laboratory testing, this poses a problem because these differences can compromise the capability to compare sequence findings among laboratories to confirm results and to query databases to identify clinically relevant variants. To provide for a more consistent representation of sequence findings described within variant files, the workgroup made several recommendations that considered alignment to a common reference sequence, variant caller settings, use of genomic coordinates, and gene and variant naming conventions. These recommendations were considered with regard to the existing variant file specifications presently used in the clinical setting. Adoption of these recommendations is anticipated to reduce the potential for ambiguity in describing sequence findings and facilitate the sharing of genomic data among clinical laboratories and other entities.

A variant by any name: quantifying annotation discordance across tools and clinical databases

Background

Clinical genomic testing is dependent on the robust identification and reporting of variant-level information in relation to disease. With the shift to high-throughput sequencing, a major challenge for clinical diagnostics is the cross-identification of variants called on their genomic position to resources that rely on transcript- or protein-based descriptions.

Methods

We evaluated the accuracy of three tools (SnpEff, Variant Effect Predictor, and Variation Reporter) that generate transcript and protein-based variant nomenclature from genomic coordinates according to guidelines by the Human Genome Variation Society (HGVS). Our evaluation was based on transcript-controlled comparisons to a manually curated set of 126 test variants of various types drawn from data sources, each with HGVS-compliant transcript and protein descriptors. We further evaluated the concordance between annotations generated by Snpeff and Variant Effect Predictor and those in major germline and cancer databases: ClinVar and COSMIC, respectively.

Results

We find that there is substantial discordance between the annotation tools and databases in the description of insertions and/or deletions. Using our ground truth set of variants, constructed specifically to identify challenging events, accuracy was between 80 and 90% for coding and 50 and 70% for protein changes for 114 to 126 variants. Exact concordance for SNV syntax was over 99.5% between ClinVar and Variant Effect Predictor and SnpEff, but less than 90% for non-SNV variants. For COSMIC, exact concordance for coding and protein SNVs was between 65 and 88% and less than 15% for insertions. Across the tools and datasets, there was a wide range of different but equivalent expressions describing protein variants.

Conclusions

Our results reveal significant inconsistency in variant representation across tools and databases. While some of these syntax differences may be clear to a clinician, they can confound variant matching, an important step in variant classification. These results highlight the urgent need for the adoption and adherence to uniform standards in variant annotation, with consistent reporting on the genomic reference, to enable accurate and efficient data-driven clinical care.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0396-7) contains supplementary material, which is available to authorized users.

Describing Sequence Variants Using HGVS Nomenclature

DNA sequencing is usually performed to determine the sequence of a region of interest or even the entire genome of an individual. After sequencing, the sequence obtained is compared to a reference, all differences (the variants) are recorded, and the possible consequences of the changes identified, on both the RNA and protein level, are predicted. Finally, when available, a database containing previously reported variants is consulted to determine what other studies might have revealed about the variant or other variants in the same sequence (gene) and what the functional and phenotypic consequences were for the individuals carrying the variant.To facilitate the reporting and databasing of variants a standard was developed, the HGVS recommendations for the description of sequence variants. HGVS nomenclature contains specific formats to describe the basic variant types; substitution, deletion, duplication, insertion, inversion, and conversion. The basics of how to apply the recommendations to describe sequence variants will be explained here. An extensive description of the current HGVS guidelines (version 15.11) is available online at http://www.HGVS.org/varnomen .

Sequence Variant Descriptions: HGVS Nomenclature and Mutalyzer

Consistent and unambiguous description of sequence variants is essential to report and exchange information on the analysis of a genome, in particular in DNA diagnostics. The HGVS nomenclature-recommendations for the description of sequence variants as originally proposed by the Human Genome Variation Society-has gradually been accepted as the international standard for variant description. In this unit, we describe the current recommendations (HGVS version 15.11) regarding how to describe variants at the DNA, RNA, and protein level. We explain the rationale and give example descriptions for all variant types: substitution, deletion, duplication, insertion, inversion, conversion, and complex, as well as special types occurring only on the RNA (splicing) or protein level (nonsense, frame shift, extension). Finally, we point users to available support tools and give examples for the use of the freely available Mutalyzer suite. An extensive version of the HGVS recommendations is available online at http://varnomen.hgvs.org/. © 2016 by John Wiley & Sons, Inc.

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.

Integrating 400 million variants from 80,000 human samples with extensive annotations: towards a knowledge base to analyze disease cohorts

BACKGROUND

Data from a plethora of high-throughput sequencing studies is readily available to researchers, providing genetic variants detected in a variety of healthy and disease populations. While each individual cohort helps gain insights into polymorphic and disease-associated variants, a joint perspective can be more powerful in identifying polymorphisms, rare variants, disease-associations, genetic burden, somatic variants, and disease mechanisms.

DESCRIPTION

We have set up a Reference Variant Store (RVS) containing variants observed in a number of large-scale sequencing efforts, such as 1000 Genomes, ExAC, Scripps Wellderly, UK10K; various genotyping studies; and disease association databases. RVS holds extensive annotations pertaining to affected genes, functional impacts, disease associations, and population frequencies. RVS currently stores 400 million distinct variants observed in more than 80,000 human samples.

CONCLUSIONS

RVS facilitates cross-study analysis to discover novel genetic risk factors, gene-disease associations, potential disease mechanisms, and actionable variants. Due to its large reference populations, RVS can also be employed for variant filtration and gene prioritization.

AVAILABILITY

A web interface to public datasets and annotations in RVS is available at https://rvs.u.hpc.mssm.edu/.

Muddled genetic terms miss and mess the message

A critical aspect of science is the clear communication of complicated matters. However, language is often ambiguous, and the message can get lost in the telling. In particular, genetic terms can have different meanings for different people. Here, I discuss this problem and suggest remedies to clarify the message.