Software-Assisted Manual Review of Clinical Next-Generation Sequencing Data: An Alternative to Routine Sanger Sequencing Confirmation with Equivalent Results in >15,000 Germline DNA Screens.

Clinical genomic tests increasingly use a next-generation sequencing (NGS) platform due in part to the high fidelity of variant calls, yet rare errors are still possible. In germline DNA screening, failure to correct such errors could have serious consequences for patients, who may follow an unwarranted screening or surgical management path. It has been suggested that routine orthogonal confirmation by Sanger sequencing is required to verify NGS results, especially low-confidence positives with depressed allele fraction (<30% of alternate allele). We evaluated whether an alternative method of confirmation-software-assisted manual call review-performed comparably with Sanger confirmation in >15,000 samples. Licensed reviewers manually inspected both raw and processed data at the batch, sample, and variant levels, including raw NGS read pileups. Of ambiguous variant calls with <30% allele fraction (1707 total calls at 38 unique sites), manual call review classified >99% (n = 1701) as true positives (enriched for long insertions or deletions and homopolymers) or true negatives (often conspicuous NGS artifacts), with the remaining <1% (n = 6) being mosaic. Critically, results from software-assisted manual review and retrospective Sanger sequencing were concordant for samples selected from all ambiguous sites. We conclude that the confirmation required for high confidence in NGS-based germline testing can manifest in different ways; a trained NGS expert operating platform-tailored review software achieves quality comparable with routine Sanger confirmation.

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.