ClinVar: improvements to accessing data.

ClinVar is a freely available, public archive of human genetic variants and interpretations of their relationships to diseases and other conditions, maintained at the National Institutes of Health (NIH). Submitted interpretations of variants are aggregated and made available on the ClinVar website (https://www.ncbi.nlm.nih.gov/clinvar/), and as downloadable files via FTP and through programmatic tools such as NCBI's E-utilities. The default view on the ClinVar website, the Variation page, was recently redesigned. The new layout includes several new sections that make it easier to find submitted data as well as summary data such as all diseases and citations reported for the variant. The new design also better represents more complex data such as haplotypes and genotypes, as well as variants that are in ClinVar as part of a haplotype or genotype but have no interpretation for the single variant. ClinVar's variant-centric XML had its production release in April 2019. The ClinVar website and E-utilities both have been updated to support the VCV (variation in ClinVar) accession numbers found in the variant-centric XML file. ClinVar's search engine has been fine-tuned for improved retrieval of search results.

ClinVar: public archive of relationships among sequence variation and human phenotype.

ClinVar (http://www.ncbi.nlm.nih.gov/clinvar/) provides a freely available archive of reports of relationships among medically important variants and phenotypes. ClinVar accessions submissions reporting human variation, interpretations of the relationship of that variation to human health and the evidence supporting each interpretation. The database is tightly coupled with dbSNP and dbVar, which maintain information about the location of variation on human assemblies. ClinVar is also based on the phenotypic descriptions maintained in MedGen (http://www.ncbi.nlm.nih.gov/medgen). Each ClinVar record represents the submitter, the variation and the phenotype, i.e. the unit that is assigned an accession of the format SCV000000000.0. The submitter can update the submission at any time, in which case a new version is assigned. To facilitate evaluation of the medical importance of each variant, ClinVar aggregates submissions with the same variation/phenotype combination, adds value from other NCBI databases, assigns a distinct accession of the format RCV000000000.0 and reports if there are conflicting clinical interpretations. Data in ClinVar are available in multiple formats, including html, download as XML, VCF or tab-delimited subsets. Data from ClinVar are provided as annotation tracks on genomic RefSeqs and are used in tools such as Variation Reporter (http://www.ncbi.nlm.nih.gov/variation/tools/reporter), which reports what is known about variation based on user-supplied locations.

Novel Method for Accurately Assessing Pull-up Artifacts in STR Analysis.

OSIRIS is a mathematically-based software tool for Short Tandem Repeat (STR) and DNA fragment analysis (https://www.ncbi.nlm.nih.gov/osiris/). As part of its routine sample analyses, OSIRIS computes unique quality metrics that can be used for sample quality assessment. A common artifact of STR analysis is cross-channel pull-up or pull-down (negative pull-up). This occurs because of the spectral overlap between the dyes used with the marker set, and the failure of the color deconvolution matrix to isolate the colors in the dye set adequately. This paper describes a mathematical method for analyzing and quantifying the pull-up patterns across sample channels and effectively identifying and correcting the pull-up artifacts, as implemented in OSIRIS. Unlike approaches to pull-up that require a training set composed of previous samples, the algorithm described here uses a mathematical model of the underlying causes of pull-up. It is based solely on the information intrinsic to the sample it is analyzing and therefore incorporates the effects of the ambient conditions and the specific procedures used in creating the sample. These conditions are the physical determinants of the level of pull-up in the sample and are not likely to be represented in a training set consisting of past samples.