ArticleHuman mutation2016
Human Variome Project Quality Assessment Criteria for Variation Databases.
Article in Human mutation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Global Globin Network and adopting genomic variant database requirements for thalassemia.Database : the journal of biological databases and curation · 2024Article
- BTKbase, Bruton Tyrosine Kinase Variant Database in X-Linked Agammaglobulinemia: Looking Back and Ahead.Human mutation · 2023Article
- RefSeq Functional Elements as experimentally assayed nongenic reference standards and functional interactions in human and mouse.Genome research · 2022Article
- Molecular alterations in the integrated diagnosis of pediatric glial and glioneuronal tumors: A single center experience.PloS one · 2022Article
- Variation benchmark datasets: update, criteria, quality and applications.Database : the journal of biological databases and curation · 2020Article
- PON-P and PON-P2 predictor performance in CAGI challenges: Lessons learned.Human mutation · 2017Article
- Clinical Interpretation of Genomic Variations.Turkish journal of haematology : official journal of Turkish Society of Haematology · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Numerous databases containing information about DNA, RNA, and protein variations are available. Gene-specific variant databases (locus-specific variation databases, LSDBs) are typically curated and maintained for single genes or groups of genes for a certain disease(s). These databases are widely considered as the most reliable information source for a particular gene/protein/disease, but it should also be made clear they may have widely varying contents, infrastructure, and quality. Quality is very important to evaluate because these databases may affect health decision-making, research, and clinical practice. The Human Variome Project (HVP) established a Working Group for Variant Database Quality Assessment. The basic principle was to develop a simple system that nevertheless provides a good overview of the quality of a database. The HVP quality evaluation criteria that resulted are divided into four main components: data quality, technical quality, accessibility, and timeliness. This report elaborates on the developed quality criteria and how implementation of the quality scheme can be achieved. Examples are provided for the current status of the quality items in two different databases, BTKbase, an LSDB, and ClinVar, a central archive of submissions about variants and their clinical significance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.