Evidence map›Paper›PMID 28357035›Full record

ArticleF1000Research2016

dbVar structural variant cluster set for data analysis and variant comparison.

Lon Phan, Jeffrey Hsu, Le Quang Minh Tri, Michaela Willi, Tamer Mansour, Yan Kai, John Garner, John Lopez, Ben Busby

Open access · goldAbstract read
In one paragraph

Article in F1000Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.9field-weighted citation impact, top 10% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 17 citations in OpenAlex.

  1. Precision Profiling of the Cardiovascular Post-Translationally Modified Proteome.Journal of cardiovascular development and disease · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 4 countries.

Lon PhanNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Jeffrey HsuCleveland Clinic Lerner Research Institute, Cleveland, OH, USA.
Le Quang Minh TriDepartment of Biotechnology, Ho Chi Minh City International University, Ho Chi Minh, Vietnam.
Michaela WilliLaboratory of Genetics and Physiology, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MA, USA; Division of Bioinformatics, Biocenter, Medical University Innsbruck, Innsbruck, Austria.
Tamer MansourLab for Data Intensive Biology, Department of Population Health and Reproduction, University of California, Davis, CA, USA; Department of Clinical Pathology, University of Mansoura, Mansoura, Egypt.
Yan KaiCancer Epigenetics Laboratory, Department of Anatomy and Regenerative Biology, The George Washington University, Washington, DC, USA; Department of Physics, The George Washington University, Washington, DC, USA.
John GarnerNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
John LopezNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Ben BusbyNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
National Institutes of Health · USCleveland Clinic Lerner College of Medicine · USGeorge Washington University · USHo Chi Minh City International University · VNMansoura University · EG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

dbVar houses over 3 million submitted structural variants (SSV) from 120 human studies including copy number variations (CNV), insertions, deletions, inversions, translocations, and complex chromosomal rearrangements. Users can submit multiple SSVs to dbVAR  that are presumably identical, but were ascertained by different platforms and samples,  to calculate whether the variant is rare or common in the population and allow for cross validation. However, because SSV genomic location reporting can vary - including fuzzy locations where the start and/or end points are not precisely known - analysis, comparison, annotation, and reporting of SSVs across studies can be difficult. This project was initiated by the Structural Variant Comparison Group for the purpose of generating a non-redundant set of genomic regions defined by counts of concordance for all human SSVs placed on RefSeq assembly GRCh38 (RefSeq accession GCF_000001405.26). We intend that the availability of these regions, called structural variant clusters (SVCs), will facilitate the analysis, annotation, and exchange of SV data and allow for simplified display in genomic sequence viewers for improved variant interpretation. Sets of SVCs were generated by variant type for each of the 120 studies as well as for a combined set across all studies. Starting from 3.64 million SSVs, 2.5 million and 3.4 million non-redundant SVCs with count >=1 were generated by variant type for each study and across all studies, respectively. In addition, we have developed utilities for annotating, searching, and filtering SVC data in GVF format for computing summary statistics, exporting data for genomic viewers, and annotating the SVC using external data sources.

Indexed as

dbVarEducationGenome AnnotationGenomicsGVFNCBIOpen-SourceSoftwareStructural Variation Cluster

Identifiers

PMID28357035
PMCPMC5345777
OpenAlexW2337437180

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.