Evidence map›Paper›PMID 33800487›Full record

ArticleGenes2021

iVar, an Interpretation-Oriented Tool to Manage the Update and Revision of Variant Annotation and Classification.

Sara Castellano, Federica Cestari, Giovanni Faglioni, Elena Tenedini, Marco Marino, Lucia Artuso, Rossella Manfredini, Mario Luppi, Tommaso Trenti, Enrico Tagliafico

Abstract read
In one paragraph

Article in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
–field-weighted citation impact
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

33 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
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

10 authors.

Sara CastellanoCenter for Genome Research, University of Modena and Reggio Emilia, 41125 Modena, Italy.ORCID 0000-0001-6786-7854
Federica CestariCenter for Genome Research, University of Modena and Reggio Emilia, 41125 Modena, Italy.ORCID 0000-0002-0554-4483
Giovanni FaglioniNabla2 s.r.l., 41124 Modena, Italy.
Elena TenediniDepartment of Laboratory Medicine and Pathology, Diagnostic Hematology and Clinical Genomics Unit, Modena University Hospital, 41124 Modena, Italy.
Marco MarinoDepartment of Laboratory Medicine and Pathology, Diagnostic Hematology and Clinical Genomics Unit, Modena University Hospital, 41124 Modena, Italy.
Lucia ArtusoDepartment of Laboratory Medicine and Pathology, Diagnostic Hematology and Clinical Genomics Unit, Modena University Hospital, 41124 Modena, Italy.
Rossella ManfrediniCentre for Regenerative Medicine, Life Sciences Department, University of Modena and Reggio Emilia, 41125 Modena, Italy.
Mario LuppiDepartment of Medical and Surgical Sciences, University of Modena and Reggio Emilia, 41124 Modena, Italy.
Tommaso TrentiDepartment of Laboratory Medicine and Pathology, Diagnostic Hematology and Clinical Genomics Unit, Modena University Hospital, 41124 Modena, Italy.
Enrico TagliaficoCenter for Genome Research, University of Modena and Reggio Emilia, 41125 Modena, Italy.ORCID 0000-0003-0981-2542

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid evolution of Next Generation Sequencing in clinical settings, and the resulting challenge of variant reinterpretation given the constantly updated information, require robust data management systems and organized approaches. In this paper, we present iVar: a freely available and highly customizable tool with a user-friendly web interface. It represents a platform for the unified management of variants identified by different sequencing technologies. iVar accepts variant call format (VCF) files and text annotation files and elaborates them, optimizing data organization and avoiding redundancies. Updated annotations can be periodically re-uploaded and associated with variants as historically tracked attributes, i.e., modifications can be recorded whenever an updated value is imported, thus keeping track of all changes. Data can be visualized through variant-centered and sample-centered interfaces. A customizable search function can be exploited to periodically check if pathogenicity-related data of a variant has changed over time. Patient recontacting ensuing from variant reinterpretation is made easier by iVar through the effective identification of all patients present in the database carrying a specific variant. We tested iVar by uploading 4171 VCF files and 1463 annotation files, obtaining a database of 4166 samples and 22,569 unique variants. iVar has proven to be a useful tool with good performance in terms of collecting and managing data from a medium-throughput laboratory.

Indexed as

Computational BiologyDatabases, GeneticGenetic VariationHigh-Throughput Nucleotide SequencingMolecular Sequence AnnotationUser-Computer InterfaceHumansclinical genomicsdatabasedata managementnext-generation sequencingvariant annotationvariant classification

Identifiers

PMID33800487
PMCPMC8001268

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.