Evidence map›Paper›PMID 36792982›Full record

ArticleBMC bioinformatics2023

ConanVarvar: a versatile tool for the detection of large syndromic copy number variation from whole-genome sequencing data.

Mikhail Gudkov, Loïc Thibaut, Matloob Khushi, Gillian M Blue, David S Winlaw, Sally L Dunwoodie, Eleni Giannoulatou

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 32% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 2 institutions in 1 country.

Mikhail GudkovVictor Chang Cardiac Research Institute, Sydney, NSW, 2010, Australia.
Loïc ThibautVictor Chang Cardiac Research Institute, Sydney, NSW, 2010, Australia.
Matloob KhushiSchool of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia.
Gillian M BlueSydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia.
David S WinlawSydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia.
Sally L DunwoodieVictor Chang Cardiac Research Institute, Sydney, NSW, 2010, Australia.
Eleni GiannoulatouVictor Chang Cardiac Research Institute, Sydney, NSW, 2010, Australia. E.Giannoulatou@victorchang.edu.au.ORCID http://orcid.org/0000-0002-7084-6736
The University of Sydney · AUVictor Chang Cardiac Research Institute · AU

Funding

National Health and Medical Research Council 1162878National Heart Foundation of Australia 101204National Heart Foundation of Australia 101894
6 · The paper itself

Abstract

backgroundA wide range of tools are available for the detection of copy number variants (CNVs) from whole-genome sequencing (WGS) data. However, none of them focus on clinically-relevant CNVs, such as those that are associated with known genetic syndromes. Such variants are often large in size, typically 1-5 Mb, but currently available CNV callers have been developed and benchmarked for the discovery of smaller variants. Thus, the ability of these programs to detect tens of real syndromic CNVs remains largely unknown.

resultsHere we present ConanVarvar, a tool which implements a complete workflow for the targeted analysis of large germline CNVs from WGS data. ConanVarvar comes with an intuitive R Shiny graphical user interface and annotates identified variants with information about 56 associated syndromic conditions. We benchmarked ConanVarvar and four other programs on a dataset containing real and simulated syndromic CNVs larger than 1 Mb. In comparison to other tools, ConanVarvar reports 10-30 times less false-positive variants without compromising sensitivity and is quicker to run, especially on large batches of samples.

conclusionsConanVarvar is a useful instrument for primary analysis in disease sequencing studies, where large CNVs could be the cause of disease.

Indexed as

DNA Copy Number VariationsGerm CellsHigh-Throughput Nucleotide SequencingWhole Genome SequencingWorkflowBioinformaticsCNVDockerWGS

Identifiers

PMID36792982
PMCPMC9930243
OpenAlexW4321004228

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.