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

doi:10.1186/s12859-023-05154-x

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

BMC Bioinformatics. 2023 Feb 15;24(1):49. doi: 10.1186/s12859-023-05154-x.

Authors

Mikhail Gudkov^{1

2

3}, Loïc Thibaut^{1

4}, Matloob Khushi⁵, Gillian M Blue^{6

7}, David S Winlaw^{6

7}, Sally L Dunwoodie^{1

3

8}, Eleni Giannoulatou^{9

10}

Affiliations

¹ Victor Chang Cardiac Research Institute, Sydney, NSW, 2010, Australia.
² School of Biomedical Engineering, The University of Sydney, Sydney, NSW, 2006, Australia.
³ St Vincent's Clinical Campus, School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, 2010, Australia.
⁴ School of Mathematics and Statistics, UNSW Sydney, Sydney, NSW, 2052, Australia.
⁵ School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia.
⁶ Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia.
⁷ Heart Centre for Children, The Children's Hospital at Westmead, Sydney, NSW, 2145, Australia.
⁸ School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Sydney, NSW, 2052, Australia.
⁹ Victor Chang Cardiac Research Institute, Sydney, NSW, 2010, Australia. E.Giannoulatou@victorchang.edu.au.
¹⁰ St Vincent's Clinical Campus, School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, 2010, Australia. E.Giannoulatou@victorchang.edu.au.

Abstract

Background: A 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.

Results: Here 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.

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

Keywords: Bioinformatics; CNV; Docker; WGS.

MeSH terms

DNA Copy Number Variations*
Germ Cells*
High-Throughput Nucleotide Sequencing
Whole Genome Sequencing
Workflow

Abstract

MeSH terms

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