Dataset Information

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

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.

SUBMITTER: Gudkov M

PROVIDER: S-EPMC9930243 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

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

Gudkov Mikhail M Thibaut Loïc L Khushi Matloob M Blue Gillian M GM Winlaw David S DS Dunwoodie Sally L SL Giannoulatou Eleni E

BMC bioinformatics 20230215 1

<h4>Background</h4>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 syndrom ...[more]

PMID: 36792982

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Project description:BackgroundWhole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations.MethodsWe apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions.ResultsThe GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls.ConclusionsChoice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.

Project description:IntroductionWhole-genome sequencing using nanopore technologies can uncover structural variants, which are DNA rearrangements larger than 50 base pairs. Nanopore technologies can also characterize their boundaries with single-base accuracy, owing to the kilobase-long reads that encompass either full variants or their junctions. Other methods, such as next-generation short read sequencing or PCR assays, are limited in their capabilities to detect or characterize structural variants. However, the existing software for nanopore sequencing data analysis still reports incomplete variant sets, which also contain erroneous calls, a considerable obstacle for the molecular diagnosis or accurate genotyping of populations.MethodsWe compared multiple factors affecting variant calling, such as reference genome version, aligner (minimap2, NGMLR, and lra) choice, and variant caller combinations (Sniffles, CuteSV, SVIM, and NanoVar), to find the optimal group of tools for calling large (>50 kb) deletions and duplications, using data from seven patients exhibiting gross gene defects on SERPINC1 and from a reference variant set as the control. The goal was to obtain the most complete, yet reasonably specific group of large variants using a single cell of PromethION sequencing, which yielded lower depth coverage than short-read sequencing. We also used a custom method for the statistical analysis of the coverage value to refine the resulting datasets.ResultsWe found that for large deletions and duplications (>50 kb), the existing software performed worse than for smaller ones, in terms of both sensitivity and specificity, and newer tools had not improved this. Our novel software, disCoverage, could polish variant callers' results, improving specificity by up to 62% and sensitivity by 15%, the latter requiring other data or samples.ConclusionWe analyzed the current situation of >50-kb copy number variants with nanopore sequencing, which could be improved. The methods presented in this work could help to identify the known deletions and duplications in a set of patients, while also helping to filter out erroneous calls for these variants, which might aid the efforts to characterize a not-yet well-known fraction of genetic variability in the human genome.

Dataset Information

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

Background

Results

Conclusions

Publications

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

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