Dataset Information

WisecondorX: improved copy number detection for routine shallow whole-genome sequencing.

ABSTRACT: Shallow whole-genome sequencing to infer copy number alterations (CNAs) in the human genome is rapidly becoming the method par excellence for routine diagnostic use. Numerous tools exist to deduce aberrations from massive parallel sequencing data, yet most are optimized for research and often fail to redeem paramount needs in a clinical setting. Optimally, a read depth-based analytical software should be able to deal with single-end and low-coverage data-this to make sequencing costs feasible. Other important factors include runtime, applicability to a variety of analyses and overall performance. We compared the most important aspect, being normalization, across six different CNA tools, selected for their assumed ability to satisfy the latter needs. In conclusion, WISECONDOR, which uses a within-sample normalization technique, undoubtedly produced the best results concerning variance, distributional assumptions and basic ability to detect true variations. Nonetheless, as is the case with every tool, WISECONDOR has limitations, which arise through its exclusiveness for non-invasive prenatal testing. Therefore, this work presents WisecondorX in addition, an improved WISECONDOR that enables its use for varying types of applications. WisecondorX is freely available at https://github.com/CenterForMedicalGeneticsGhent/WisecondorX.

SUBMITTER: Raman L

PROVIDER: S-EPMC6393301 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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WisecondorX: improved copy number detection for routine shallow whole-genome sequencing.

Raman Lennart L Dheedene Annelies A De Smet Matthias M Van Dorpe Jo J Menten Björn B

Nucleic acids research 20190201 4

Shallow whole-genome sequencing to infer copy number alterations (CNAs) in the human genome is rapidly becoming the method par excellence for routine diagnostic use. Numerous tools exist to deduce aberrations from massive parallel sequencing data, yet most are optimized for research and often fail to redeem paramount needs in a clinical setting. Optimally, a read depth-based analytical software should be able to deal with single-end and low-coverage data-this to make sequencing costs feasible. O ...[more]

PMID: 30566647

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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

WisecondorX: improved copy number detection for routine shallow whole-genome sequencing.

Publications

WisecondorX: improved copy number detection for routine shallow whole-genome sequencing.

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