Impact of DNA source on genetic variant detection from human whole-genome sequencing data

BackgroundWhole blood is currently the most common DNA source for whole-genome sequencing (WGS), but for studies requiring non-invasive collection, self-collection, greater sample stability or additional tissue references, saliva or buccal samples may be preferred. However, the relative quality of sequencing data and accuracy of genetic variant detection from blood-derived, saliva-derived and buccal-derived DNA need to be thoroughly investigated.MethodsMatched blood, saliva and buccal samples from four unrelated individuals were used to compare sequencing metrics and variant-detection accuracy among these DNA sources.ResultsWe observed significant differences among DNA sources for sequencing quality metrics such as percentage of reads aligned and mean read depth (p<0.05). Differences were negligible in the accuracy of detecting short insertions and deletions; however, the false positive rate for single nucleotide variation detection was slightly higher in some saliva and buccal samples. The sensitivity of copy number variant (CNV) detection was up to 25% higher in blood samples, depending on CNV size and type, and appeared to be worse in saliva and buccal samples with high bacterial concentration. We also show that methylation-based enrichment for eukaryotic DNA in saliva and buccal samples increased alignment rates but also reduced read-depth uniformity, hampering CNV detection.ConclusionFor WGS, we recommend using DNA extracted from blood rather than saliva or buccal swabs; if saliva or buccal samples are used, we recommend against using methylation-based eukaryotic DNA enrichment. All data used in this study are available for further open-science investigation.

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Performance of copy number variants detection based on whole-genome sequencing by DNBSEQ platforms

BMC Bioinformatics ◽

10.1186/s12859-020-03859-x ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Junhua Rao ◽

Lihua Peng ◽

Xinming Liang ◽

Hui Jiang ◽

Chunyu Geng ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Technology Use ◽

Copy Number ◽

Massively Parallel Sequencing ◽

Copy Number Variants ◽

Whole Genome ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Cnv Detection

Abstract Background DNBSEQ™ platforms are new massively parallel sequencing (MPS) platforms that use DNA nanoball technology. Use of data generated from DNBSEQ™ platforms to detect single nucleotide variants (SNVs) and small insertions and deletions (indels) has proven to be quite effective, while the feasibility of copy number variants (CNVs) detection is unclear. Results Here, we first benchmarked different CNV detection tools based on Illumina whole-genome sequencing (WGS) data of NA12878 and then assessed these tools in CNV detection based on DNBSEQ™ sequencing data from the same sample. When the same tool was used, the CNVs detected based on DNBSEQ™ and Illumina data were similar in quantity, length and distribution, while great differences existed within results from different tools and even based on data from a single platform. We further estimated the CNV detection power based on available CNV benchmarks of NA12878 and found similar precision and sensitivity between the DNBSEQ™ and Illumina platforms. We also found higher precision of CNVs shorter than 1 kbp based on DNBSEQ™ platforms than those based on Illumina platforms by using Pindel, DELLY and LUMPY. We carefully compared these two available benchmarks and found a large proportion of specific CNVs between them. Thus, we constructed a more complete CNV benchmark of NA12878 containing 3512 CNV regions. Conclusions We assessed and benchmarked CNV detections based on WGS with DNBSEQ™ platforms and provide guidelines for future studies.

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Reconstruction of high read-depth signals from low-depth whole genome sequencing data using deep learning

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm.2017.8217832 ◽

2017 ◽

Author(s):

Yao-zhong Zhang ◽

Seiya Imoto ◽

Satoru Miyano ◽

Rui Yamaguchi

Keyword(s):

Deep Learning ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Read Depth ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data

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A deep learning approach for filtering structural variants in short read sequencing data

Briefings in Bioinformatics ◽

10.1093/bib/bbaa370 ◽

2020 ◽

Author(s):

Yongzhuang Liu ◽

Yalin Huang ◽

Guohua Wang ◽

Yadong Wang

Keyword(s):

Deep Learning ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Whole Genome ◽

Structural Variants ◽

Sequencing Data ◽

Clinical Practices ◽

Short Read ◽

Structural Variant ◽

Variant Detection

Abstract Short read whole genome sequencing has become widely used to detect structural variants in human genetic studies and clinical practices. However, accurate detection of structural variants is a challenging task. Especially existing structural variant detection approaches produce a large proportion of incorrect calls, so effective structural variant filtering approaches are urgently needed. In this study, we propose a novel deep learning-based approach, DeepSVFilter, for filtering structural variants in short read whole genome sequencing data. DeepSVFilter encodes structural variant signals in the read alignments as images and adopts the transfer learning with pre-trained convolutional neural networks as the classification models, which are trained on the well-characterized samples with known high confidence structural variants. We use two well-characterized samples to demonstrate DeepSVFilter’s performance and its filtering effect coupled with commonly used structural variant detection approaches. The software DeepSVFilter is implemented using Python and freely available from the website at https://github.com/yongzhuang/DeepSVFilter.

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Evaluation of tools for identifying large copy number variations from ultra-low-coverage whole-genome sequencing data

BMC Genomics ◽

10.1186/s12864-021-07686-z ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Johannes Smolander ◽

Sofia Khan ◽

Kalaimathy Singaravelu ◽

Leni Kauko ◽

Riikka J. Lund ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Sex Chromosomes ◽

Copy Number ◽

Copy Number Variations ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Low Coverage ◽

Cnv Detection

Abstract Background Detection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years. However, only a little is known about the applicability of the developed algorithms to ultra-low-coverage (0.0005–0.8×) data that is used in various research and clinical applications, such as digital karyotyping and single-cell CNV detection. Result Here, the performance of six popular read-depth based CNV detection algorithms (BIC-seq2, Canvas, CNVnator, FREEC, HMMcopy, and QDNAseq) was studied using ultra-low-coverage WGS data. Real-world array- and karyotyping kit-based validation were used as a benchmark in the evaluation. Additionally, ultra-low-coverage WGS data was simulated to investigate the ability of the algorithms to identify CNVs in the sex chromosomes and the theoretical minimum coverage at which these tools can accurately function. Our results suggest that while all the methods were able to detect large CNVs, many methods were susceptible to producing false positives when smaller CNVs (< 2 Mbp) were detected. There was also significant variability in their ability to identify CNVs in the sex chromosomes. Overall, BIC-seq2 was found to be the best method in terms of statistical performance. However, its significant drawback was by far the slowest runtime among the methods (> 3 h) compared with FREEC (~ 3 min), which we considered the second-best method. Conclusions Our comparative analysis demonstrates that CNV detection from ultra-low-coverage WGS data can be a highly accurate method for the detection of large copy number variations when their length is in millions of base pairs. These findings facilitate applications that utilize ultra-low-coverage CNV detection.

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CNVpytor: a tool for CNV/CNA detection and analysis from read depth and allele imbalance in whole genome sequencing

10.1101/2021.01.27.428472 ◽

2021 ◽

Author(s):

Milovan Suvakov ◽

Arijit Panda ◽

Colin Diesh ◽

Ian Holmes ◽

Alexej Abyzov

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Copy Number ◽

Read Depth ◽

Copy Number Variations ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Modular Architecture ◽

Small Indels

AbstractDetecting copy number variations (CNVs) and copy number alterations (CNAs) based on whole genome sequencing data is important for personalized genomics and treatment. CNVnator is one of the most popular tools for CNV/CNA discovery and analysis based on read depth (RD). Herein, we present an extension of CNVnator developed in Python -- CNVpytor. CNVpytor inherits the reimplemented core engine of its predecessor and extends visualization, modularization, performance, and functionality. Additionally, CNVpytor uses B-allele frequency (BAF) likelihood information from single nucleotide polymorphism and small indels data as additional evidence for CNVs/CNAs and as primary information for copy number neutral losses of heterozygosity. CNVpytor is significantly faster than CNVnator—particularly for parsing alignment files (2 to 20 times faster)—and has (20-50 times) smaller intermediate files. CNV calls can be filtered using several criteria and annotated. Modular architecture allows it to be used in shared and cloud environments such as Google Colab and Jupyter notebook. Data can be exported into JBrowse, while a lightweight plugin version of CNVpytor for JBrowse enables nearly instant and GUI-assisted analysis of CNVs by any user. CNVpytor release and the source code are available on GitHub at https://github.com/abyzovlab/CNVpytor under the MIT license.

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Empirical design of a variant quality control pipeline for whole genome sequencing data using replicate discordance

Scientific Reports ◽

10.1038/s41598-019-52614-7 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Robert P. Adelson ◽

Alan E. Renton ◽

Wentian Li ◽

Nir Barzilai ◽

Gil Atzmon ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Variant Calling ◽

Read Depth ◽

Concordance Rate ◽

Whole Genome Sequencing Data ◽

Analysis Tool ◽

Whole Genome ◽

Sequencing Data ◽

Genome Wide

Abstract The success of next-generation sequencing depends on the accuracy of variant calls. Few objective protocols exist for QC following variant calling from whole genome sequencing (WGS) data. After applying QC filtering based on Genome Analysis Tool Kit (GATK) best practices, we used genotype discordance of eight samples that were sequenced twice each to evaluate the proportion of potentially inaccurate variant calls. We designed a QC pipeline involving hard filters to improve replicate genotype concordance, which indicates improved accuracy of genotype calls. Our pipeline analyzes the efficacy of each filtering step. We initially applied this strategy to well-characterized variants from the ClinVar database, and subsequently to the full WGS dataset. The genome-wide biallelic pipeline removed 82.11% of discordant and 14.89% of concordant genotypes, and improved the concordance rate from 98.53% to 99.69%. The variant-level read depth filter most improved the genome-wide biallelic concordance rate. We also adapted this pipeline for triallelic sites, given the increasing proportion of multiallelic sites as sample sizes increase. For triallelic sites containing only SNVs, the concordance rate improved from 97.68% to 99.80%. Our QC pipeline removes many potentially false positive calls that pass in GATK, and may inform future WGS studies prior to variant effect analysis.

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Evaluation of the performance of copy number variant prediction tools for the detection of deletions from whole genome sequencing data

10.1101/482554 ◽

2018 ◽

Author(s):

Whitney Whitford ◽

Klaus Lehnert ◽

Russell G. Snell ◽

Jessie C. Jacobsen

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Copy Number ◽

Whole Genome ◽

Read Pair ◽

False Discovery ◽

Software Packages ◽

Variant Detection ◽

Detection Software ◽

Cnv Detection

AbstractBackgroundWhole genome sequencing (WGS) has increased in popularity and decreased in cost over the past decade, rendering this approach as a viable and sensitive method for variant detection. In addition to its utility for single nucleotide variant detection, WGS data has the potential to detect Copy Number Variants (CNV) to fine resolution. Many CNV detection software packages have been developed exploiting four main types of data: read pair, split read, read depth, and assembly based methods. The aim of this study was to evaluate the efficiency of each of these main approaches in detecting deletions.MethodsWGS data and high confidence deletion calls for the individual NA12878 from the Genome in a Bottle consortium were the benchmark dataset. The performance of Breakdancer, CNVnator, Delly, FermiKit, and Pindel was assessed by comparing the accuracy and sensitivity of each software package in detecting deletions exceeding 1kb.ResultsThere was considerable variability in the outputs of the different WGS CNV detection programs. The best performance was seen from Breakdancer and Delly, with 92.6% and 96.7% sensitivity, respectively and 34.5% and 68.5% false discovery rate (FDR), respectively. In comparison, Pindel, CNVnator, and FermiKit were less effective with sensitivities of 69.1%, 66.0%, and 15.8%, respectively and FDR of 91.3%, 69.0%, and 31.7%, respectively. Concordance across software packages was poor, with only 27 of the total 612 benchmark deletions identified by all five methodologies.ConclusionsThe WGS based CNV detection tools evaluated show disparate performance in identifying deletions ≥1kb, particularly those utilising different input data characteristics. Software that exploits read pair based data had the highest sensitivity, namely Breakdancer and Delly. Breakdancer also had the second lowest false discovery rate. Therefore, in this analysis read pair methods (Breakdancer in particular) were the best performing approaches for the identification of deletions ≥1kb, balancing accuracy and sensitivity. There is potential for improvement in the detection algorithms, particularly for reducing FDR. This analysis has validated the utility of WGS based CNV detection software to reliably identify deletions, and these findings will be of use when choosing appropriate software for deletion detection, in both research and diagnostic medicine.

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CNVpytor: a tool for copy number variation detection and analysis from read depth and allele imbalance in whole-genome sequencing

GigaScience ◽

10.1093/gigascience/giab074 ◽

2021 ◽

Vol 10 (11) ◽

Cited By ~ 1

Author(s):

Milovan Suvakov ◽

Arijit Panda ◽

Colin Diesh ◽

Ian Holmes ◽

Alexej Abyzov

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Copy Number ◽

Read Depth ◽

Copy Number Variations ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Nucleotide Polymorphisms ◽

Sequencing Data ◽

Modular Architecture

Abstract Background Detecting copy number variations (CNVs) and copy number alterations (CNAs) based on whole-genome sequencing data is important for personalized genomics and treatment. CNVnator is one of the most popular tools for CNV/CNA discovery and analysis based on read depth. Findings Herein, we present an extension of CNVnator developed in Python—CNVpytor. CNVpytor inherits the reimplemented core engine of its predecessor and extends visualization, modularization, performance, and functionality. Additionally, CNVpytor uses B-allele frequency likelihood information from single-nucleotide polymorphisms and small indels data as additional evidence for CNVs/CNAs and as primary information for copy number–neutral losses of heterozygosity. Conclusions CNVpytor is significantly faster than CNVnator—particularly for parsing alignment files (2–20 times faster)—and has (20–50 times) smaller intermediate files. CNV calls can be filtered using several criteria, annotated, and merged over multiple samples. Modular architecture allows it to be used in shared and cloud environments such as Google Colab and Jupyter notebook. Data can be exported into JBrowse, while a lightweight plugin version of CNVpytor for JBrowse enables nearly instant and GUI-assisted analysis of CNVs by any user. CNVpytor release and the source code are available on GitHub at https://github.com/abyzovlab/CNVpytor under the MIT license.

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