scholarly journals GROM-RD: Resolving genomic biases to improve read depth detection of copy number variants

2014 ◽  
Author(s):  
Sean D Smith ◽  
Joseph K Kawash ◽  
Andrey Grigoriev
Keyword(s):  
Copy Number ◽  
Copy Number Variants ◽  
Read Depth ◽  
Read Coverage ◽  
Novel Approach ◽  
Depth Analysis ◽  
Gc Bias ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Cnv Detection

Amplifications or deletions of genome segments, known as copy number variants (CNVs), have been associated with many diseases. Read depth analysis of next-generation sequencing (NGS) is an essential method of detecting CNVs. However, genome read coverage is frequently distorted by various biases of NGS platforms, which reduce predictive capabilities of existing approaches. Additionally, the use of read depth tools has been somewhat hindered by imprecise breakpoint identification. We developed GROM-RD, an algorithm that analyzes multiple biases in read coverage to detect CNVs in NGS data. We found non-uniform variance across distinct GC regions after using existing GC bias correction methods and developed a novel approach to normalize such variance. Although complex and repetitive genome segments complicate CNV detection, GROM-RD adjusts for repeat bias and uses a two-pipeline masking approach to detect CNVs in complex and repetitive segments while improving sensitivity in less complicated regions. To overcome a typical weakness of RD methods, GROM-RD employs a CNV search using size-varying overlapping windows to improve breakpoint resolution. We compared our method to two widely used programs based on read depth methods, CNVnator and RDXplorer, and observed improved CNV detection and breakpoint accuracy for GROM-RD. GROM-RD is available at http://grigoriev.rutgers.edu/software/

scholarly journals GROM-RD: Resolving genomic biases to improve read depth detection of copy number variants

2014 ◽  
Author(s):  
Sean D Smith ◽  
Joseph K Kawash ◽  
Andrey Grigoriev
Keyword(s):  
Copy Number ◽  
Copy Number Variants ◽  
Read Depth ◽  
Read Coverage ◽  
Novel Approach ◽  
Depth Analysis ◽  
Gc Bias ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Cnv Detection

Amplifications or deletions of genome segments, known as copy number variants (CNVs), have been associated with many diseases. Read depth analysis of next-generation sequencing (NGS) is an essential method of detecting CNVs. However, genome read coverage is frequently distorted by various biases of NGS platforms, which reduce predictive capabilities of existing approaches. Additionally, the use of read depth tools has been somewhat hindered by imprecise breakpoint identification. We developed GROM-RD, an algorithm that analyzes multiple biases in read coverage to detect CNVs in NGS data. We found non-uniform variance across distinct GC regions after using existing GC bias correction methods and developed a novel approach to normalize such variance. Although complex and repetitive genome segments complicate CNV detection, GROM-RD adjusts for repeat bias and uses a two-pipeline masking approach to detect CNVs in complex and repetitive segments while improving sensitivity in less complicated regions. To overcome a typical weakness of RD methods, GROM-RD employs a CNV search using size-varying overlapping windows to improve breakpoint resolution. We compared our method to two widely used programs based on read depth methods, CNVnator and RDXplorer, and observed improved CNV detection and breakpoint accuracy for GROM-RD. GROM-RD is available at http://grigoriev.rutgers.edu/software/

T-CNV: a robust tool for detecting and visualizing copy number variants in targeted sequencing data.

2020 ◽  
Author(s):  
liu ye ◽  
wu yangming ◽  
zheng zexin ◽  
zhou tianliangwen
Keyword(s):  
Copy Number ◽  
Copy Number Variants ◽  
Control Sample ◽  
Read Depth ◽  
Gaussian Mixture ◽  
Sequencing Data ◽  
Positive Predict Value ◽  
Sliding Method ◽  
Targeted Ngs ◽  
Cnv Detection

Abstract Background Copy number variants (CNVs) are widespread among human genes, causing Mendelian or sporadic traits, or associating with complex diseases. Several tools have been developed for CNV assessment based on next generation sequencing (NGS) data using Read-depth (RD) strategy. However, maintaining high level of sensitivity and specificity is always challenging. Here, we present a novel, powerful, user-friendly and open accessed tool, T-CNV for CNV detection and visualization in targeted NGS panel.Results T-CNV consists of primary CNV detection and CNV candidates confirmation steps. After computing log2 values of normalized read depth ratio of tumor and normal/control sample, T-CNV confirms each possible CNV candidates by bins method, Gaussian Mixture Model (GMM) clustering approach and window-sliding method. We benchmarked its capacity with MLPA-validated dataset. Compared to three other advanced tools, T-CNV presents excellent performance with 95.42% sensitivity, 99.93% specificity and 93.63% positive predict value in MLPA-validated dataset, while achieving satisfactory performance in simulation study (sensitivity 65.95%, positive predict value 88.71% at coverage 100X).Conclusions T-CNV is a novel and robust tool for CNV detection and visualization in targeted NGS panel consisting of determination of possible CNV candidates and further confirmation by three different methods. It’s publicly available at https://github.com/Top-Gene/T-CNV.

scholarly journals CrowdVariant: a crowdsourcing approach to classify copy number variants

2016 ◽  
Author(s):  
Peyton Greenside ◽  
Justin M. Zook ◽  
Marc Salit ◽  
Ryan Poplin ◽  
Madeleine Cule ◽  
...  
Keyword(s):  
Copy Number ◽  
Reference Sample ◽  
Copy Number Variants ◽  
Substantial Improvement ◽  
Small Scale ◽  
High Confidence ◽  
High Quality ◽  
Genome Wide ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

AbstractCopy number variants (CNVs) are an important type of genetic variation and play a causal role in many diseases. However, they are also notoriously difficult to identify accurately from next-generation sequencing (NGS) data. For larger CNVs, genotyping arrays provide reasonable benchmark data, but NGS allows us to assay a far larger number of small (< 10kbp) CNVs that are poorly captured by array-based methods. The lack of high quality benchmark callsets of small-scale CNVs has limited our ability to assess and improve CNV calling algorithms for NGS data. To address this issue we developed a crowdsourcing framework, called CrowdVariant, that leverages Google’s high-throughput crowdsourcing platform to create a high confidence set of copy number variants for NA24385 (NIST HG002/RM 8391), an Ashkenazim reference sample developed in partnership with the Genome In A Bottle Consortium. In a pilot study we show that crowdsourced classifications, even from non-experts, can be used to accurately assign copy number status to putative CNV calls and thereby identify a high-quality subset of these calls. We then scale our framework genome-wide to identify 1,781 high confidence CNVs, which multiple lines of evidence suggest are a substantial improvement over existing CNV callsets, and are likely to prove useful in benchmarking and improving CNV calling algorithms. Our crowdsourcing methodology may be a useful guide for other genomics applications.

scholarly journals CNV-BAC: Copy number Variation Detection in Bacterial Circular Genome

2020 ◽  
Vol 36 (12) ◽  
pp. 3890-3891
Author(s):  
Linjie Wu ◽  
Han Wang ◽  
Yuchao Xia ◽  
Ruibin Xi
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
Genome Structure ◽  
Real Data ◽  
Read Depth ◽  
Supplementary Information ◽  
Circular Genome ◽  
Number Variation ◽  
Copy Number Variation Detection ◽  
Cnv Detection

Abstract Motivation Whole-genome sequencing (WGS) is widely used for copy number variation (CNV) detection. However, for most bacteria, their circular genome structure and high replication rate make reads more enriched near the replication origin. CNV detection based on read depth could be seriously influenced by such replication bias. Results We show that the replication bias is widespread using ∼200 bacterial WGS data. We develop CNV-BAC (CNV-Bacteria) that can properly normalize the replication bias and other known biases in bacterial WGS data and can accurately detect CNVs. Simulation and real data analysis show that CNV-BAC achieves the best performance in CNV detection compared with available algorithms. Availability and implementation CNV-BAC is available at https://github.com/XiDsLab/CNV-BAC. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Detection and characterization of copy number variants based on whole-genome sequencing by DNBSEQ platforms

2019 ◽  
Author(s):  
Junhua Rao ◽  
Lihua Peng ◽  
Fang Chen ◽  
Hui Jiang ◽  
Chunyu Geng ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Copy Number Variants ◽  
Copy Number Variant ◽  
Whole Genome ◽  
Genome Wide ◽  
Wide Range ◽  
Distribution Sensitivity ◽  
Cnv Detection

AbstractBackgroundNext-generation sequence (NGS) has rapidly developed in past years which makes whole-genome sequencing (WGS) becoming a more cost- and time-efficient choice in wide range of biological researches. We usually focus on some variant detection via WGS data, such as detection of single nucleotide polymorphism (SNP), insertion and deletion (Indel) and copy number variant (CNV), which playing an important role in many human diseases. However, the feasibility of CNV detection based on WGS by DNBSEQ™ platforms was unclear. We systematically analysed the genome-wide CNV detection power of DNBSEQ™ platforms and Illumina platforms on NA12878 with five commonly used tools, respectively.ResultsDNBSEQ™ platforms showed stable ability to detect slighter more CNVs on genome-wide (average 1.24-fold than Illumina platforms). Then, CNVs based on DNBSEQ™ platforms and Illumina platforms were evaluated with two public benchmarks of NA12878, respectively. DNBSEQ™ and Illumina platforms showed similar sensitivities and precisions on both two benchmarks. Further, the difference between tools for CNV detection was analyzed, and indicated the selection of tool for CNV detection could affected the CNV performance, such as count, distribution, sensitivity and precision.ConclusionThe major contribution of this paper is providing a comprehensive guide for CNV detection based on WGS by DNBSEQ™ platforms for the first time.

Combinatorial Detection Algorithm for Copy Number Variations Using High-throughput Sequencing Reads

2019 ◽  
Vol 33 (14) ◽  
pp. 1950022
Author(s):  
Hai Yang ◽  
Daming Zhu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
High Throughput ◽  
Copy Number ◽  
High Throughput Sequencing ◽  
Hidden Markov ◽  
Read Depth ◽  
Detection Algorithm ◽  
Gc Bias ◽  
Unmapped Reads

Copy number variation (CNV) is a prevalent kind of genetic structural variation which leads to an abnormal number of copies of large genomic regions, such as gain or loss of DNA segments larger than 1[Formula: see text]kb. CNV exists not only in human genome but also in plant genome. Current researches have testified that CNV is associated with many complex diseases. In this paper, guanine-cytosine (GC) bias, mappability and their effect on read depth signals in sequencing data are discussed first. Subsequently, a new correction method for GC bias and an improved combinatorial detection algorithm for CNV using high-throughput sequencing reads based on hidden Markov model (CNV-HMM) are proposed. The corrected read depth signals have lower correlation with GC content, mappability of reads and the width of analysis window. Then we create a hidden Markov model which maps the reads onto the reference genome and records the unmapped reads. The unmapped reads are counted and normalized. The CNV-HMM detects the abnormal signal of read count and gains the candidate CNVs using the expectation maximization (EM) algorithm. Finally, we filter the candidate CNVs using split reads to promote the performance of our algorithm. The experiment result indicates that the CNV-HMM algorithm has higher accuracy and sensitivity for CNVs detection than most current detection algorithms.

scholarly journals RBV: Read balance validator, a tool for prioritising copy number variations in germline conditions

2018 ◽  
Author(s):  
Whitney Whitford ◽  
Klaus Lehnert ◽  
Russell G. Snell ◽  
Jessie C. Jacobsen
Keyword(s):  
Copy Number ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Relative Proportion ◽  
Copy Number Variants ◽  
Read Depth ◽  
Copy Number Variations ◽  
Single Nucleotide Variants ◽  
Software Packages ◽  
Bioinformatic Tool

AbstractBackgroundThe popularisation and decreased cost of genome resequencing has resulted in an increased use in molecular diagnostics. While there are a number of established and high quality bioinfomatic tools for identifying small genetic variants including single nucleotide variants and indels, currently there is no established standard for the detection of copy number variants (CNVs) from sequence data. The requirement for CNV detection from high throughput sequencing has resulted in the development of a large number of software packages. These tools typically utilise the sequence data characteristics: read depth, split reads, read pairs, and assembly-based techniques. However the additional source of information from read balance, defined as relative proportion of reads of each allele at each position, has been underutilised in the existing applications.ResultsWe present Read Balance Validator (RBV), a bioinformatic tool which uses read balance for prioritisation and validation of putative CNVs. The software simultaneously interrogates nominated regions for the presence of deletions or multiplications, and can differentiate larger CNVs from diploid regions. Additionally, the utility of RBV to test for inheritance of CNVs is demonstrated in this report.ConclusionsRBV is a CNV validation and prioritisation bioinformatic tool for both genome and exome sequencing available as a python package from https://github.com/whitneywhitford/RBV

scholarly journals Frequency of the STRC-CATSPER2 deletion in STRC-associated hearing loss patients

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Shin-ya Nishio ◽  
Shin-ichi Usami
Keyword(s):  
Hearing Loss ◽  
Male Infertility ◽  
Genetic Counselling ◽  
Copy Number ◽  
Read Depth ◽  
Copy Number Variations ◽  
Genomic Region ◽  
High Prevalence ◽  
Homozygous Deletions ◽  
Next Generation Sequencing Ngs

AbstractThe STRC gene, located on chromosome 15q15.3, is one of the genetic causes of autosomal recessive mild-to-moderate sensorineural hearing loss. One of the unique characteristics of STRC-associated hearing loss is the high prevalence of long deletions or copy number variations observed on chromosome 15q15.3. Further, the deletion of chromosome 15q15.3 from STRC to CATSPER2 is also known to be a genetic cause of deafness infertility syndrome (DIS), which is associated with not only hearing loss but also male infertility, as CATSPER2 plays crucial roles in sperm motility. Thus, information regarding the deletion range for each patient is important to the provision of appropriate genetic counselling for hearing loss and male infertility. In the present study, we performed next-generation sequencing (NGS) analysis for 9956 Japanese hearing loss patients and analyzed copy number variations in the STRC gene based on NGS read depth data. In addition, we performed Multiplex Ligation-dependent Probe Amplification analysis to determine the deletion range including the PPIP5K1, CKMT1B, STRC and CATSPER2 genomic region to estimate the prevalence of the STRC-CATSPER deletion, which is causative for DIS among the STRC-associated hearing loss patients. As a result, we identified 276 cases with STRC-associated hearing loss. The prevalence of STRC-associated hearing loss in Japanese hearing loss patients was 2.77% (276/9956). In addition, 77.1% of cases with STRC homozygous deletions carried a two copy loss of the entire CKMT1B-STRC-CATSPER2 gene region. This information will be useful for the provision of more appropriate genetic counselling regarding hearing loss and male infertility for the patients with a STRC deletion.

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