Application of low-depth whole-genome sequencing for copy number variations in genetic diagnosis of X-linked ichthyosis due to STS gene deletion

2019 ◽  
Vol 52 (10) ◽  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Gene Deletion ◽  
Genetic Diagnosis ◽  
Copy Number Variations ◽  
Whole Genome

scholarly journals A Comparison of Tools for Copy-Number Variation Detection in Germline Whole Exome and Whole Genome Sequencing Data

Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6283
Author(s):  
Migle Gabrielaite ◽  
Mathias Husted Torp ◽  
Malthe Sebro Rasmussen ◽  
Sergio Andreu-Sánchez ◽  
Filipe Garrett Vieira ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Reference Sample ◽  
Snp Array ◽  
Copy Number Variations ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Standard Reference Sample ◽  
Whole Exome

Copy-number variations (CNVs) have important clinical implications for several diseases and cancers. Relevant CNVs are hard to detect because common structural variations define large parts of the human genome. CNV calling from short-read sequencing would allow single protocol full genomic profiling. We reviewed 50 popular CNV calling tools and included 11 tools for benchmarking in a reference cohort encompassing 39 whole genome sequencing (WGS) samples paired current clinical standard—SNP-array based CNV calling. Additionally, for nine samples we also performed whole exome sequencing (WES), to address the effect of sequencing protocol on CNV calling. Furthermore, we included Gold Standard reference sample NA12878, and tested 12 samples with CNVs confirmed by multiplex ligation-dependent probe amplification (MLPA). Tool performance varied greatly in the number of called CNVs and bias for CNV lengths. Some tools had near-perfect recall of CNVs from arrays for some samples, but poor precision. Several tools had better performance for NA12878, which could be a result of overfitting. We suggest combining the best tools also based on different methodologies: GATK gCNV, Lumpy, DELLY, and cn.MOPS. Reducing the total number of called variants could potentially be assisted by the use of background panels for filtering of frequently called variants.

scholarly journals Submegabase copy number variations arise during cerebral cortical neurogenesis as revealed by single-cell whole-genome sequencing

2018 ◽  
Vol 115 (42) ◽  
pp. 10804-10809 ◽  
Author(s):  
Suzanne Rohrback ◽  
Craig April ◽  
Fiona Kaper ◽  
Richard R. Rivera ◽  
Christine S. Liu ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Single Cell ◽  
Genome Sequencing ◽  
Copy Number ◽  
Cortical Development ◽  
Building Blocks ◽  
Copy Number Variations ◽  
Whole Genome ◽  
Mature Brain ◽  
The Brain

Somatic copy number variations (CNVs) exist in the brain, but their genesis, prevalence, forms, and biological impact remain unclear, even within experimentally tractable animal models. We combined a transposase-based amplification (TbA) methodology for single-cell whole-genome sequencing with a bioinformatic approach for filtering unreliable CNVs (FUnC), developed from machine learning trained on lymphocyte V(D)J recombination. TbA–FUnC offered superior genomic coverage and removed >90% of false-positive CNV calls, allowing extensive examination of submegabase CNVs from over 500 cells throughout the neurogenic period of cerebral cortical development in Mus musculus. Thousands of previously undocumented CNVs were identified. Half were less than 1 Mb in size, with deletions 4× more common than amplification events, and were randomly distributed throughout the genome. However, CNV prevalence during embryonic cortical development was nonrandom, peaking at midneurogenesis with levels triple those found at younger ages before falling to intermediate quantities. These data identify pervasive small and large CNVs as early contributors to neural genomic mosaicism, producing genomically diverse cellular building blocks that form the highly organized, mature brain.

Abstract 2739: Low-pass whole genome sequencing detects copy number variations in circulating tumor DNA

2017 ◽  
Author(s):  
Xiaoji Chen ◽  
Jill M. Spoerke ◽  
Kathryn Yoh ◽  
Walter C. Darbonne ◽  
Ling-Yuh Huw ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Circulating Tumor Dna ◽  
Copy Number Variations ◽  
Whole Genome ◽  
Low Pass ◽  
Tumor Dna

Defining the heterogeneity landscape of pancreatic cancer copy number alterations.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15776-e15776
Author(s):  
Timour Baslan ◽  
Jie Wu ◽  
Yee Him Cheung ◽  
Jonathan Bermeo ◽  
Nevenka Dimitrova
Keyword(s):  
Pancreatic Cancer ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Somatic Mosaicism ◽  
Copy Number Variations ◽  
Whole Genome ◽  
Copy Number Alterations ◽  
Clonal Heterogeneity ◽  
Limited Effectiveness

e15776 Background: Pancreatic cancer (PDAC) is projected to become the second leading cause of cancer related mortality by 2030. Bulk whole genome sequencing studies of PDAC have characterized the landscape of clonal mutations and highlighted the prominence of copy number alterations (CNAs) in PDAC genomes. However, little is known with regards to the extent of sub-clonal heterogeneity of somatic CNAs and it is hypothesized that this heterogeneity is a contributing factor to the limited effectiveness of existing therapies. Methods: We retrieved absolute copy number information using bulk sparse whole genome sequencing of multi-region sampled PDAC samples as well as matching primary-metastasis tumor samples from over 100 patients. In addition, we analyzed copy number variations in a subset of these samples (n = 15) at single-cell resolution (~1000 cells in total). Results: We describe a detailed picture of sub-clonal CNAs genetic heterogeneity. Our results illustrate, among other findings, (1) extensive sub-clonal diversity of CNAs giving rise to many genetically unique sub-clonal cancer populations, (2) somatic mosaicism of chromosomal amplicons in single-cancer cells, (3) variation in the dosage of cancer genes, including the KRAS oncogene, in different tumor sub-clones and (4) somatic alterations, such as amplification of 8q11 containing the metastasis promoting gene IKBKB, associated with primary PDAC progression to liver metastasis. Conclusions: Our results offer an in-depth view of the sub-clonal heterogeneity of somatic CNAs in pancreatic cancer and illustrate ways in which such heterogeneity could lead to therapeutic resistance.

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

BMC Genomics ◽  
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.

Analysis of Copy Number Variations by Low-Depth Whole-Genome Sequencing in Fetuses with Congenital Cardiovascular Malformations

2020 ◽  
Vol 160 (11-12) ◽  
pp. 643-649
Author(s):  
Jiwei Huang ◽  
Xine Deng ◽  
Yuanliu Wang ◽  
Ning Tang ◽  
Dingyuan Zeng
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Clinical Data ◽  
Copy Number ◽  
Chromosomal Abnormalities ◽  
Copy Number Variations ◽  
Whole Genome ◽  
Cardiovascular Malformations ◽  
Group A ◽  
Group B

Congenital cardiovascular malformations (CVMs) due to genomic mutations bring a greater risk of morbidity and comorbidity and increase the risks related to heart surgery. However, reports on CVMs induced by genomic mutations based on actual clinical data are still limited. In this study, 181 fetuses were screened by fetal echocardiography for prenatal diagnosis of congenital heart disease, including 146 cases without ultrasound extracardiac findings (Group A) and 35 cases with ultrasound extracardiac findings (Group B). All cases were analyzed by clinical data, karyotyping, and low-depth whole-genome sequencing. The rates of chromosomal abnormalities in Groups A and B were 4.8% (7/146) and 37.1% (13/35), respectively. There was a significant difference in the incidence of chromosomal abnormalities between Groups A and B (p < 0.001). In Group A, CNV-seq identified copy number variations (CNVs) in an additional 9.6% (14/146) of cases with normal karyotypes, including 7 pathogenic CNVs and 7 variations of uncertain clinical significance. In Group B, one pathogenic CNV was identified in a case with normal karyotype. Chromosomal abnormality is one of the most common causes of CVM with extracardiac defects. Low-depth whole-genome sequencing could effectively become a first approach for CNV diagnosis in fetuses with CVMs.

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

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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