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

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

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.

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

2019 ◽  
Vol 9 (1) ◽  
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.

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

GigaScience ◽  
2021 ◽  
Vol 10 (11) ◽  
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.

scholarly journals Whole genome sequencing reveals high differentiation, low levels of genetic diversity and short runs of homozygosity among Swedish wels catfish

Heredity ◽  
2021 ◽  
Author(s):  
Axel Jensen ◽  
Mette Lillie ◽  
Kristofer Bergström ◽  
Per Larsson ◽  
Jacob Höglund
Keyword(s):  
Genetic Diversity ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome Sequencing Data ◽  
Peripheral Populations ◽  
Whole Genome ◽  
Runs Of Homozygosity ◽  
Sequencing Data ◽  
Isolated Populations ◽  
Native Populations

AbstractThe use of genetic markers in the context of conservation is largely being outcompeted by whole-genome data. Comparative studies between the two are sparse, and the knowledge about potential effects of this methodology shift is limited. Here, we used whole-genome sequencing data to assess the genetic status of peripheral populations of the wels catfish (Silurus glanis), and discuss the results in light of a recent microsatellite study of the same populations. The Swedish populations of the wels catfish have suffered from severe declines during the last centuries and persists in only a few isolated water systems. Fragmented populations generally are at greater risk of extinction, for example due to loss of genetic diversity, and may thus require conservation actions. We sequenced individuals from the three remaining native populations (Båven, Emån, and Möckeln) and one reintroduced population of admixed origin (Helge å), and found that genetic diversity was highest in Emån but low overall, with strong differentiation among the populations. No signature of recent inbreeding was found, but a considerable number of short runs of homozygosity were present in all populations, likely linked to historically small population sizes and bottleneck events. Genetic substructure within any of the native populations was at best weak. Individuals from the admixed population Helge å shared most genetic ancestry with the Båven population (72%). Our results are largely in agreement with the microsatellite study, and stresses the need to protect these isolated populations at the northern edge of the distribution of the species.

Sign in / Sign up

Export Citation Format

Share Document