Literature DB >> 32153642

SECNVs: A Simulator of Copy Number Variants and Whole-Exome Sequences From Reference Genomes.

Yue Xing1,2,3, Alan R Dabney2, Xiao Li4, Guosong Wang5, Clare A Gill5, Claudio Casola6.   

Abstract

Copy number variants are duplications and deletions of the genome that play an important role in phenotypic changes and human disease. Many software applications have been developed to detect copy number variants using either whole-genome sequencing or whole-exome sequencing data. However, there is poor agreement in the results from these applications. Simulated datasets containing copy number variants allow comprehensive comparisons of the operating characteristics of existing and novel copy number variant detection methods. Several software applications have been developed to simulate copy number variants and other structural variants in whole-genome sequencing data. However, none of the applications reliably simulate copy number variants in whole-exome sequencing data. We have developed and tested Simulator of Exome Copy Number Variants (SECNVs), a fast, robust and customizable software application for simulating copy number variants and whole-exome sequences from a reference genome. SECNVs is easy to install, implements a wide range of commands to customize simulations, can output multiple samples at once, and incorporates a pipeline to output rearranged genomes, short reads and BAM files in a single command. Variants generated by SECNVs are detected with high sensitivity and precision by tools commonly used to detect copy number variants. SECNVs is publicly available at https://github.com/YJulyXing/SECNVs.
Copyright © 2020 Xing, Dabney, Li, Wang, Gill and Casola.

Entities:  

Keywords:  copy number variation; read depth; simulation; software; whole-exome sequencing

Year:  2020        PMID: 32153642      PMCID: PMC7046838          DOI: 10.3389/fgene.2020.00082

Source DB:  PubMed          Journal:  Front Genet        ISSN: 1664-8021            Impact factor:   4.599


  46 in total

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2.  A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

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3.  Genome-Wide Copy Number Variation Detection Using NGS: Data Analysis and Interpretation.

Authors:  Wei Shen; Philippe Szankasi; Jacob Durtschi; Todd W Kelley; Xinjie Xu
Journal:  Methods Mol Biol       Date:  2019

Review 4.  Connecting Proteomics to Next-Generation Sequencing: Proteogenomics and Its Current Applications in Biology.

Authors:  Teck Yew Low; M Aiman Mohtar; Mia Yang Ang; Rahman Jamal
Journal:  Proteomics       Date:  2018-12-11       Impact factor: 3.984

5.  Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth.

Authors:  Menachem Fromer; Jennifer L Moran; Kimberly Chambert; Eric Banks; Sarah E Bergen; Douglas M Ruderfer; Robert E Handsaker; Steven A McCarroll; Michael C O'Donovan; Michael J Owen; George Kirov; Patrick F Sullivan; Christina M Hultman; Pamela Sklar; Shaun M Purcell
Journal:  Am J Hum Genet       Date:  2012-10-05       Impact factor: 11.025

6.  Comparative analysis of methods for identifying somatic copy number alterations from deep sequencing data.

Authors:  Amjad Alkodsi; Riku Louhimo; Sampsa Hautaniemi
Journal:  Brief Bioinform       Date:  2014-03-05       Impact factor: 11.622

7.  cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate.

Authors:  Günter Klambauer; Karin Schwarzbauer; Andreas Mayr; Djork-Arné Clevert; Andreas Mitterecker; Ulrich Bodenhofer; Sepp Hochreiter
Journal:  Nucleic Acids Res       Date:  2012-02-01       Impact factor: 16.971

8.  Pysim-sv: a package for simulating structural variation data with GC-biases.

Authors:  Yuchao Xia; Yun Liu; Minghua Deng; Ruibin Xi
Journal:  BMC Bioinformatics       Date:  2017-03-14       Impact factor: 3.169

9.  Fast and accurate short read alignment with Burrows-Wheeler transform.

Authors:  Heng Li; Richard Durbin
Journal:  Bioinformatics       Date:  2009-05-18       Impact factor: 6.937

Review 10.  Whole-genome CNV analysis: advances in computational approaches.

Authors:  Mehdi Pirooznia; Fernando S Goes; Peter P Zandi
Journal:  Front Genet       Date:  2015-04-13       Impact factor: 4.599

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  2 in total

1.  Identification of Copy Number Alterations from Next-Generation Sequencing Data.

Authors:  Sheida Nabavi; Fatima Zare
Journal:  Adv Exp Med Biol       Date:  2022       Impact factor: 2.622

2.  CNVind: an open source cloud-based pipeline for rare CNVs detection in whole exome sequencing data based on the depth of coverage.

Authors:  Wiktor Kuśmirek; Robert Nowak
Journal:  BMC Bioinformatics       Date:  2022-03-05       Impact factor: 3.169

  2 in total

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