Copy number variation detection workflow using next generation sequencing data

Prashanthi Dharanipragada, N. Parekh
{"title":"Copy number variation detection workflow using next generation sequencing data","authors":"Prashanthi Dharanipragada, N. Parekh","doi":"10.1109/BSB.2016.7552117","DOIUrl":null,"url":null,"abstract":"In the last decade, discovery of copy number variations (CNVs) have dramatically changed our understanding of differences between individuals. CNVs include both additional copies of sequence (duplications) and loss of genetic material (deletions) and provide an alternate paradigm for the genetic basis of human diseases. Genome-wide CNV detection is now possible using high-throughput, low-cost next generation sequencing (NGS) methods. Nature of NGS data demands various preprocessing and pretreatment steps before extracting any meaningful information. Among the plethora of variant calling methods available, R-based methods offer flexible environment, facilitating choice of various methods depending on the type of data or type of analysis to be performed. Here we give a pipeline for various steps involved in CNV detection in NGS data using R-based algorithms and packages.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BSB.2016.7552117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In the last decade, discovery of copy number variations (CNVs) have dramatically changed our understanding of differences between individuals. CNVs include both additional copies of sequence (duplications) and loss of genetic material (deletions) and provide an alternate paradigm for the genetic basis of human diseases. Genome-wide CNV detection is now possible using high-throughput, low-cost next generation sequencing (NGS) methods. Nature of NGS data demands various preprocessing and pretreatment steps before extracting any meaningful information. Among the plethora of variant calling methods available, R-based methods offer flexible environment, facilitating choice of various methods depending on the type of data or type of analysis to be performed. Here we give a pipeline for various steps involved in CNV detection in NGS data using R-based algorithms and packages.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用下一代测序数据的拷贝数变异检测工作流程
在过去的十年中,拷贝数变异(CNVs)的发现极大地改变了我们对个体差异的理解。CNVs包括序列的额外拷贝(重复)和遗传物质的损失(缺失),并为人类疾病的遗传基础提供了另一种范式。使用高通量、低成本的下一代测序(NGS)方法,全基因组CNV检测现在成为可能。NGS数据的性质要求在提取有意义的信息之前进行各种预处理和预处理。在大量可用的变量调用方法中,基于r的方法提供了灵活的环境,便于根据数据类型或要执行的分析类型选择各种方法。在这里,我们给出了使用基于r的算法和包在NGS数据中进行CNV检测的各个步骤的管道。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Statistical discrimination of breast cancer microarray data Identification of conserved regulatory motif signatures in human miRNA upstream regions Improving extraction of protein — Protein interaction datasets from KUPS using hashing approach Extraction of associated quantitative traits by association mining Prediction of catalytic site of proteins based on amino acid triads approach using non parametric function
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1