{"title":"使用下一代测序数据的拷贝数变异检测工作流程","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":"{\"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}","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}
Copy number variation detection workflow using next generation sequencing data
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.