NanoBLASTer:牛津纳米孔单分子测序reads的快速定位和表征

M. R. Amin, S. Skiena, M. Schatz
{"title":"NanoBLASTer:牛津纳米孔单分子测序reads的快速定位和表征","authors":"M. R. Amin, S. Skiena, M. Schatz","doi":"10.1109/ICCABS.2016.7802776","DOIUrl":null,"url":null,"abstract":"The quality of the Oxford Nanopores long DNA sequence reads has been, to date, lower than other technologies, causing great interest to develop new algorithms that can make use of the data. So far, alignment methods including LAST, BLAST, BWA-MEM and GraphMap have been used to analyze these sequences. However, each of these tools has significant challenges to use with these data: LAST and BLAST require considerable processing time for high sensitivity, BWA-MEM has the smallest average alignment length, and GraphMap aligns many random strings with moderate accuracy. To address these challenges we developed a new read aligner called NanoBLASTer specifically designed for long nanopore reads. In experiments resequencing the well-studied S. cerevisiae (yeast) and Escherichia coli (E. coli) genomes, we show that our algorithm produces longer alignments with higher overall sensitivity than LAST, BLAST and BWA-MEM. We also show that the runtime of NanoBLASTer is faster than GraphMap, BLAST and BWA-MEM.","PeriodicalId":306466,"journal":{"name":"2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"NanoBLASTer: Fast alignment and characterization of Oxford Nanopore single molecule sequencing reads\",\"authors\":\"M. R. Amin, S. Skiena, M. Schatz\",\"doi\":\"10.1109/ICCABS.2016.7802776\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The quality of the Oxford Nanopores long DNA sequence reads has been, to date, lower than other technologies, causing great interest to develop new algorithms that can make use of the data. So far, alignment methods including LAST, BLAST, BWA-MEM and GraphMap have been used to analyze these sequences. However, each of these tools has significant challenges to use with these data: LAST and BLAST require considerable processing time for high sensitivity, BWA-MEM has the smallest average alignment length, and GraphMap aligns many random strings with moderate accuracy. To address these challenges we developed a new read aligner called NanoBLASTer specifically designed for long nanopore reads. In experiments resequencing the well-studied S. cerevisiae (yeast) and Escherichia coli (E. coli) genomes, we show that our algorithm produces longer alignments with higher overall sensitivity than LAST, BLAST and BWA-MEM. We also show that the runtime of NanoBLASTer is faster than GraphMap, BLAST and BWA-MEM.\",\"PeriodicalId\":306466,\"journal\":{\"name\":\"2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCABS.2016.7802776\",\"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 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCABS.2016.7802776","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

迄今为止,牛津纳米孔长DNA序列读取的质量一直低于其他技术,这引起了人们对开发可以利用这些数据的新算法的极大兴趣。目前已使用LAST、BLAST、BWA-MEM和GraphMap等比对方法对这些序列进行分析。然而,这些工具在处理这些数据时都面临着重大挑战:LAST和BLAST需要相当长的处理时间才能获得高灵敏度,BWA-MEM具有最小的平均对齐长度,而GraphMap以中等精度对齐许多随机字符串。为了解决这些挑战,我们开发了一种名为NanoBLASTer的新型读取校准器,专门用于长纳米孔读取。在对酿酒酵母和大肠杆菌基因组进行重测序的实验中,我们发现我们的算法比LAST、BLAST和BWA-MEM产生更长的序列,总体灵敏度更高。我们还表明,NanoBLASTer的运行速度比GraphMap、BLAST和BWA-MEM快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
NanoBLASTer: Fast alignment and characterization of Oxford Nanopore single molecule sequencing reads
The quality of the Oxford Nanopores long DNA sequence reads has been, to date, lower than other technologies, causing great interest to develop new algorithms that can make use of the data. So far, alignment methods including LAST, BLAST, BWA-MEM and GraphMap have been used to analyze these sequences. However, each of these tools has significant challenges to use with these data: LAST and BLAST require considerable processing time for high sensitivity, BWA-MEM has the smallest average alignment length, and GraphMap aligns many random strings with moderate accuracy. To address these challenges we developed a new read aligner called NanoBLASTer specifically designed for long nanopore reads. In experiments resequencing the well-studied S. cerevisiae (yeast) and Escherichia coli (E. coli) genomes, we show that our algorithm produces longer alignments with higher overall sensitivity than LAST, BLAST and BWA-MEM. We also show that the runtime of NanoBLASTer is faster than GraphMap, BLAST and BWA-MEM.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Curvelet-based texture classification of critical Gleason patterns of prostate histological images NanoBLASTer: Fast alignment and characterization of Oxford Nanopore single molecule sequencing reads Identifying hotspots in five year survival electronic health records of older adults HRVCam: A software for real-time feedback of heart rate and HRV A deep learning-based segmentation method for brain tumor in MR images
×
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