无服务器计算中RNA-seq读取的大规模并行比对

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data and Cognitive Computing Pub Date : 2023-05-15 DOI:10.3390/bdcc7020098
Pietro Cinaglia, J. L. Vázquez-Poletti, M. Cannataro
{"title":"无服务器计算中RNA-seq读取的大规模并行比对","authors":"Pietro Cinaglia, J. L. Vázquez-Poletti, M. Cannataro","doi":"10.3390/bdcc7020098","DOIUrl":null,"url":null,"abstract":"In recent years, the use of Cloud infrastructures for data processing has proven useful, with a computing potential that is not affected by the limitations of a local infrastructure. In this context, Serverless computing is the fastest-growing Cloud service model due to its auto-scaling methodologies, reliability, and fault tolerance. We present a solution based on in-house Serverless infrastructure, which is able to perform large-scale RNA-seq data analysis focused on the mapping of sequencing reads to a reference genome. The main contribution was bringing the computation of genomic data into serverless computing, focusing on RNA-seq read-mapping to a reference genome, as this is the most time-consuming task for some pipelines. The proposed solution handles massive parallel instances to maximize the efficiency in terms of running time. We evaluated the performance of our solution by performing two main tests, both based on the mapping of RNA-seq reads to Human GRCh38. Our experiments demonstrated a reduction of 79.838%, 90.079%, and 96.382%, compared to the local environments with 16, 8, and 4 virtual cores, respectively. Furthermore, serverless limitations were investigated.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Massive Parallel Alignment of RNA-seq Reads in Serverless Computing\",\"authors\":\"Pietro Cinaglia, J. L. Vázquez-Poletti, M. Cannataro\",\"doi\":\"10.3390/bdcc7020098\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, the use of Cloud infrastructures for data processing has proven useful, with a computing potential that is not affected by the limitations of a local infrastructure. In this context, Serverless computing is the fastest-growing Cloud service model due to its auto-scaling methodologies, reliability, and fault tolerance. We present a solution based on in-house Serverless infrastructure, which is able to perform large-scale RNA-seq data analysis focused on the mapping of sequencing reads to a reference genome. The main contribution was bringing the computation of genomic data into serverless computing, focusing on RNA-seq read-mapping to a reference genome, as this is the most time-consuming task for some pipelines. The proposed solution handles massive parallel instances to maximize the efficiency in terms of running time. We evaluated the performance of our solution by performing two main tests, both based on the mapping of RNA-seq reads to Human GRCh38. Our experiments demonstrated a reduction of 79.838%, 90.079%, and 96.382%, compared to the local environments with 16, 8, and 4 virtual cores, respectively. Furthermore, serverless limitations were investigated.\",\"PeriodicalId\":36397,\"journal\":{\"name\":\"Big Data and Cognitive Computing\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2023-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Big Data and Cognitive Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/bdcc7020098\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data and Cognitive Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/bdcc7020098","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 1

摘要

近年来,使用云基础设施进行数据处理已被证明是有用的,其计算潜力不受本地基础设施限制的影响。在这种情况下,无服务器计算由于其自动扩展方法、可靠性和容错性而成为增长最快的云服务模型。我们提出了一种基于内部Serverless基础设施的解决方案,该解决方案能够执行大规模RNA-seq数据分析,重点是将测序读数映射到参考基因组。主要贡献是将基因组数据的计算纳入无服务器计算,重点是RNA-seq读取到参考基因组的映射,因为这对一些管道来说是最耗时的任务。所提出的解决方案处理大量并行实例,以最大限度地提高运行时间的效率。我们通过进行两项主要测试来评估我们的解决方案的性能,这两项测试都基于RNA-seq读数与人类GRCh38的映射。与具有16个、8个和4个虚拟核心的本地环境相比,我们的实验分别减少了79.838%、90.0079%和96.382%。此外,还研究了无服务器限制。
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
Massive Parallel Alignment of RNA-seq Reads in Serverless Computing
In recent years, the use of Cloud infrastructures for data processing has proven useful, with a computing potential that is not affected by the limitations of a local infrastructure. In this context, Serverless computing is the fastest-growing Cloud service model due to its auto-scaling methodologies, reliability, and fault tolerance. We present a solution based on in-house Serverless infrastructure, which is able to perform large-scale RNA-seq data analysis focused on the mapping of sequencing reads to a reference genome. The main contribution was bringing the computation of genomic data into serverless computing, focusing on RNA-seq read-mapping to a reference genome, as this is the most time-consuming task for some pipelines. The proposed solution handles massive parallel instances to maximize the efficiency in terms of running time. We evaluated the performance of our solution by performing two main tests, both based on the mapping of RNA-seq reads to Human GRCh38. Our experiments demonstrated a reduction of 79.838%, 90.079%, and 96.382%, compared to the local environments with 16, 8, and 4 virtual cores, respectively. Furthermore, serverless limitations were investigated.
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
Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
期刊最新文献
A Survey of Incremental Deep Learning for Defect Detection in Manufacturing BNMI-DINA: A Bayesian Cognitive Diagnosis Model for Enhanced Personalized Learning Semantic Similarity of Common Verbal Expressions in Older Adults through a Pre-Trained Model Knowledge-Based and Generative-AI-Driven Pedagogical Conversational Agents: A Comparative Study of Grice’s Cooperative Principles and Trust Distributed Bayesian Inference for Large-Scale IoT Systems
×
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