The ViReflow pipeline enables user friendly large scale viral consensus genome reconstruction.
The ViReflow pipeline enables user friendly large scale viral consensus genome reconstruction.
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DOI：
10.1038/s41598-022-09035-w
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发表时间：
2022-03-24
影响因子：
4.6
通讯作者：
Knight R
中科院分区：
文献类型：
--
作者：
Moshiri N;Fisch KM;Birmingham A;DeHoff P;Yeo GW;Jepsen K;Laurent LC;Knight R
在整个COVID-19大流行期间，大规模测序和数据共享工作使全世界能够实时监测新型SARS-CoV-2毒株，其结果为公共卫生官员提供了可采取行动的信息，以防止病毒传播。然而，出色的排序带来了出色的计算，虽然云计算平台将高性能计算直接带到所有寻求它的人手中，但云计算集群的最佳设计和配置需要大量的系统管理专业知识。我们开发了ViReflow，这是一种用户友好的病毒共识序列重建管道，可以利用Amazon Web Services（AWS）云计算资源和Reflow系统快速分析病毒序列数据集。ViReflow是专门为应对COVID-19大流行而开发的，但它适用于任何病毒病原体。重要的是，当与足够的计算资源一起使用时，ViReflow可以在10分钟内从1000个SARS-CoV-2样本的扩增子序列数据中修剪、映射、调用变体和调用共有序列，而无需用户干预。ViReflow的简单性、灵活性和可扩展性使其成为病毒分子流行病学研究的理想工具。
Throughout the COVID-19 pandemic, massive sequencing and data sharing efforts enabled the real-time surveillance of novel SARS-CoV-2 strains throughout the world, the results of which provided public health officials with actionable information to prevent the spread of the virus. However, with great sequencing comes great computation, and while cloud computing platforms bring high-performance computing directly into the hands of all who seek it, optimal design and configuration of a cloud compute cluster requires significant system administration expertise. We developed ViReflow, a user-friendly viral consensus sequence reconstruction pipeline enabling rapid analysis of viral sequence datasets leveraging Amazon Web Services (AWS) cloud compute resources and the Reflow system. ViReflow was developed specifically in response to the COVID-19 pandemic, but it is general to any viral pathogen. Importantly, when utilized with sufficient compute resources, ViReflow can trim, map, call variants, and call consensus sequences from amplicon sequence data from 1000 SARS-CoV-2 samples at 1000X depth in < 10 min, with no user intervention. ViReflow’s simplicity, flexibility, and scalability make it an ideal tool for viral molecular epidemiological efforts.
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DOI：
10.1097/qai.0000000000002612
发表时间：
2021-04-15
期刊：
Journal of acquired immune deficiency syndromes (1999)
影响因子：
--
作者：
Moshiri N;Smith DM;Mirarab S
通讯作者：
Mirarab S
影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
DOI：
10.1093/bioinformatics/btab015
发表时间：
2021-07-19
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Posada-Céspedes S;Seifert D;Topolsky I;Jablonski KP;Metzner KJ;Beerenwinkel N
通讯作者：
Beerenwinkel N
影响因子：
3.7
作者：
Bhoyar RC;Jain A;Sehgal P;Divakar MK;Sharma D;Imran M;Jolly B;Ranjan G;Rophina M;Sharma S;Siwach S;Pandhare K;Sahoo S;Sahoo M;Nayak A;Mohanty JN;Das J;Bhandari S;Mathur SK;Kumar A;Sahlot R;Rojarani P;Lakshmi JV;Surekha A;Sekhar PC;Mahajan S;Masih S;Singh P;Kumar V;Jose B;Mahajan V;Gupta V;Gupta R;Arumugam P;Singh A;Nandy A;P V R;Jha RM;Kumari A;Gandotra S;Rao V;Faruq M;Kumar S;Reshma G B;Varma G N;Roy SS;Sengupta A;Chattopadhyay S;Singhal K;Pradhan S;Jha D;Naushin S;Wadhwa S;Tyagi N;Poojary M;Scaria V;Sivasubbu S
通讯作者：
Sivasubbu S
影响因子：
6.4
作者：
Karthikeyan S;Nguyen A;McDonald D;Zong Y;Ronquillo N;Ren J;Zou J;Farmer S;Humphrey G;Henderson D;Javidi T;Messer K;Anderson C;Schooley R;Martin NK;Knight R
通讯作者：
Knight R