IDseq-An open source cloud-based pipeline and analysis service for metagenomic pathogen detection and monitoring.
IDseq-An open source cloud-based pipeline and analysis service for metagenomic pathogen detection and monitoring.
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DOI：
10.1093/gigascience/giaa111
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发表时间：
2020-10-15
期刊：
影响因子：
9.2
通讯作者：
DeRisi JL
中科院分区：
文献类型：
--
作者：
Kalantar KL;Carvalho T;de Bourcy CFA;Dimitrov B;Dingle G;Egger R;Han J;Holmes OB;Juan YF;King R;Kislyuk A;Lin MF;Mariano M;Morse T;Reynoso LV;Cruz DR;Sheu J;Tang J;Wang J;Zhang MA;Zhong E;Ahyong V;Lay S;Chea S;Bohl JA;Manning JE;Tato CM;DeRisi JL
宏基因组下一代测序 (mNGS) 能够快速、公正地检测和鉴定微生物，无需病原体特异性试剂、培养或微生物景观的先验知识。 mNGS 数据分析需要一系列计算密集型处理步骤来准确确定样品的微生物组成。现有的 mNGS 数据分析工具通常需要生物信息学专业知识和本地服务器级硬件资源的访问权限。对于许多研究实验室来说，这构成了一个障碍，特别是在资源有限的环境中。我们推出 IDseq，这是一种基于云的开源宏基因组学管道和服务，用于全球病原体检测和监测 (https://idseq.net)。 IDseq Portal 接受原始 mNGS 数据，执行宿主和质量过滤步骤，然后执行基于组装的比对管道，从而将读数和重叠群分配到分类类别。分类相对丰度在易于使用的网络应用程序中报告和可视化，以促进数据解释和假设生成。此外，IDseq 支持环境背景模型生成和自动内部尖峰控制识别，提供对数据解释至关重要的统计数据。 IDseq 的设计目的是检测新型病原体。在这里，我们使用合成进化的病毒序列和真实世界样本对新型病毒检测能力进行基准测试，包括对从感染最近出现的 SARS-CoV-2 的中国武汉游客在柬埔寨当地获取和处理的鼻咽拭子样本进行 IDseq 分析。 IDseq 门户降低了 mNGS 数据分析的准入门槛，使实验室科学家、临床医生和生物信息学家能够从 mNGS 数据集中获取已知和新型病原体的见解。
Metagenomic next-generation sequencing (mNGS) has enabled the rapid, unbiased detection and identification of microbes without pathogen-specific reagents, culturing, or a priori knowledge of the microbial landscape. mNGS data analysis requires a series of computationally intensive processing steps to accurately determine the microbial composition of a sample. Existing mNGS data analysis tools typically require bioinformatics expertise and access to local server-class hardware resources. For many research laboratories, this presents an obstacle, especially in resource-limited environments. We present IDseq, an open source cloud-based metagenomics pipeline and service for global pathogen detection and monitoring (https://idseq.net). The IDseq Portal accepts raw mNGS data, performs host and quality filtration steps, then executes an assembly-based alignment pipeline, which results in the assignment of reads and contigs to taxonomic categories. The taxonomic relative abundances are reported and visualized in an easy-to-use web application to facilitate data interpretation and hypothesis generation. Furthermore, IDseq supports environmental background model generation and automatic internal spike-in control recognition, providing statistics that are critical for data interpretation. IDseq was designed with the specific intent of detecting novel pathogens. Here, we benchmark novel virus detection capability using both synthetically evolved viral sequences and real-world samples, including IDseq analysis of a nasopharyngeal swab sample acquired and processed locally in Cambodia from a tourist from Wuhan, China, infected with the recently emergent SARS-CoV-2. The IDseq Portal reduces the barrier to entry for mNGS data analysis and enables bench scientists, clinicians, and bioinformaticians to gain insight from mNGS datasets for both known and novel pathogens.
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影响因子：
3.7
作者：
Laurence M;Hatzis C;Brash DE
通讯作者：
Brash DE
影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
影响因子：
14.9
作者：
Ainsworth D;Sternberg MJE;Raczy C;Butcher SA
通讯作者：
Butcher SA
影响因子：
46.9
作者：
Luo C;Knight R;Siljander H;Knip M;Xavier RJ;Gevers D
通讯作者：
Gevers D
DOI：
10.1146/annurev-pathmechdis-012418-012751
发表时间：
2019-01-24
期刊：
Annual review of pathology
影响因子：
--
作者：
Gu W;Miller S;Chiu CY
通讯作者：
Chiu CY