Supporting data for "IDseq- An Open Source Cloud-based Pipeline and Analysis Service for Metagenomic Pathogen Detection and Monitoring"

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. 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 which 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.

宏基因组下一代测序（Metagenomic next generation sequencing, mNGS）无需病原体特异性试剂、培养流程或微生物群落先验知识，即可实现快速、无偏倚的微生物检测与鉴定。mNGS数据分析需依托一系列计算密集型处理步骤，方可准确测定样本中的微生物组成。现有mNGS数据分析工具通常要求使用者具备生物信息学专业知识，且需使用本地服务器级硬件资源，这对多数研究实验室而言是一项障碍，在资源匮乏的环境中尤为突出。本研究推出IDseq：一款基于云计算的开源宏基因组学流程与服务平台，用于全球范围内的病原体检测与监测。IDseq门户可接收原始mNGS数据，完成宿主序列过滤与质量控制步骤，随后运行基于组装的比对流程，将测序读段（reads）与重叠群（contigs）归类至相应的分类学类别。该平台会输出分类学相对丰度信息，并通过易用的网页应用进行可视化展示，助力研究人员解读数据与生成研究假说。此外，IDseq支持环境背景模型构建与内参spike-in对照自动识别，提供对数据解读至关重要的统计指标。IDseq的设计初衷为检测新型病原体。本研究通过人工合成的病毒序列与真实世界样本，对IDseq的新型病毒检测能力进行基准测试，其中包括对柬埔寨当地采集并处理的鼻咽拭子样本的分析——该样本来自一名感染了新近出现的新型冠状病毒（SARS-CoV-2）的中国武汉游客。IDseq门户降低了mNGS数据分析的入门门槛，使得实验室科研人员、临床医生与生物信息学家均可从mNGS数据集当中，针对已知与新型病原体获取研究洞见。