A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples.
A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples.
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DOI：
10.1101/gr.171934.113
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发表时间：
2014-07
期刊：
影响因子：
7
通讯作者：
Chiu CY
中科院分区：
文献类型：
--
作者：
Naccache SN;Federman S;Veeraraghavan N;Zaharia M;Lee D;Samayoa E;Bouquet J;Greninger AL;Luk KC;Enge B;Wadford DA;Messenger SL;Genrich GL;Pellegrino K;Grard G;Leroy E;Schneider BS;Fair JN;Martínez MA;Isa P;Crump JA;DeRisi JL;Sittler T;Hackett J Jr;Miller S;Chiu CY
无偏的下一代测序（NGS）方法能够在临床微生物学实验室中进行全面的病原体检测，并在公共卫生监测、疫情调查和传染病诊断方面具有众多应用。然而，该技术的实际部署受到准确分析结果和在临床相关时间范围内的生物信息学挑战的阻碍。在这里，我们描述了SURPI（“基于序列的超快速病原体识别”），一种用于从临床样本生成的复杂宏基因组NGS数据中进行病原体识别的计算管道，并展示了该管道在分析237个临床样本（包括超过11亿个序列）中的使用。SURPI可部署在基于云的服务器和独立服务器上，利用两种最先进的对齐器进行加速分析，SNAP和RAPSearch，它们与现有的生物信息学工具一样准确，但性能更快。在快速模式下，SURPI通过在11分钟至5小时内扫描7- 5亿个读段的数据集来检测病毒和细菌，而在综合模式下，识别所有已知微生物，然后在50分钟至16小时内重新组装和蛋白质同源性搜索不同的病毒。SURPI还直接为急性病患者的实时微生物诊断做出了贡献，强调了其在开发无偏倚的基于NGS的感染性疾病临床检测中的潜在关键作用，这些检测需要快速周转时间。
Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI (“sequence-based ultrarapid pathogen identification”), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7–500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times.
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