PAIPline: pathogen identification in metagenomic and clinical next generation sequencing samples.
PAIPline: pathogen identification in metagenomic and clinical next generation sequencing samples.
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DOI：
10.1093/bioinformatics/bty595
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发表时间：
2018-09-01
期刊：
影响因子：
--
通讯作者：
Nitsche A
中科院分区：
文献类型：
--
作者：
Andrusch A;Dabrowski PW;Klenner J;Tausch SH;Kohl C;Osman AA;Renard BY;Nitsche A
下一代测序（NGS）为研究人员提供了在研究和诊断环境中表征宏基因组和临床样品的强大工具。NGS允许以无偏见的方式开放查看可用于病原体检测的样品，而无需事先假设可能的病原体。然而，用于病原体检测的NGS数据集存在不同的障碍，例如病原体与宿主读段的比例非常不利。除了经常出现假阳性和不相关的微生物（如污染物）外，工具经常受到低病原体载量样本的挑战，并且可能不会报告低于一定阈值的微生物。此外，一些宏基因组分析工具仅关注一组特定的病原体，例如细菌。我们提出了PAIPline，这是一个专门设计用于解决诊断样品中病原体检测相关问题的生物信息学管道。PAIPline特别注重用户友好性，并将从预处理到解决模糊读数和过滤到可视化的所有必要步骤封装在一个工具中。与现有工具相比，PAIPline在保持灵敏度的同时更具体。这在比较评估中示出，其中PAIPline在先前公开的充分表征的数据集上与其他公知的宏基因组分析工具一起沿着基准化。此外，作为国际合作项目的一部分，PAIPline被应用于当时病因不明的出血热暴发样本。所呈现的结果表明，PAIPline可以作为一个强大的，可靠的，用户友好的，适应性强，可推广的独立软件，用于NGS样品的诊断，并作为进一步的下游分析的垫脚石。PAIPline可在https://gitlab.com/rki_bioinformatics/paipline上免费获得。
Next generation sequencing (NGS) has provided researchers with a powerful tool to characterize metagenomic and clinical samples in research and diagnostic settings. NGS allows an open view into samples useful for pathogen detection in an unbiased fashion and without prior hypothesis about possible causative agents. However, NGS datasets for pathogen detection come with different obstacles, such as a very unfavorable ratio of pathogen to host reads. Alongside often appearing false positives and irrelevant organisms, such as contaminants, tools are often challenged by samples with low pathogen loads and might not report organisms present below a certain threshold. Furthermore, some metagenomic profiling tools are only focused on one particular set of pathogens, for example bacteria. We present PAIPline, a bioinformatics pipeline specifically designed to address problems associated with detecting pathogens in diagnostic samples. PAIPline particularly focuses on userfriendliness and encapsulates all necessary steps from preprocessing to resolution of ambiguous reads and filtering up to visualization in a single tool. In contrast to existing tools, PAIPline is more specific while maintaining sensitivity. This is shown in a comparative evaluation where PAIPline was benchmarked along other well-known metagenomic profiling tools on previously published well-characterized datasets. Additionally, as part of an international cooperation project, PAIPline was applied to an outbreak sample of hemorrhagic fevers of then unknown etiology. The presented results show that PAIPline can serve as a robust, reliable, user-friendly, adaptable and generalizable stand-alone software for diagnostics from NGS samples and as a stepping stone for further downstream analyses. PAIPline is freely available under https://gitlab.com/rki_bioinformatics/paipline.
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