The electronic tree of life (eToL): a net of long probes to characterize the microbiome from RNA-seq data.
The electronic tree of life (eToL): a net of long probes to characterize the microbiome from RNA-seq data.
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DOI：
10.1186/s12866-022-02671-2
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发表时间：
2022-12-22
期刊：
影响因子：
4.2
通讯作者：
Lathe, Richard
中科院分区：
文献类型：
--
作者：
Hu, Xinyue;Haas, Juergen G.;Lathe, Richard
微生物组分析通常需要基于PCR或宏基因组鸟枪测序、复杂的程序和大量数据。基于广泛可用的RNA-seq数据的替代方法受到限制，因为微生物/病毒的转录组与宿主的转录组之间的序列相似性，再加上此类文库中宿主序列的极度丰富。目前的方法也仅限于特定的微生物组。需要涵盖整个生命树的微生物组分析的替代方法。我们报告了一种方法，专门检索人类组织RNA-seq数据中的非人类序列。对于细胞微生物，我们使用了生物信息学“网”，基于过滤的64-mer序列，该序列是从生命之树（“电子生命树”，eToL）中的小亚基核糖体RNA（rRNA）序列设计的，以全面（98%）捕获靶组织中存在的所有非人类rRNA序列。使用脑作为模型，检索匹配读段，重新排除人类相关序列，然后构建重叠群和物种鉴定，然后确认相应物种组的丰度和身份。我们提供了自动化这种分析的方法。与宏基因组学相比，该方法将计算时间减少了>1000倍。对于病毒来说，变异方法是必要的。同样，由于病毒和人类序列之间的显著匹配，“剥离”方法是必不可少的。污染过程中的工作是一个潜在的问题，我们讨论的策略来规避这个问题。为了说明该方法的多功能性，我们报告了使用eToL方法来明确识别整个生命树中人类组织RNA-seq数据中的外源微生物和病毒序列，包括细菌，叶绿体，基底真核生物，真菌和Holozoa/后生动物，并讨论了所涉及的技术和生物信息学挑战。这种通用方法可能在微生物组分析（包括诊断）中得到广泛应用。在线版本包含补充材料，可通过10.1186/s12866-022-02671-2获得。
Microbiome analysis generally requires PCR-based or metagenomic shotgun sequencing, sophisticated programs, and large volumes of data. Alternative approaches based on widely available RNA-seq data are constrained because of sequence similarities between the transcriptomes of microbes/viruses and those of the host, compounded by the extreme abundance of host sequences in such libraries. Current approaches are also limited to specific microbial groups. There is a need for alternative methods of microbiome analysis that encompass the entire tree of life. We report a method to specifically retrieve non-human sequences in human tissue RNA-seq data. For cellular microbes we used a bioinformatic 'net', based on filtered 64-mer sequences designed from small subunit ribosomal RNA (rRNA) sequences across the Tree of Life (the 'electronic tree of life', eToL), to comprehensively (98%) entrap all non-human rRNA sequences present in the target tissue. Using brain as a model, retrieval of matching reads, re-exclusion of human-related sequences, followed by contig building and species identification, is followed by confirmation of the abundance and identity of the corresponding species groups. We provide methods to automate this analysis. The method reduces the computation time versus metagenomics by a factor of >1000. A variant approach is necessary for viruses. Again, because of significant matches between viral and human sequences, a 'stripping' approach is essential. Contamination during workup is a potential problem, and we discuss strategies to circumvent this issue. To illustrate the versatility of the method we report the use of the eToL methodology to unambiguously identify exogenous microbial and viral sequences in human tissue RNA-seq data across the entire tree of life including Archaea, Bacteria, Chloroplastida, basal Eukaryota, Fungi, and Holozoa/Metazoa, and discuss the technical and bioinformatic challenges involved. This generic methodology is likely to find wide application in microbiome analysis including diagnostics. The online version contains supplementary material available at 10.1186/s12866-022-02671-2.
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