Bruno Fosso, Marinella Marzano, Santamaria Monica
Department of Biosciences, Biotechnology and Biopharmaceutics, University of Bari, Bari, 70126, Italy.
Methods Mol Biol. 2015;1269:257-78. doi: 10.1007/978-1-4939-2291-8_16.
In recent years, thanks to the essential support provided by the Next-Generation Sequencing (NGS) technologies, Metagenomics is enabling the direct access to the taxonomic and functional composition of mixed microbial communities living in any environmental niche, without the prerequisite to isolate or culture the single organisms. This approach has already been successfully applied for the analysis of many habitats, such as water or soil natural environments, also characterized by extreme physical and chemical conditions, food supply chains, and animal organisms, including humans. A shotgun sequencing approach can lead to investigate both organisms and genes diversity. Anyway, if the purpose is limited to explore the taxonomic complexity, an amplicon-based approach, based on PCR-targeted sequencing of selected genetic species markers, commonly named "meta-barcodes", is desirable. Among the genomic regions most widely used for the discrimination of bacterial organisms, in some cases up to the species level, some hypervariable domains of the gene coding for the 16S rRNA occupy a prominent place. The amplification of a certain meta-barcode from a microbial community through the use of PCR primers able to work in the entire considered taxonomic group is the first task after the extraction of the total DNA. Generally, this step is followed by the high-throughput sequencing of the resulting amplicons libraries by means of a selected NGS platform. Finally, the interpretation of the huge amount of produced data requires appropriate bioinformatics tools and know-how in addition to efficient computational resources. Here a computational methodology suitable for the taxonomic characterization of 454 meta-barcode sequences is described in detail. In particular, a dataset covering the V1-V3 region belonging to the bacterial 16S rRNA coding gene and produced in the Human Microbiome Project (HMP) from a palatine tonsils sample is analyzed. The proposed exercise includes the basic steps to manage raw sequencing data, remove amplification and pyrosequencing errors, and finally map sequences on the taxonomy.
近年来，得益于下一代测序（NGS）技术提供的重要支持，宏基因组学使得人们能够直接获取生活在任何环境生态位中的混合微生物群落的分类学和功能组成，而无需事先分离或培养单个生物体。这种方法已成功应用于许多栖息地的分析，如水或土壤等自然环境（其特点还包括极端的物理和化学条件）、食品供应链以及包括人类在内的动物有机体。鸟枪法测序方法可用于研究生物体和基因的多样性。无论如何，如果目的仅限于探索分类学复杂性，基于对选定的遗传物种标记（通常称为“元条形码”）进行PCR靶向测序的基于扩增子的方法是可取的。在最广泛用于区分细菌生物体（在某些情况下可达物种水平）的基因组区域中，编码16S rRNA的基因的一些高变区占据突出地位。通过使用能够在整个考虑的分类群中起作用的PCR引物从微生物群落中扩增特定的元条形码，是提取总DNA后的首要任务。通常，这一步骤之后是通过选定的NGS平台对所得扩增子文库进行高通量测序。最后，除了高效的计算资源外，对大量生成数据的解释还需要适当的生物信息学工具和专业知识。这里详细描述了一种适用于454元条形码序列分类学表征的计算方法。特别是，分析了一个覆盖细菌16S rRNA编码基因V1-V3区域的数据集，该数据集是在人类微生物组计划（HMP）中从腭扁桃体样本产生的。所提出的操作包括管理原始测序数据、去除扩增和焦磷酸测序错误以及最终将序列映射到分类学上的基本步骤。
