Inglis Laura K, Edwards Robert A
Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia.
Microorganisms. 2022 Aug 19;10(8):1671. doi: 10.3390/microorganisms10081671.
The microbiome is an essential part of most ecosystems. It was originally studied mostly through culturing but relatively few microbes can be cultured, so much of the microbiome was left unexplored. The emergence of metagenomic sequencing techniques changed that and allowed the study of microbiomes from all sorts of habitats. Metagenomic sequencing also allowed for a more thorough exploration of prophages, viruses that integrate into bacterial genomes, and how they benefit their hosts. One issue with using open-access metagenomic data is that sequences added to databases often have little to no metadata to work with, so finding enough sequences can be difficult. Many metagenomes have been manually curated but this is a time-consuming process and relies heavily on the uploader to be accurate and thorough when filling in metadata fields and the curators to be working with the same ontologies. Using algorithms to automatically sort metagenomes based on either the taxonomic profile or the functional profile may be a viable solution to the issues with manually curated metagenomes, but it requires that the algorithm is trained on carefully curated datasets and using the most informative profile possible in order to minimize errors.
微生物群落是大多数生态系统的重要组成部分。它最初主要通过培养进行研究，但能够培养的微生物相对较少，因此大部分微生物群落仍未得到探索。宏基因组测序技术的出现改变了这一状况，使得对各种栖息地的微生物群落进行研究成为可能。宏基因组测序还使得对原噬菌体（整合到细菌基因组中的病毒）及其如何使宿主受益进行更深入的探索成为可能。使用开放获取的宏基因组数据存在的一个问题是，添加到数据库中的序列通常几乎没有或根本没有元数据可供使用，因此很难找到足够的序列。许多宏基因组已进行人工整理，但这是一个耗时的过程，并且在很大程度上依赖于上传者在填写元数据字段时准确、全面，以及整理者使用相同的本体。使用算法根据分类学概况或功能概况自动对宏基因组进行分类，可能是解决人工整理宏基因组问题的一个可行方案，但这需要在经过精心整理的数据集上对算法进行训练，并使用信息量最大的概况，以尽量减少错误。
