Analysis of sequencing strategies and tools for taxonomic annotation: Defining standards for progressive metagenomics
Analysis of sequencing strategies and tools for taxonomic annotation: Defining standards for progressive metagenomics
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DOI：
10.1038/s41598-018-30515-5
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发表时间：
2018-08-13
影响因子：
4.6
通讯作者：
Sanchez-Flores，Alejandro
中科院分区：
文献类型：
--
作者：
Escobar-Zepeda，Alejandra;Ernestina Godoy-Lozano，Elizabeth;Sanchez-Flores，Alejandro
宏基因组学研究最近蓬勃发展，由于DNA测序技术的改进，推动了新的分析工具的出现和分类数据库的增长。然而，没有万能的策略可以保证给定项目的最佳结果，并且可以测试软件，参数和数据库的多种组合。因此，我们进行了一个公正的比较，使用8个生物信息学工具和4个分类数据库的统计分类措施，定义了一个基准框架，以评估每个工具在标准化的背景下。使用16 S rRNA扩增子的计算机模拟数据和全宏基因组鸟枪数据，我们比较了来自不同软件和数据库组合的结果，以检测与算法或数据库注释相关的偏倚。使用我们的基准框架，研究人员可以定义截止值来评估其结果的预期错误率和覆盖率，而无论每个软件使用的分数如何。选择最佳工具的快速指南，所有数据集和脚本都可以复制我们的结果并对任何新方法进行基准测试，请访问https：//github。com/Ales-ibt/Metagenomic-benchmark.最后，我们强调了金标准，数据库管理和人工检查分类分析结果的重要性，以更好，更准确地描述微生物多样性。
Metagenomics research has recently thrived due to DNA sequencing technologies improvement, driving the emergence of new analysis tools and the growth of taxonomic databases. However, there is no all-purpose strategy that can guarantee the best result for a given project and there are several combinations of software, parameters and databases that can be tested. Therefore, we performed an impartial comparison, using statistical measures of classification for eight bioinformatic tools and four taxonomic databases, defining a benchmark framework to evaluate each tool in a standardized context. Using in silico simulated data for 16S rRNA amplicons and whole metagenome shotgun data, we compared the results from different software and database combinations to detect biases related to algorithms or database annotation. Using our benchmark framework, researchers can define cut-off values to evaluate the expected error rate and coverage for their results, regardless the score used by each software. A quick guide to select the best tool, all datasets and scripts to reproduce our results and benchmark any new method are available at https://github. com/Ales-ibt/Metagenomic-benchmark. Finally, we stress out the importance of gold standards, database curation and manual inspection of taxonomic profiling results, for a better and more accurate microbial diversity description.