基于Nextflow构建的宏条形码自动化分析流程EPPS

基于宏条形码技术的物种快速检测有助于生物多样性的评估、预测和保护。本文介绍了常用宏条形码分析的步骤和参数设定方法。我们利用Nextflow搭建了一款宏条形码分析流程EPPS, 可以自动化地运行从原始数据的质量控制到环境多样性的比较。Nextflow软件还拥有流程监控的功能, 可视化输出每个进程所消耗的时间与内存。本文还使用测试数据和已发表数据证明该平台能够有效地分析宏条形码数据并可靠地分析环境生物多样性的相似性。     

Bioinformatics matters: The accuracy of plant and soil fungal community data is highly dependent on the metabarcoding pipeline

Fungal communities associated with plants and soil influence plant fitness and ecosystem functioning. They are frequently studied by metabarcoding approaches targeting the ribosomal internal transcribed spacer (ITS), but there is no consensus concerning the most appropriate bioinformatic approach for the analysis of these data. We sequenced an artificial fungal community composed of 189 strains covering a wide range of Ascomycota and Basidiomycota, to compare the performance of 360 software and parameter combinations. The most sensitive approaches, based on the USEARCH and VSEARCH clustering algorithms, detected almost all fungal strains but greatly overestimated the total number of strains. By contrast, approaches using DADA2 to detect amplicon sequence variants were the most effective for recovering the richness and composition of the fungal community. Our results suggest that analyzing single forward (R1) sequences with DADA2 and no filter other than the removal of low-quality and chimeric sequences is a good option for fungal community characterization.