Abstract
Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.
Highlights
Fungi are major ecological and functional players in terrestrial ecosystems
Our analysis revealed that the computation time, quality of error filtering and output of specific bioinformatics process largely depends on the platform used
Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset
Summary
Fungi are major ecological and functional players in terrestrial ecosystems. The full diversity of fungi remains largely uncharted due to their largely unculturable nature, the lack of tangible morphological manifestations and shortcomings of the mycological community to sample beyond traditional habitats and substrates (Grossart et al 2016; Hibbett et al 2017; Lücking et al 2018). Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. 31 used multiple platforms that support all steps in the analysis of HTS-based metabarcoding datasets: QIIME2 (v2018.2; Caporaso et al 2010), LotuS (v1.59; Hildebrand et al 2014), Galaxy (v.2.1.1; Afgan et al 2016), PipeCraft (v1.0; Anslan et al 2017) and PIPITS (v2.0; Gweon et al 2015) (Table 1; Figure 1).
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