Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

By

Anslan, Sten
Nilsson, R. Henrik
Wurzbacher, Christian
Baldrian, Petr
Tedersoo, Leho
Bahram, Mohammad

Type

Article

Date of Publication

2018

Original Publication

NeoBiota : advancing research on alien species and biological invasions

Volume

39

Pages

29-40

Subjects

amplicon sequencing , fungal biodiversity , Metagenomics , microbial communities , microbiome , mycobiome

Contributed by

Pensoft Publishers

Language

English

Published by

Sophia, Bulgaria Pensoft Publishers

Identifiers

DOI: https://doi.org/10.3897/mycokeys.39.28109

 

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.