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

Journal article
Authors Sten Anslan
R. Henrik Nilsson
Christian Wurzbacher
Petr Baldrian
Leho Tedersoo
Mohammad Bahram
Published in MycoKeys
Volume 39
Pages 29-40
ISSN 1314-4057
Publication year 2018
Published at Department of Biological and Environmental Sciences
Pages 29-40
Language en
Keywords Amplicon sequencing, Fungal biodiversity, Metagenomics, Microbial communities, Microbiome, Mycobiome
Subject categories Bioinformatics (Computational Biology), Microbiology, Botany, Bioinformatics and Systems Biology, Biological Systematics, Other Natural Sciences, Functional genomics


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.

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