TY - JOUR UR - https://doi.org/10.7287/peerj.preprints.27019v2 DO - 10.7287/peerj.preprints.27019v2 TI - Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding AU - Anslan,Sten AU - Nilsson,Henrik AU - Wurzbacher,Christian AU - Baldrian,Petr AU - Tedersoo,Leho AU - Bahram,Mohammad DA - 2018/08/29 PY - 2018 KW - Fungal communities KW - mycobiome KW - metabarcoding KW - metagenomics KW - amplicon sequencing. AB - 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 appear 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 data set. We conclude that the output of each platform require manual validation of the OTUs by examining the taxonomy assignment values. VL - 6 SP - e27019v2 T2 - PeerJ Preprints JO - PeerJ Preprints J2 - PeerJ Preprints SN - 2167-9843 ER -