Abstract

Background: Amplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (less OTU scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences.Results: In LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios.We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines, yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxon composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified taxa and a higher fraction of reads assigned to true taxa (48% and 57% at species; 83% and 98% at genus level, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reported 16S sequences.Conclusion: LotuS2 is a lightweight and user-friendly pipeline that is fast, precise, and streamlined, using extensive pre- and post-ASV/OTU clustering steps to further increase data quality. High data usage rates and reliability enable high-throughput microbiome analysis in minutes.

Keywords

Microbiome; Short read; Long read; Amplicon sequencing; Amplicon data analysis; 16S rRNA; ITS

Published in

Microbiome
2022, Volume: 10, article number: 176
Publisher: BMC

SLU Authors

• Bahram, Mohammad

  • Department of Ecology, Swedish University of Agricultural Sciences
    
  • University of Tartu

Sustainable Development Goals

Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation


UKÄ Subject classification

Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology


Publication identifier

DOI: https://doi.org/10.1186/s40168-022-01365-1

Permanent link to this page (URI)

https://res.slu.se/id/publ/119641