Introduction

The objective of these analyses is to compare the tools currently available for analyzing extensive ITS data. For this purpose, we will analyze real ITS1 and ITS2 data. FROGS (Escudié et al. 2018), USEARCH (Edgar, 2016), DADA2 (Callahan et al. 2016) and Qiime2 (Bolyen et al. 2019) have been used and compared using their respective guidelines.

Description of data

Sequencing data

  • 12 samples
  • paired-end Miseq sequencing on a single run
  • MOCK composed of representative species of the meat microbiota
    • Composed of 40 fungal species
    • 6 repetitions (3 DNA pool + 3 PCR1 pool)
  • 2 barcodes : ITS1 (ITS1F-ITS2) and ITS2 (ITS3-ITS4Kyo)
  • Same PCR1 and PCR2 conditions for all samples and barcodes

Primers

Amplicon Fwd.name Fwd.sequence Rv.nam Rv.sequence
ITS1 ITS1F CTTGGTCATTTAGAGGAAGTAA ITS2 GCATCGATGAAGAACGCAGC
ITS2 ITS3 GCATCGATGAAGAACGCAGC ITS4Kyo GCAWAWCAAWAAGCGGAGGA

Expected references

## Min:  132  ; Max:  477

Sequencing depth

Summary table:

Results

Expected vs. observed depth (count of sequences)

MEAT ITS1 ADN

MEAT ITS1 PCR

MEAT ITS2 ADN

MEAT ITS2 PCR

Expected vs. observed Richness (count of OTU, ASV, ZOTU)

MEAT ITS1 ADN

MEAT ITS1 PCR

MEAT ITS2 ADN

MEAT ITS2 PCR

Metrics calculated in relation to the expected

The results obtained for each tool were compared to what was expected and different metrics were calculated. Affiliations and associated abundances are taken into account.

The metrics are:

  • Divergence (takes into account abundance)
  • False negatives
  • False positives
  • True positives

MEAT ITS1 ADN

MEAT ITS1 PCR

MEAT ITS2 ADN

MEAT ITS2 PCR

Taxonomies found vs. lost

Tools are clustered using the canberra distance.

MEAT ITS1 ADN

MEAT ITS1 PCR

MEAT ITS2 ADN

MEAT ITS2 PCR

Reconstruction of the reference

MEAT ITS1 ADN

Count of OTU/ASV/ZOTU detected
EXPECTED FROGS USEARCH DADA2-se DADA2-pe QIIME-se QIIME-pe
40 34 25 32 31 28 28
Count of OTU/ASV/ZOTU perfectly reconstructed
EXPECTED FROGS USEARCH DADA2-se DADA2-pe QIIME-se QIIME-pe
40 31 25 30 27 19 28

MEAT ITS1 PCR

Count of OTU/ASV/ZOTU detected
EXPECTED FROGS USEARCH DADA2-se DADA2-pe QIIME-se QIIME-pe
40 38 29 36 34 31 31
Count of OTU/ASV/ZOTU perfectly reconstructed
EXPECTED FROGS USEARCH DADA2-se DADA2-pe QIIME-se QIIME-pe
40 33 27 31 29 20 29

MEAT ITS2 ADN

Count of OTU/ASV/ZOTU detected
EXPECTED FROGS USEARCH DADA2-se DADA2-pe QIIME-se QIIME-pe
40 35 25 27 29 25 25
Count of OTU/ASV/ZOTU perfectly reconstructed
EXPECTED FROGS USEARCH DADA2-se DADA2-pe QIIME-se QIIME-pe
40 32 24 27 29 21 24

MEAT ITS2 PCR

Count of OTU/ASV/ZOTU detected
EXPECTED FROGS USEARCH DADA2-se DADA2-pe QIIME-se QIIME-pe
40 37 25 30 30 25 25
Count of OTU/ASV/ZOTU perfectly reconstructed
EXPECTED FROGS USEARCH DADA2-se DADA2-pe QIIME-se QIIME-pe
40 32 25 29 30 21 24




References

  • Escudié, Frédéric, Lucas Auer, Maria Bernard, Mahendra Mariadassou, Laurent Cauquil, Katia Vidal, Sarah Maman, Guillermina Hernandez-Raquet, Sylvie Combes, and Géraldine Pascal. 2018. “FROGS: Find, Rapidly, Otus with Galaxy Solution.” Bioinformatics 34 (8): 1287–94. https://doi.org/10.1093/bioinformatics/btx791
  • Bolyen, Evan, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian C Abnet, Gabriel A Al-Ghalith, Harriet Alexander, et al. 2019. “Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using Qiime 2.” Nature Biotechnology 37 (8): 852–57. https://doi.org/10.1038/s41587-019-0209-9
  • Callahan, Benjamin J, Paul J McMurdie, Michael J Rosen, Andrew W Han, Amy Jo A Johnson, and Susan P Holmes. 2016. “DADA2: High-Resolution Sample Inference from Illumina Amplicon Data.” Nature Methods 13 (7): 581–83. https://dx.doi.org/10.1038%2Fnmeth.3869
  • R.C. Edgar. 2016. “UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing”. BioRxiv. https://doi.org/10.1101/081257