# Its all fun guys: a comparison of bioinformatic pipelines for metabarcoding plant and soil fungal communities

Charlie Pauvert, Marc Buée, Valerie Laval, Véronique Edel-Hermann, Laure Fauchery, Angelique Gautier, Isabelle Lesur, Jessica Vallance, Corinne Vacher

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## ► To cite this version:

Charlie Pauvert, Marc Buée, Valerie Laval, Véronique Edel-Hermann, Laure Fauchery, et al.. Its all fun guys: a comparison of bioinformatic pipelines for metabarcoding plant and soil fungal communities. International Phytobiomes Conference 2018, Dec 2018, Montpellier, France. 2018. [hal-01938707](https://hal.science/hal-01938707)

HAL Id: hal-01938707

https://hal.science/hal-01938707

Submitted on 5 Jun 2020

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INRA
SCIENCE & IMPACT

# ITS ALL FUN GUYS
## A COMPARISON OF BIOINFORMATIC PIPELINES FOR METABARCODING PLANT AND SOIL FUNGAL COMMUNITIES

**CHARLIE PAUVERT¹, MARC BUÉE², VALÉRIE LAVAL³, VÉRONIQUE EDEL-HERMANN⁴, LAURE FAUCHERY², ANGÉLIQUE GAUTIER³, ISABELLE LESUR¹,⁵, JESSICA VALLANCE⁶, CORINNE VACHER¹**

## Introduction

Plant and soil fungal communities influence plant fitness and ecosystem functioning.

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These fungi are classically studied by **metabarcoding** approaches targeting the ribosomal internal transcribed spacer (ITS)

Metabarcoding

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Composition

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Bioinformatics

But there is **no clear consensus** concerning the most appropriate bioinformatic pipeline.

## Objectives

1. Make and sequence a **mock community** of 189 Dikarya strains commonly found in agricultural and forest soils and in plant tissues.

2. Compare the ability of 288 **combinations of bioinformatic softwares and parameters** to recover the fungal strains present in this mock community, in the expected proportions.

## Methods

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**Fig 1 Taxonomic composition of the artificial fungal community.** The nodes of the tree are the ITS1 sequences (n = 175) of the fungal strains (n = 189) that constitute the artificial or « mock » community.

DNA were **pooled** amplified and sequenced with **Illumina MiSeq**

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DNA were **independently** amplified and sequenced with **Sanger**

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<figure><img src="image_11.png" /><figcaption>**Fig 2 Overview of the bioinformatic pipelines compared in this study** →</figcaption></figure>

The MiSeq sequences were analyzed with 288 unique combinations of bioinformatic softwares and parameters (referred to hereafter as pipelines)

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### Pipeline comparison

**Sensitivity**

Percentage of recovered mock strains

**Precision**

Percentage of OTU/ASV matching mock strains

**Compositional similarity**

Bray-Curtis similarity between the community recovered and the mock community

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## Results

<table><thead><tr><th></th><th>Highest Sensitivity</th><th>Highest Precision</th><th>Highest Similarity</th><th>Best Trade-off</th></tr></thead><tbody><tr><td>1. Assembly</td><td>QUALITY_R1</td><td>FASTQJOIN_150</td><td>QUALITY_R1</td><td>QUALITY_R1</td></tr><tr><td>2. Extraction</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>3. Variation</td><td>VSEARCH</td><td>DADA2</td><td>DADA2</td><td>DADA2</td></tr><tr><td>4. Chimeras</td><td>Retained</td><td>Removed</td><td>Retained</td><td>Removed</td></tr><tr><td>5. Filtering</td><td>All</td><td>10</td><td>All</td><td>All</td></tr><tr><td></td><td>↓ Se1</td><td>↓ P1</td><td>↓ Si1</td><td>↓ Si5</td></tr></tbody></table>

**Table 1 Pipelines with the best performances for each three criteria and the pipeline with the best trade-off.** Pipelines are labeled below the table and the steps are numbered as in Fig 2.

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**Fig 3 Total number of OTUs (or ASVs) retrieved by the top four pipelines (Table 1).** The black horizontal line indicates the expected richness.

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**Fig 4 Values of precision and compositional similarity to the mock fungal community, for all 288 bioinformatic pipelines.** Only the top four pipelines are labeled

None of the pipelines scored highest in all three criteria (Table 1).

Observed fungal richness depended strongly on the bioinformatic pipeline (Fig 3).

Community richness and composition were well recovered by DADA2 (Fig 3 & 4).

## Conclusion

**The Si5 pipeline** used single forward (R1) sequences with DADA2 and no filter other than the removal of low-quality and chimeric sequences.

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One of the best trade-offs

No overestimation of the number of fungal strains. One of the best to recover the composition of the mock community. Good performance on precision and sensitivity as well.

**Acknowledgments** Attendance at this congress was possible thanks to the award for the best presentation of the Pathobiome 2018 congress. We thank Matthieu Barret, Martial Briand, Lucas Auer, Gregory Gambetta, Guilherme Martins, Frédéric Barraquand and Tania Fort for useful comments and discussions. We also thank the INRA MEM metaprogramme (Meta-Omics of Microbial Ecosystems) for financial and scientific support. The mock community sequencing was funded by the INRA MEM MetaBAR project (PI: MB) and the bioinformatic analyses were performed as part of the INRA MEM Learn-biocontrol project (PI: CV). Additional funding was received from the LABEX COTE (ANR-10-LABX-45) and the LABEX CEBA (ANR-10-LABX-25-01). CP's PhD grant was funded by the INRA and Bordeaux Sciences Agro (BSA). We thank the Genotoul sequencing facility (Get-PlaGe) for sequencing the mock community and the Genotoul bioinformatics facility (Bioinfo Genotoul) for providing computing and storage resources.

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INRA
SCIENCE & IMPACT

1. BIOGECO, INRA, Univ. Bordeaux, 33615 Pessac, France

2. INRA, Université de Lorraine, UMR IAM 1136, Laboratoire d'Excellence ARBRE, Centre INRA-Lorraine, 54280 Champenoux, France

3. BIOGER, INRA, 78850 Thiverval Grignon, France

4. Agroécologie, AgroSup Dijon, CNRS, INRA, Univ. Bourgogne Franche-Comté, 21000 Dijon, France

5. HelixVenture, 33700 Mérignac, France

6. SAVE, Bordeaux Sciences Agro, INRA, ISVV, Univ. Bordeaux, 33882 Villenave d'Ornon, France

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