# Easy16S: a user-friendly Shiny web-service for exploration and visualization of microbiome data.

Cédric Midoux

<figure><img src="image_1.png" /></figure>

## To cite this version:

Cédric Midoux. Easy16S: a user-friendly Shiny web-service for exploration and visualization of microbiome data.. Journées NEM, INRAE, Apr 2024, Paris, France. hal-04549290

HAL Id: hal-04549290

https://hal.inrae.fr/hal-04549290

Submitted on 17 Apr 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>
# Easy16S: a user-friendly Shiny web-service for exploration and visualization of microbiome data.

Cédric Midoux

► To cite this version:

Cédric Midoux. Easy16S: a user-friendly Shiny web-service for exploration and visualization of mi-crobiome data.. Journées NEM, INRAE, Apr 2024, Paris, France. hal-04549290

HAL Id: hal-04549290

https://hal.inrae.fr/hal-04549290v1

Submitted on 17 Apr 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
<figure><img src="image_3.png" /></figure>

# Easy16S : a user-friendly Shiny web-service for exploration and visualization of microbiome data.

## Journées Nutrition et Ecosystèmes Microbiens 2024

Cédric Midoux ID

PROSE & MAIAGE

April 16, 2024

<figure><img src="image_4.png" /></figure>

This work is licensed under a Creative Commons Attribution-ShareAlike 2.0 Generic License.
<figure><img src="image_2.png" /></figure>

# Easy16S : a user-friendly Shiny web-service for exploration and visualization of microbiome data.

## Journées Nutrition et Ecosystèmes Microbiens 2024

Cédric Midoux ID

PROSE & MAIAGE

April 16, 2024

<figure><img src="image_3.png" /></figure>

This work is licensed under a Creative Commons Attribution-ShareAlike 2.0 Generic License.
# Metabarcoding workflow

1

Collect an
environmental
sample

<figure><img src="image_5.png" /></figure>

2

DNA extraction
from environmental
sample

<figure><img src="image_6.png" /></figure>

3

Amplify DNA
markers

<figure><img src="image_7.png" /></figure>

4

High-throughput
sequencing

<figure><img src="image_8.png" /></figure>

5

Bioinformatic
processing

Species
identification

<figure><img src="image_9.png" /></figure>

6

Ecological
analysis

<figure><img src="image_10.png" /></figure>

7

Alvaro (2017)
# Metabarcoding workflow

1

Collect an
environmental
sample

<figure><img src="image_4.png" /></figure>

2

DNA extraction
from environmental
sample

<figure><img src="image_5.png" /></figure>

3

Amplify DNA
markers

<figure><img src="image_6.png" /></figure>

4

High-throughput
sequencing

<figure><img src="image_7.png" /></figure>

5

Bioinformatic
processing

6

Species
identification

<figure><img src="image_8.png" /></figure>

7

Ecological
analysis

<figure><img src="image_9.png" /></figure>

Alvaro (2017)
# Exploration and visualisation?

<figure><img src="image_11.png" /></figure>
<figure><img src="image_10.png" /></figure>

# Exploration and visualisation?

<figure><img src="image_11.png" /></figure>
# Perform Complex Analyses?

* Depends on huge data, specific formats

* Requires computational resources

* Need to install tools locally

* Training and acquiring expertise in analyses

* Need time to carry out analyses

* Integrate data and metadata

* Tools lack interactivity
# Perform Complex Analyses?

* Depends on huge data, specific formats

* Requires computational resources

* Need to install tools locally

* Training and acquiring expertise in analyses

* Need time to carry out analyses

* Integrate data and metadata

* Tools lack interactivity
<figure><img src="image_12.png" /></figure>

# Statement of need

Easy16S is designed to facilitate the exploration, visualization, and analysis of microbiome data !

* User-friendly interactive web application

* Convenient functions and default settings

* To explore, visualize, and analyze metabarcoding data

* Hosted on an online instance

* Built with R and based on the phyloseq package

* Integrated with the FROGS sequence processing suite
<figure><img src="image_12.png" /></figure>

# Statement of need

Easy16S is designed to facilitate the exploration, visualization, and analysis of microbiome data !

* User-friendly interactive web application

* Convenient functions and default settings

* To explore, visualize, and analyze metabarcoding data

* Hosted on an online instance

* Built with R and based on the phyloseq package

* Integrated with the FROGS sequence processing suite
Live Demo
Live Demo
Upload data

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>
Upload data

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>
# Transform data

<figure><img src="image_16.png" /></figure>
# Transform data

<figure><img src="image_16.png" /></figure>
# Summary

<figure><img src="image_17.png" /></figure>
# Summary

<figure><img src="image_17.png" /></figure>
# Tables

<figure><img src="image_18.png" /></figure>
# Tables

<figure><img src="image_18.png" /></figure>
# Metadata

<figure><img src="image_19.png" /></figure>
# Metadata

<figure><img src="image_19.png" /></figure>
<figure><img src="image_20.png" /></figure>

# Barplot

<figure><img src="image_21.png" /></figure>

NEM research network 2024
<figure><img src="image_20.png" /></figure>

# Barplot

<figure><img src="image_21.png" /></figure>

NEM research network 2024
# Beta diversity

<figure><img src="image_22.png" /></figure>
<figure><img src="image_22.png" /></figure>

# Beta diversity

<figure><img src="image_23.png" /></figure>
<figure><img src="image_23.png" /></figure>

# Differential abundance

<figure><img src="image_24.png" /></figure>
<figure><img src="image_24.png" /></figure>

# Differential abundance

<figure><img src="image_25.png" /></figure>
<figure><img src="image_25.png" /></figure>

# Differential abundance

<figure><img src="image_26.png" /></figure>
# Differential abundance

<figure><img src="image_26.png" /></figure>
# Take Home Messages
# Take Home Messages
Online instance

<figure><img src="image_27.png" /></figure>
<figure><img src="image_27.png" /></figure>

Online instance

<figure><img src="image_28.png" /></figure>
<figure><img src="image_28.png" /></figure>

# Documentation

<figure><img src="image_29.png" /></figure>
<figure><img src="image_29.png" /></figure>

# Documentation

<figure><img src="image_30.png" /></figure>
# GitLab (issues and contributions)

<figure><img src="image_30.png" /></figure>
<figure><img src="image_31.png" /></figure>

# GitLab (issues and contributions)

<figure><img src="image_32.png" /></figure>
Easy16S: a user-friendly Shiny web-service for

exploration and visualization of microbiome data.

Cédric Midoux ⚼ 1,2,3, Olivier Rué ⚼ 2,3, Olivier Chapleur ⚼ 1, Ariane Bize ⚼ 1,

Valentin Loux ⚼ 2,3, and Mahendra Mariadassou ⚼ 2,3

1 Université Paris-Saclay, INRAE, PROSE, 92761, Antony, France 2 Université Paris-Saclay, INRAE,

MalAGE, 78350, Jouy-en-Josas, France 3 Université Paris-Saclay, INRAE, BioInfomics, MIGALE

bioinformatics facility, 78350, Jouy-en-Josas, France

DOI: 10.1000/draft

Software

• Review ⚼

• Repository ⚼

• Archive ⚼

Editor: ⚼

Submitted: 27 March 2024

Published: unpublished

License

Authors of papers retain copyright
and release the work under a
Creative Commons Attribution 4.0
International License (CC BY 4.0)

Summary

The analysis of microbiome data has become a major asset for investigating microbial diversity and dynamics, in diverse fields, like health (Chapelle et al., 2013), environmental studies (Karimi et al., 2020), food-processing (Chabot et al., 2014), or environmental biotechnologies (Poirier et al., 2016). Due to sequencing advances, microbiome studies now require exploration,

analysis and interpretation of large and high-dimensional datasets. Metabarcoding approaches,
in particular, are based on a two-step process. First, a bioinformatics pipeline processes raw
amplicon sequencing reads into Operational Taxonomic Unit (OTU) or Amplicon Sequence

Variant (ASV), generating counts and taxonomic affiliations for each of them. Second, these
tables are enriched with sample metadata to investigate relevant biological questions using
statistical analyses. The affordability of amplicon sequencing has led to its widespread use
in microbial ecology. Therefore, there is a growing demand for user-friendly interactive
tools, enabling researchers to analyze their data autonomously, alleviating the dependence on

bioinformaticians, biostatisticians or the need to acquire skills in R programming.

Regarding the bioinformatics part, many solutions are available to produce count tables from
reads (Halpern et al., 2021), relying either on command-line tools provided through a
Galaxy interface (ie. QIIME (Caporaso et al., 2010), FROGS (Escudé et al., 2017), or on R
pipelines (ie. BAOA2 (Callahan et al., 2016)). For the second step, several packages dedicated
to the analysis and visualization of microbiomes are available, such as phyloseq (Paul J
McMurray & Holmes, 2013), microbione (Lahli & Shetty, 2012-2019), metacoder (Foster et
al., 2011) and many others. Their use can however sometimes be complex. There are relatively
few tools available for rapid, interactive analysis of microbiome data (shiny-phyloseq (P. J
McMurdo & Holmes, 2014) is no longer supported; antimalcutes (Zhao et al., 2021) requires
local installation; shanan (Volant et al., 2020) is integrated into a global workflow and cannot
be decoupled from the bioinformatics components).

Statement of need

Here, we introduce Easy16S, an R-package and an interactive Shiny application (Chang et

al., 2021), aiming to facilitate exploratory microbiome data analysis, data visualization, and

statistical analysis. This tool is specifically designed for biologists eager to swiftly explore their
data and generate figures interactively. It is easy-to-use and especially focused on the mapping
of covariates of interest to microbiome structure.

This application is built upon phyloseq-class objects powered by (Paul J. McMurray & Holmes,
2013). These objects integrate a matrix of OTU/ASV abundances per sample, a data-frame of

<footnote>Midoux et al. (2024) Easy16S: a user-friendly Shiny web-service for exploration and visualization of microbiome data. *Journal of Open Source Software*, 9(0), 6564. https://doi.org/10.1000/jss/draft.</footnote>
Easy16S: a user-friendly Shiny web-service for

exploration and visualization of microbiome data.

Cédric Midoux ⚼ 1,2,3, Olivier Rué ⚼ 2,3, Olivier Chapleur ⚼ 1, Ariane Bize ⚼ 1,

Valentin Loux ⚼ 2,3, and Mahendra Mariadassou ⚼ 2,3

1 Université Paris-Saclay, INRAE, PROSE, 92761, Antony, France 2 Université Paris-Saclay, INRAE,

MalAGE, 78350, Jouy-en-Josas, France 3 Université Paris-Saclay, INRAE, BioInfomics, MIGALE

bioinformatics facility, 78350, Jouy-en-Josas, France

DOI: 10.1000/draft

Software

• Review ⚼

• Repository ⚼

• Archive ⚼

Editor: ⚼

Submitted: 27 March 2024

Published: unpublished

License

Authors of papers retain copyright
and release the work under a
Creative Commons Attribution 4.0
International License (CC BY 4.0)

Summary

The analysis of microbiome data has become a major asset for investigating microbial diversity and dynamics, in diverse fields, like health (Chapelle et al., 2013), environmental studies (Karimi et al., 2020), food-processing (Chabrier et al., 2014), or environmental biotechnologies (Poirier et al., 2016). Due to sequencing advances, microbiome studies now require exploration,

analysis and interpretation of large and high-dimensional datasets. Metabarcoding approaches,
in particular, are based on a two-step process. First, a bioinformatics pipeline processes raw
amplicon sequencing reads into Operational Taxonomic Unit (OTU) or Amplicon Sequence

Variant (ASV), generating counts and taxonomic affiliations for each of them. Second, these
tables are enriched with sample metadata to investigate relevant biological questions using
statistical analyses. The affordability of amplicon sequencing has led to its widespread use
in microbial ecology. Therefore, there is a growing demand for user-friendly interactive

tools, enabling researchers to analyze their data autonomously, alleviating the dependence on
bioinformaticians, biostatisticians or the need to acquire skills in R programming.

Regarding the bioinformatics part, many solutions are available to produce count tables from
reads (Halpern et al., 2021), relying either on command-line tools provided through a
Galaxy interface (ie. QIIME (Caporaso et al., 2010), FROGS (Escudé et al., 2017), or on R
pipelines (ie. BAOA2 (Callahan et al., 2016)). For the second step, several packages dedicated
to the analysis and visualization of microbiomes are available, such as phyloseq (Paul J
McMurray & Holmes, 2013), microbione (Lahli & Shetty, 2012-2019), metacoder (Foster et
al., 2011) and many others. Their use can however sometimes be complex. There are relatively
few tools available for rapid, interactive analysis of microbiome data (shiny-phyloseq (P. J
McMurdo & Holmes, 2014) is no longer supported; antimalcules (Zhao et al., 2021) requires
local installation; shenan (Volant et al., 2020) is integrated into a global workflow and cannot
be decoupled from the bioinformatics components).

n: Regarding the bioinformatics part, many solutions are available to produce count tables from
reads (Halpern et al., 2021), relying either on command-line tools provided through a
Galaxy interface (ie. QIIME (Caporaso et al., 2010), FROGS (Escudé et al., 2017), or on R
pipelines (ie. BAOA2 (Callahan et al., 2016)). For the second step, several packages dedicated
to the analysis and visualization of microbiomes are available, such as phyloseq (Paul J
McMurray & Holmes, 2013), microbione (Lahli & Shetty, 2012-2019), metacoder (Foster et
al., 2011) and many others. Their use can however sometimes be complex. There are relatively
few tools available for rapid, interactive analysis of microbiome data (shiny-phyloseq (P. J
McMurdo & Holmes, 2014) is no longer supported; antimalcules (Zhao et al., 2021) requires
local installation; shenan (Volant et al., 2020) is integrated into a global workflow and cannot
be decoupled from the bioinformatics components).

n: Regarding the bioinformatics part, many solutions are available to produce count tables from
reads (Halpern et al., 2021), relying either on command-line tools provided through a
Galaxy interface (ie. QIIME (Caporaso et al., 2010), FROGS (Escudé et al., 2017), or on R
pipelines (ie. BAOA2 (Callahan et al., 2016)). For the second step, several packages dedicated
to the analysis and visualization of microbiomes are available, such as phyloseq (Paul J
McMurray & Holmes, 2013), microbione (Lahli & Shetty, 2012-2019), metacoder (Foster et
al., 2011) and many others. Their use can however sometimes be complex. There are relatively
few tools available for rapid, interactive analysis of microbiome data (shiny-phyloseq (P. J
McMurdo & Holmes, 2014) is no longer supported; antimalcules (Zhao et al., 2021) requires
local installation; shenan (Volant et al., 2020) is integrated into a global workflow and cannot
be decoupled from the bioinformatics components).

n: Regarding the bioinformatics part, many solutions are available to produce count tables from
reads (Halpern et al., 2021), relying either on command-line tools provided through a
Galaxy interface (ie. QIIME (Caporaso et al., 2010), FROGS (Escudé et al., 2017), or on R
pipelines (ie. BAOA2 (Callahan et al., 2016)). For the second step, several packages dedicated
to the analysis and visualization of microbiomes are available, such as phyloseq (Paul J
McMurray & Holmes, 2013), microbione (Lahli & Shetty, 2012-2019), metacoder (Foster et
al., 2011) and many others. Their use can however sometimes be complex. There are relatively
few tools available for rapid, interactive analysis of microbiome data (shiny-phyloseq (P. J
McMurdo & Holmes, 2014) is no longer supported; antimalcules (Zhao et al., 2021) requires
local installation; shenan (Volant et al., 2020) is integrated into a global workflow and cannot
be decoupled from the bioinformatics components).

n: Regarding the bioinformatics part, many solutions are available to produce count tables from
reads (Halpern et al., 2021), relying either on command-line tools provided through a
Galaxy interface (ie. QIIME (Caporaso et al., 2010), FROGS (Escudé et al., 2017), or on R
pipelines (ie. BAOA2 (Callahan et al., 2016)). For the second step, several packages dedicated
to the analysis and visualization of microbiomes are available, such as phyloseq (Paul J
McMurray & Holmes, 2013), microbione (Lahli & Shetty, 2012-2019), metacoder (Foster et
al., 2011) and many others. Their use can however sometimes be complex. There are relatively
few tools available for rapid, interactive analysis of microbiome data (shiny-phyloseq (P. J
McMurdo & Holmes, 2014) is no longer supported; antimalcules (Zhao et al., 2021) requires
local installation; shenan (Volant et al., 2020) is integrated into a global workflow and cannot
be decoupled from the bioinformatics components).

Statement of need

n: Here, we introduce Easy16S, an R-package and an interactive Shiny application (Chang et

al., 2021), aiming to facilitate exploratory microbiome data analysis, data visualization, and

n: statistical analysis. This tool is specifically designed for biologists eager to swiftly explore their
data and generate figures interactively. It is easy-to-use and especially focused on the mapping
of covariates of interest to microbiome structure.

n: This application is built upon phyloseq-class objects powered by (Paul J. McMurray & Holmes,
2013). These objects integrate a matrix of OTU/ASV abundances per sample, a data-frame of

<footnote>Midoux et al. (2024) Easy16S: a user-friendly Shiny web-service for exploration and visualization of microbiome data. *Journal of Open Source Software*, 9(0), 6564. https://doi.org/10.1000/jos.draft.</footnote>
# Take Home Messages

* Online instance:

* shiny.migale.inrae.fr/app/easy16S

* Documentation:

* easy16s.migale.inrae.fr

* GitLab (issues and contributions):

* forgemia.inra.fr/migale/easy16s

* Publication: **JOSS** Submitted

<figure><img src="image_31.png" /></figure>
# Take Home Messages

* Online instance:

* shiny.migale.inrae.fr/app/easy16S

* Documentation:

* easy16s.migale.inrae.fr

* GitLab (issues and contributions):

* forgemia.inra.fr/migale/easy16s

* Publication: **JOSS** Submitted

<figure><img src="image_33.png" /></figure>
Bibliography

Alvaro, Sebastian. 2017. "Amplicon Sequencing and High-Throughput Genotyping – Metagenomics." http://www.sixthresearcher.com/amplicon-sequencing-and-high-throughput-genotyping-metagenomics/.

Liu, Yong-Xin, Yuan Qin, Tong Chen, Meiping Lu, Xubo Qian, Xiaoxuan Guo, and Yang Bai. 2020. "A Practical Guide to Amplicon and Metagenomic Analysis of Microbiome Data." *Protein & Cell* 12 (5): 315-30. https://doi.org/10.1007/s13238-020-00724-8.
# Bibliography

Alvaro, Sebastian. 2017. "Amplicon Sequencing and High-Throughput Genotyping – Metagenomics." http://www.sixthresearcher.com/amplicon-sequencing-and-high-throughput-genotyping-metagenomics/.

Liu, Yong-Xin, Yuan Qin, Tong Chen, Meiping Lu, Xubo Qian, Xiaoxuan Guo, and Yang Bai. 2020. "A Practical Guide to Amplicon and Metagenomic Analysis of Microbiome Data." *Protein & Cell* 12 (5): 315-30. https://doi.org/10.1007/s13238-020-00724-8.