dada2_FROGS workflow

Introduction

This repository git@forgemia.inra.fr:migale/dada2_frogs.git contains the Rmd file used to perform a combination of dada2 [1] and FROGS [2] [3] and generate a report. It is able to deal with 16S and ITS data.

Some adjustments are to be made to use it on another infrastructure than the migale platform.

Use cases

16S Workflow

flowchart LR
    style FROGS stroke:#5f999d
    style dada2 stroke:#5f999d
    database[(Databank)]-->affiliation
    fastq[[Fastq]]
    biom[[BIOM]]
    tsv[[TSV]]
    
    fastq-->seqkit
    seqkit---plotQualityProfiles
    subgraph dada2
        direction TB
        plotQualityProfiles-->filterAndTrim1[filterAndTrim]-->filterAndTrim2[filterAndTrim]-->learnErrors-->dada-->mergePairs-->makeSequenceTable
    end
    filterAndTrim1-->cutadapt-->filterAndTrim2
    makeSequenceTable-->rc
    subgraph FROGS
        direction TB
        rc[remove chimera]-->filters-->affiliation
    end
    
    
    affiliation-->biom
    affiliation--> tsv

ITS worflow

flowchart LR
    style FROGS stroke:#5f999d
    style dada2 stroke:#5f999d
    style mergePairs stroke:#5F999d,stroke-width:4px
    style ITSx stroke:#5F999d,stroke-width:4px
    database[(Databank)]-->affiliation
    fastq[[Fastq]]
    biom[[BIOM]]
    tsv[[TSV]]
    
    fastq-->seqkit
    seqkit---plotQualityProfiles
    subgraph dada2
        direction TB
        plotQualityProfiles-->filterAndTrim1[filterAndTrim]-->filterAndTrim2[filterAndTrim]-->learnErrors-->dada-->mergePairs-->makeSequenceTable
    end
    filterAndTrim1-->cutadapt-->filterAndTrim2
    makeSequenceTable-->rc
    subgraph FROGS
        direction TB
        rc[remove chimera]-->filters-->ITSx-->affiliation
    end
    
    
    affiliation-->biom
    affiliation--> tsv

Getting started

Clone the repository

git clone git@forgemia.inra.fr:migale/dada2_frogs.git
cd dada2_frogs

Test it

Warning

If you are not using the migale infrastructure: make sure to add FROGS (v4.0.1) and seqkit installed on your system and update lines in dada2_FROGS.Rmd (line 100):

Sys.setenv(PATH = paste("/usr/local/genome/Anaconda3/envs/seqkit-2.0.0/bin", "/usr/local/genome/Anaconda3/envs/frogs-4.0.1/bin", Sys.getenv("PATH"), sep = ":"))

16S data

Download the 16S reference databank from Silva

wget http://genoweb.toulouse.inra.fr/frogs_databanks/assignation/SILVA/16S/silva_138.1_16S_pintail100.tar.gz
tar zxvf silva_138.1_16S_pintail100.tar.gz

Extract FASTQ files from the archive provided in the data folder

cd data/16S
tar zxvf 16S16samples.tar.gz
cd -

Run the script

mkdir out_16S/
R -e "rmarkdown::render('dada2_FROGS.Rmd', params=list(
  author='Olivier Rué', 
  reference='silva_138.1_16S_pintail100/silva_138.1_16S_pintail100.fasta', 
  region='16S', 
  expname='16S TOY DATASET',
  input_directory='data/16S/',
  forward_primer='AGAGTTTGATCCTGGCTCAG',
  reverse_primer='ATTACCGCGGCTGCTGG',
  output_directory='out_16S/',
  min_abundance=0.00005,
  its=FALSE,
  threads=1), 
  output_file = 'out_16S/report.html')"

ITS data

Download the ITS reference databank from UNITE

wget http://genoweb.toulouse.inra.fr/frogs_databanks/assignation/Unite/Unite_Fungi_8.3_20210510.tar.gz
tar zxvf Unite_Fungi_8.3_20210510.tar.gz

Extract FASTQ files from the archive provided in the data folder

cd data/ITS
tar zxvf ITS4samples.tar.gz
cd -

Run the script

mkdir out_ITS/
R -e "rmarkdown::render('dada2_FROGS.Rmd', params=list(
  author='Olivier Rué', 
  reference='Unite_Fungi_8.3_20210510/Unite_Fungi_8.3_20210510.fasta', 
  region='ITS', 
  expname='ITS TOY DATASET',
  input_directory='data/ITS/',
  forward_primer='CTTGGTCATTTAGAGGAAGTAA',
  reverse_primer='GCTGCGTTCTTCATCGATGC',
  output_directory='out_ITS/',
  min_abundance=0.00005,
  its=TRUE,
  its_region='ITS1',
  threads=1), 
  output_file = 'out_ITS/report.html')"

Parameters

Id	Meaning
author	author
reference	path to the reference FASTA file used for taxonomic assignation (indexed for Blast and/or RDP)
phix	path to the phiX databank (used for FROGS filters)
region	the targeted region of the amplicons (only used in the report subtitle)
expname	the experience name (report title)
input_directory	the input directory containing raw FASTQ files (paired-end reads)
forward_primer	the sequence of the 5’ primer
reverse_primer	the sequence of the 3’ primer
output_directory	the output directory
min_reads	Expected number of reads (only used in graphics)
min_abundance	the min abundance used for FROGS filters (can be relative or absolute, see FROGS documentation)
its	Is your data ITS data?
its_region	For ITS data, ITS1 or ITS2 (usefull for ITSx)
output_file	name of the HTML report
threads	number of CPUs available

Outputs

References

1. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from illumina amplicon data. Nature methods. 2016;13:581.

2. Bernard M, Rué O, Mariadassou M, Pascal G. FROGS: a powerful tool to analyse the diversity of fungi with special management of internal transcribed spacers. Briefings in Bioinformatics. 2021;22. doi:10.1093/bib/bbab318.

3. Escudié F, Auer L, Bernard M, Mariadassou M, Cauquil L, Vidal K, et al. FROGS: Find, Rapidly, OTUs with Galaxy Solution. Bioinformatics. 2018;34:1287–94. doi:10.1093/bioinformatics/btx791.

Reuse

https://creativecommons.org/licenses/by-sa/4.0/

A work by Migale Bioinformatics Facility
Université Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, France
Université Paris-Saclay, INRAE, BioinfOmics, MIGALE bioinformatics facility, 78350, Jouy-en-Josas, France