QIIME2_Illumina

QIIME2_Illumina is a Meta-barcoding pipeline for analysing Illumina data in QIIME2 framework. Tested with Ubuntu 20.04.1 LTS.

Getting started

Prerequisites

Miniconda3. Tested with conda 4.8.5. which conda should return the path to the executable. If you don't have Miniconda3 installed, you could download and install it with:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
chmod 755 Miniconda3-latest-Linux-x86_64.sh
./Miniconda3-latest-Linux-x86_64.sh

A fasta file that you want to use as a reference database and a text file with taxonmy, or a preformatted marker gene reference database.

Installation

git clone https://github.com/MaestSi/QIIME2_Illumina.git
cd QIIME2_Illumina
chmod 755 *
./install.sh

A conda environment named QIIME2_Illumina_env is created, where qiime2-2021.8 is installed.

Usage

The QIIME2_Illumina pipeline is composed of a set of scripts which should be run sequentially. The pipeline assumes you keep the default name to output files, and leave them in the working directory.

Train_classifier.sh

Usage: Train_classifier.sh <FW_primer> <RV_primer> <DB_FASTA> <TAXONOMY_TSV> <MIN_LEN> <MAX_LEN>

Note: this script should be run only if you don't have a Naive-Bayes classifier trained on the region of interest of your marker gene yet.

Inputs:

<FW_primer>: the sequence of the forward PCR primer
<RV_primer>: the sequence of the reverse PCR primer
<DB_fasta>: a fasta file containing sequences of the reference database
<TAXONOMY_tsv>: a text file containing taxonomy corresponding to sequences in the reference database
<MIN_LEN>: minimum expected amplicon size
<MAX_LEN>: maximum expected amplicon size

Outputs:

<"DB".nb-classifier.qza>: Naive-Bayes classifier trained on the region of interest of the marker gene
<"DB".qza>: QIIME2 artifact of type DNAFASTAFormat containing reference sequences
<"TAXONOMY".qza>: QIIME2 artifact of type HeaderlessTSVTaxonomyFormat containing taxonomy of reference sequences

Create_manifest.sh

Usage: Create_manifest.sh <sample_metadata> <reads_dir>

Inputs:

<sample-metadata.tsv>: file containing metadata for all samples, validated with Keemei
<reads_dir>: directory containing R1 and R2 reads in fastq.gz format

Outputs:

<manifest.txt>: file used for importing reads in QIIME2

Import_data.sh

Usage: Import_data.sh <manifest.txt> <FW_primer> <RV_primer>

Inputs:

<manifest.txt>: file used for importing reads in QIIME2
<FW_primer>: the sequence of the forward PCR primer
<RV_primer>: the sequence of the reverse PCR primer

Outputs:

<sequences_untrimmed.qza>: reads before PCR primers trimming
<sequences.qza>: reads after PCR primers trimming
<demux_summary_untrimmed.qzv>: QIIME2 visualization file for inspecting sequencing quality before PCR primers trimming
<demux_summary.qzv>: QIIME2 visualization file for inspecting sequencing quality after PCR primers trimming

Denoise_sequences.sh

Usage: Denoise_sequences.sh <trim_left_FW> <trim_left_RV> <trunc_left_FW> <trunc_left_RV>

Inputs:

<trim_left_FW>: number of bases to be trimmed from 5' end of forward reads
<trim_left_RV>: number of bases to be trimmed from 5' end of reverse reads
<trunc_left_FW>: number of bases to truncate forward reads at (choose a value by looking at demux_summary.qzv file)
<trunc_left_RV>: number of bases to be truncate reverse reads at (choose a value by looking at demux_summary.qzv file)

Outputs:

<rep-seqs.qz*>: representative sequences (Amplicon Sequence Variants, ASVs)
<table.qz*>: table with counts for each ASV
<denoising-stats.qzv>: statistics describing denoising performed by DADA2

Assign_taxonomy.sh

Usage: Assign_taxonomy.sh <classifier>

Inputs:

<classifier>: QIIME2 artifact (.qza) for Naive-Bayes classifier

Outputs:

<taxonomy.qz*>: taxonomy assigned to each ASV
<taxa-bar-plots.qzv>: taxonomy barplot describing samples composition at each taxonomic level
<feature-table_*freq.tsv>: tables with the number or proportion of reads assigned to each taxa

Diversity_analyses.sh

Usage: Diversity_analyses.sh <sampling_depth>

Inputs:

<sampling_depth>: minimum number of filtered fragments for a sample to be included in the analyses; the value should be chosen by looking at table.qzv file

Outputs:

<core-metrics-results>: directory containing various beta diversity analyses
<alpha-rarefaction.qzv>: rarefaction curve, computing the dependance between sequencing depths and various alpha diversity metrics

Results visualization

All .qzv and .qza artifacts can be visualized either importing them to QIIME2 View or with command:

source activate QIIME2_Illumina_env
qiime tools view <file.qz*>

Citations

The QIIME2_Illumina pipeline is composed of some wrapper scripts for QIIME2.

If this tool is useful for your work, please consider citing our manu scripts.

Matoute, A.; Maestri, S.; Saout, M.; Laghoe, L.; Simon, S.; Blanquart, H.; Hernandez Martinez, M.A.; Pierre Demar, M. Meat-Borne-Parasite: A Nanopore-Based Meta-Barcoding Work-Flow for Parasitic Microbiodiversity Assessment in the Wild Fauna of French Guiana. Curr. Issues Mol. Biol. 2024, 46, 3810-3821. https://doi.org/10.3390/cimb46050237

Klain, V., Maestri, S., & Bicca-Marques, J. C. (2024). Landscape structure influences the eukaryome of a folivorous-frugivorous primate. Studies on Neotropical Fauna and Environment, 1–8. https://doi.org/10.1080/01650521.2024.2423559

Please, refer to the following manuscript for further information.

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