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This project investigates the microbial and viral communities, vertebrate biodiversity, and prevalence of antimicrobial resistance (AMR) genes in 12 European wetlands across Germany, France, and Spain

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Real-time genomic pathogen, resistance, and host range characterization from passive water sampling of wetland ecosystems (PRJEB96272)

Project Overview

This project investigates the microbial and viral communities, vertebrate biodiversity, and prevalence of antimicrobial resistance (AMR) genes in 12 European wetlands across Germany, France, and Spain, along the east atlantic flyway, a major bird migration corridor. Samples were collected from sites categorized by land use (anthropogenic vs. natural) using torpedo-shaped passive water samplers. From dual DNA/RNA extractions, four parallel analyses were conducted using Oxford Nanopore Technologies sequencing (MinION Mk1D/Mk1C, R10.4.1 flow cells):

  1. Shotgun Metagenomics (DNA): Characterization of microbial communities and AMR genes.
  2. Vertebrate Metabarcoding (12S rRNA rRNA): Identification of local vertebrate fauna using 12S rRNA rRNA amplicons.
  3. Avian Influenza Virus (AIV) Analysis (RNA): Targeted whole-genome sequencing of AIV-positive samples.
  4. RNA Viromics (RNA): Untargeted analysis of RNA virus communities.

This repository contains the bioinformatic workflows used to process the sequencing data.

ENA Files: https://www.ebi.ac.uk/ena/browser/view/PRJEB96272 for Metagenomic, 12S rRNA, and Virome data.

AIV Files: GSAID EPI4628641 (Note: Account required for download!)

Sample Information and Barcoding Scheme

⚠️ IMPORTANT: This table is essential for reproducing our analyses. It maps sampling locations to their corresponding barcodes and FASTQ files.

Site Coding System

Sites are coded using a [Country][Environment][Number] format:

  • Country: G (Germany), F (France), S (Spain)
  • Environment: A (Anthropogenic), N (Natural)
  • Number: Sequential identification within each category

1. DNA Shotgun Metagenomics Samples

Location Sample ID ONT Barcode FASTQ Filename
Germany - Anthropogenic
GA1 GA1.1 BC01 SQK-RBK114-24_barcode01.GA1.1.fastq.tar.gz
GA1 GA1.2 BC01 SQK-RBK114-24_barcode01.GA1.2.fastq.tar.gz
GA2 GA2.1 BC03 SQK-RBK114-24_barcode03.GA2.1.fastq.tar.gz
GA2 GA2.2 BC03 SQK-RBK114-24_barcode03.GA2.2.fastq.tar.gz
GA3 GA3.1 BC05 SQK-RBK114-24_barcode05.GA3.1.fastq.tar.gz
GA3 GA3.2 BC05 SQK-RBK114-24_barcode05.GA3.2.fastq.tar.gz
Germany - Natural
GN1 GN1.1 BC17 SQK-RBK114-24_barcode17.GN1.1.fastq.tar.gz
GN1 GN1.2 BC07 SQK-RBK114-24_barcode07.GN1.2.fastq.tar.gz
GN2 GN2.1 BC07 SQK-RBK114-24_barcode07.GN2.1.fastq.tar.gz
GN2 GN2.2 BC07 SQK-RBK114-24_barcode07.GN2.2.fastq.tar.gz
GN3 GN3.1 BC09 SQK-RBK114-24_barcode09.GN3.1.fastq.tar.gz
GN3 GN3.2 BC09 SQK-RBK114-24_barcode09.GN3.2.fastq.tar.gz
France - Anthropogenic
FA1 FA1.1 BC19 SQK-RBK114-24_barcode19.FA1.1.fastq.tar.gz
FA1 FA1.2 BC19 SQK-RBK114-24_barcode19.FA1.2.fastq.tar.gz
FA2 FA2.1 BC21 SQK-RBK114-24_barcode21.FA2.1.fastq.tar.gz
FA2 FA2.2 BC13 SQK-RBK114-24_barcode13.FA2.2.fastq.tar.gz
France - Natural
FN1 FN1.1 BC11 SQK-RBK114-24_barcode11.FN1.1.fastq.tar.gz
FN1 FN1.2 BC11 SQK-RBK114-24_barcode11.FN1.2.fastq.tar.gz
Spain - Anthropogenic
SA1 SA1.1 BC23 SQK-RBK114-24_barcode23.SA1.1.fastq.tar.gz
SA1 SA1.2 BC15 SQK-RBK114-24_barcode15.SA1.2.fastq.tar.gz
Spain - Natural
SN1 SN1.1 BC13 SQK-RBK114-24_barcode13.SN1.1.fastq.tar.gz
SN1 SN1.2 BC13 SQK-RBK114-24_barcode13.SN1.2.fastq.tar.gz
SN2 SN2.1 BC15 SQK-RBK114-24_barcode15.SN2.1.fastq.tar.gz
SN2 SN2.2 BC15 SQK-RBK114-24_barcode15.SN2.2.fastq.tar.gz

2. 12S rRNA Vertebrate Metabarcoding Samples

2. 12S rRNA Vertebrate Metabarcoding Samples

Raw Data: torpedo12s_all.fastq.gz (multiplexed file containing all samples)

Location Sample ID ONT Barcode 9bp Tag
Germany - Anthropogenic
GA1 1 BC01 acataacgc
GA2 2 BC02 tagtggttc
GA3 3 BC03 caatagtga
Germany - Natural
GN1 4 BC04 taatacggt
GN2 5 BC05 agtcaaggc
GN3 6 BC06 actattggt
France - Anthropogenic
FA1 7 BC07 agtgtccag
FA2 8 BC08 aagacttcc
France - Natural
FN1 9 BC09 ttacagatc
Spain - Anthropogenic
SA1 10 BC10 ctccacttc
Spain - Natural
SN1 11 BC11 ctatccgcc
SN2 12 BC12 ctgcgagca
Controls
Negative PCR c_neg_pcr BC13 ttagtccac
Negative Extraction c_neg_ext BC14 cgcacagcc
Positive Control c_pos_sample_dani BC15 ttatatatg

3. AIV (RNA) Analysis Samples

AIV Files: GSAID EPI4628641

4. RNA Viromics Samples

Location Sample ID ONT Barcode FASTQ Filename
Germany - Anthropogenic
GA1 GA1 BC01 SQK-RPB114-24_barcode01.GA1.fastq.tar.gz
GA2 GA2 BC02 SQK-RPB114-24_barcode02.GA2.fastq.tar.gz
GA3 GA3 BC03 SQK-RPB114-24_barcode03.GA3.fastq.tar.gz
Germany - Natural
GN1 GN1 BC04 SQK-RPB114-24_barcode04.GN1.fastq.tar.gz
GN2 GN2 BC05 SQK-RPB114-24_barcode05.GN2.fastq.tar.gz
GN3 GN3 BC06 SQK-RPB114-24_barcode06.GN3.fastq.tar.gz
France - Anthropogenic
FA1 FA1 BC08 SQK-RPB114-24_barcode08.FA1.fastq.tar.gz
FA2 FA2 BC02 SQK-RPB114-24_barcode02.FA2.fastq.tar.gz
France - Natural
FN1 FN1 BC01 SQK-RPB114-24_barcode01.FN1.fastq.tar.gz
Spain - Anthropogenic
SA1 SA1 BC11 SQK-RPB114-24_barcode11.SA1.fastq.tar.gz
Spain - Natural
SN1 SN1 BC10 SQK-RPB114-24_barcode10.SN1.fastq.tar.gz
SN2 SN2 BC12 SQK-RPB114-24_barcode12.SN2.fastq.tar.gz

Site Descriptions

  • Anthropogenic sites (n=6):

    • GA1: Urban landfill pond
    • GA2: Village wetland
    • GA3: Urban pond
    • FA1: Intensive duck farm
    • FA2: Intensive duck farm
    • SA1: Sewage treatment plant

  • Natural sites (n=6):

    • GN1-3: Coastal marshes/peat bogs
    • FN1: Wetland reserve
    • SN1-2: Wetland reserves

Notes:

  • DNA shotgun samples: Duplicate extractions per site (_1 and _2 suffixes)
  • 12S rRNA rRNA, AIV, Virome samples: Single extraction per site (location = sample ID)
  • 12S rRNA rRNA samples: Include both ONT barcodes and 9bp tags for demultiplexing
  • AIV samples: Only AIV-positive samples proceed to consensus genome generation
  • All sites are lentic (non-flowing) wetlands
  • Raw FASTQ files are available at ENA under accession PRJEB96272

Analysis Pipeline Overview

This repository outlines four main analysis pipelines:

  1. DNA Shotgun Metagenomics: Processes FASTQ files from environmental DNA for community analysis, assembly, and AMR/pathogen detection.
  2. AIV (RNA) Analysis: Aligns reads from AIV-positive samples to generate consensus genomes.
  3. Viral Metagenomics (Viromics): Processes cDNA reads from total environmental RNA to characterize the RNA virome.
  4. 12S rRNA rRNA Vertebrate Genomics: Processes 12S rRNA gene amplicons to identify vertebrate species.
flowchart TD
    A[Passive Water Sample] --> B[DNA]
    A --> C[RNA]
    
    %% DNA Branch
    B --> D[DNA Shotgun Metagenomics]
    B --> E[12S rRNA Vertebrate Genomics]
    
    %% DNA Shotgun Pipeline
    D --> D1[Basecalling & Demultiplexing<br/>Dorado v5.0.0]
    D1 --> D2[Adapter Trimming<br/>Porechop v0.2.4]
    D2 --> D3[Quality Filtering<br/>NanoFilt v2.8.0<br/>≥100 bp]
    D3 --> D4{Analysis Strategy}
    
    D4 --> D5[Community Analysis]
    D4 --> D6[Hazard Detection]
    
    D5 --> D5a[Rarefaction<br/>Seqkit v2.3.0<br/>87,000 reads]
    D5a --> D5b[Taxonomic Classification<br/>Kraken2 v2.1.2<br/>NCBI nt_core]
    D5a --> D5c[Pathogen ID - Reads<br/>Minimap2 v2.24 +<br/>MEGAN-CE v6.21.1]
    
    D6 --> D6a[Full Dataset]
    D6a --> D6b[De novo Assembly<br/>nanoMDBG v1.1]
    D6b --> D6c[Pathogen ID - Contigs<br/>Minimap2 v2.24 +<br/>MEGAN-CE v6.21.1]
    D6a --> D6h[Pathogen ID - Reads<br/>Minimap2 v2.24 +<br/>MEGAN-CE v6.21.1]
    D6b --> D6d[AMR Detection<br/>AMRFinderPlus v4.0.23<br/>+ Prodigal v2.6.3]
    D6b --> D6e[Plasmid Detection<br/>PlasmidFinder v2.1.6]
    D6a --> D6f[Virulence Factors<br/>DIAMOND v2.1.13<br/>vs VFDB]
    
    %% 12S rRNA Pipeline
    E --> E1[PCR Amplification<br/>12S rRNAV05 primers<br/>+ human blocking]
    E1 --> E2[Demultiplexing<br/>OBITools4 v1.3.1<br/>9 bp tags]
    E2 --> E3[Primer Trimming<br/>Cutadapt v4.2]
    E3 --> E4[Quality Filtering<br/>VSEARCH v2.21<br/>maxEE 1.0]
    E4 --> E5[Dereplication &<br/>Singleton Removal<br/>VSEARCH]
    E5 --> E6[Chimera Removal<br/>VSEARCH]
    E6 --> E7[OTU Clustering<br/>VSEARCH<br/>97% similarity]
    E7 --> E8[Taxonomic Assignment<br/>VSEARCH<br/>vs MIDORI2]
    
    %% RNA Branch
    C --> F[AIV Analysis]
    C --> G[Viral Metagenomics]
    
    %% AIV Pipeline
    F --> F1[Basecalling & Demultiplexing<br/>Dorado v5.0.0]
    F1 --> F2[Quality Filtering]
    F2 --> F3[Alignment to AIV References<br/>Minimap2 v2.28<br/>-ax map-ont]
    F3 --> F4[Best Reference Selection<br/>SAMtools v1.17]
    F4 --> F5[Consensus Generation<br/>BCFtools v1.17<br/>8 segments]
    
    %% Viromics Pipeline
    G --> G1[DNase Treatment]
    G1 --> G2[cDNA Synthesis<br/>SMART-9N protocol]
    G2 --> G3[Adapter Trimming<br/>Porechop v0.2.4]
    G3 --> G4[Quality Filtering<br/>NanoFilt v2.8.0]
    G4 --> G5[Translated Alignment<br/>DIAMOND BLASTx v2.1.13<br/>vs NCBI NR]
    G5 --> G6[RNA Virome Profile]
    
    %% Styling
    classDef dna fill:#e1f5fe,stroke:#01579b,stroke-width:2px
    classDef rna fill:#fce4ec,stroke:#880e4f,stroke-width:2px
    classDef tool fill:#f3e5f5,stroke:#4a148c,stroke-width:2px
    
    class B,D,E,D1,D2,D3,D4,D5,D5a,D5b,D5c,D6,D6a,D6b,D6c,D6d,D6e,D6f,D6g,D6h,E1,E2,E3,E4,E5,E6,E7,E8 dna
    class C,F,G,F1,F2,F3,F4,F5,G1,G2,G3,G4,G5,G6 rna
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Tools Used

This project integrates the following key bioinformatics tools:

  • Basecalling, Trimming and File Transformation:
    • Dorado: Basecaller for ONT data (v5.0.0).
    • Porechop: Adapter and barcode trimming (v0.2.4).
    • NanoFilt: Quality and length filtering for ONT reads (v2.8.0).
    • Seqkit: Toolkit for FASTA/Q sequence manipulation (v2.3.0).
  • Metagenomics & Assembly:
    • Kraken2: K-mer based taxonomic classification (v2.1.2).
    • metaFlye: Long-read assembler for metagenomes (v2.9.6).
    • nanoMDBG: Long-read assembler for metagenomes (v1.1).
    • Minimap2: Long-read alignment (v2.28).
    • Racon: Consensus correction/polishing for assemblies (v1.5.0).
    • Medaka: Consensus correction/polishing for assemblies (v1.7.2).
    • Prokka: Prokaryotic genome annotation (v1.14.5).
  • Pathogen & AMR Analysis:
    • AMRFinderPlus: Detection of AMR genes (v4.0.23).
    • Prodigal: ORF prediction (v2.6.3).
    • DIAMOND: Protein sequence alignment (v2.1.13).
    • MEGAN-CE: Interactive metagenomic analysis (v6.21.1).
    • PlasmidFinder: Plasmid detection from assemblies (v2.1.6).
  • Vertebrate & Virome Analysis:
    • OBITools4: Barcode demultiplexing for amplicons (v1.3.1).
    • Cutadapt: Primer trimming (v4.2).
    • VSEARCH: Sequence analysis toolkit (clustering, chimera removal) (v2.21).
  • AIV & General Utilities:
    • SAMtools: Utilities for SAM/BAM alignment files (v1.17).
    • BCFtools: Utilities for variant calling and consensus generation (v1.17).

Repository Structure

  • dna_shotgun_analysis_pipeline.md: Detailed commands for the DNA shotgun metagenomics workflow.
  • aiv_rna_analysis_pipeline.md: Detailed commands for the AIV (RNA) analysis workflow.
  • virome_analysis_pipeline.md: Detailed commands for the viral metagenomics workflow.
  • rrna_vertebrate_analysis.md: Detailed commands for the 12S rRNA vertebrate analysis workflow.
  • Installation_tutorial.md: Guide for installing all required tools and databases.

Usage Workflow

DNA Shotgun Metagenomics Workflow

  1. Read Processing: Reads were demultiplexed and trimmed with Porechop, then filtered to remove reads < 100 bp with NanoFilt.

  2. Taxonomic Classification: Reads were classified using Kraken2 against the NCBI nt_core database. A dual strategy was used:

    • For community analysis (beta-diversity): Samples were rarefied to 87,000 reads using Seqkit.
    • For hazard detection (pathogens/AMR): The complete, non-rarefied dataset was used to maximize sensitivity.

  3. Metagenome Assembly & Annotation: De novo assembly was performed with nanoMDBG, chosen over metaFlye because its lack of a minimum read length requirement prevented the >50% data loss that metaFlye would have caused. Assemblies were functionally annotated with Prokka.

  4. Pathogen & AMR Gene Detection:

    • Pathogen ID: Reads and contigs were aligned to nt_core with Minimap2 and assigned taxonomy with MEGAN-CE using a conservative Lowest Common Ancestor (LCA) approach.
    • Virulence Factors: For specific samples (e.g., high Vibrio cholerae), reads were screened against the Virulence Factor Database (VFDB) using DIAMOND BLASTx.
    • AMR Genes: AMRFinderPlus (in --plus mode with Prodigal for ORF prediction) was run on both reads and contigs.
    • AMR Mobility: Contigs were screened for plasmids using PlasmidFinder.

    For detailed commands, see dna_shotgun_analysis_pipeline.md.

AIV (RNA) Analysis Workflow

  1. Read Processing: After basecalling and demultiplexing with Dorado (which removes primers), reads were quality filtered.

  2. Alignment to Reference Genomes: Filtered reads were aligned to a custom European AIV reference database using Minimap2 (v2.28) with the -ax map-ont setting.

  3. Consensus Sequence Generation: SAMtools was used to process alignments and identify the best-matching reference for each of the eight AIV segments. A consensus sequence for each segment was then generated using BCFtools.

    For detailed commands, see aiv_rna_analysis_pipeline.md.

Viral Metagenomics (Viromics) Workflow

  1. Library Prep & Read Processing: RNA was DNase-treated and then converted to cDNA using the Rapid SMART-9N protocol, which employs random priming and template switching. Barcoded amplicons were generated and sequenced. Reads were trimmed (Porechop) and filtered (NanoFilt).

  2. Taxonomic Classification: Reads were taxonomically classified by translated alignment using DIAMOND BLASTx against the NCBI non-redundant (NR) protein database. Viral hits were tallied to profile the RNA virome of each sample.

    For detailed commands, see virome_analysis_pipeline.md.

12S rRNA Vertebrate Genomics Workflow

  1. Library Prep: A ~97 bp fragment of the 12S rRNA rRNA gene was amplified using 12S rRNAV05 primers with 9 bp tags. A human-blocking oligonucleotide was included. The final library was prepared with the Ligation Sequencing Kit (SQK-LSK114).

  2. Read Processing: Reads were demultiplexed by their 9 bp tags using OBITools4. Primers were trimmed with Cutadapt.

  3. OTU Clustering & Classification: The VSEARCH pipeline was used to:

    • Filter reads by expected error (maxEE 1.0).
    • Dereplicate sequences and remove singletons.
    • Remove chimeras.
    • Cluster sequences into Operational Taxonomic Units (OTUs) at 97% similarity.

  4. Taxonomic Assignment: OTU representative sequences were identified via global alignment against the MIDORI2 reference database.

    For detailed commands, see rrna_vertebrate_analysis.md.

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This project investigates the microbial and viral communities, vertebrate biodiversity, and prevalence of antimicrobial resistance (AMR) genes in 12 European wetlands across Germany, France, and Spain

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metagenomics pathogens aiv viromics

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