16SfastLAB is a reproducible and scalable pipeline for processing 16S rRNA gene amplicon sequencing data. It assigns taxonomic information at the genus level for the Lactobacillaceae family using Snakemake and Conda.

Contents

• Introduction
• Workflow
• Installation
• Usage
• Input
• Output
• Options
• Dependencies
• Example Data
• Tips 🐯

Introduction

The 16SfastLAB Pipeline is designed for bioinformatics users who need to quickly understand the presence of lactic acid bacteria in paired-end 16S rRNA sequencing data. This pipeline:

This pipeline:

• Merges paired-end FASTQ files using VSEARCH.
• Converts merged FASTQ files into FASTA format.
• Runs BLASTn against a custom 16S database.
• Extracts genus-level information from BLAST output.
• Filters and summarizes BLAST results to generate relative abundance frequency tables.
• Combines frequency data across samples into a single CSV, organized by the latest taxonomic order of Lactobacillaceae.

Workflow

Installation

Prerequisites

• Conda/Mamba: Ensure you have Miniconda installed. (Using Mamba for faster environment resolution is recommended.)
• Git: To clone the repository.

Steps

1. Clone the repository:

   git clone https://github.com/nanzhen102/16SfastLAB.git
   cd 16SfastLAB

2. Create Conda Environments:

   Please install the following manually before running the pipeline:

   • vsearch (≥2.15)
   • blastn
   • Python 3 (≥3.8) with packages:
     • biopython
     • pandas

   conda install bioconda::vsearch # install vsearch (then blastn will be installed at the same time)
   mamba install -c conda-forge -c bioconda biopython # install biopython

Usage

From the main project directory, execute the pipeline with:

snakemake --cores 8 

This command will:

• Build and activate the required Conda environments.
• Process each sample from the data/ directory.
• Generate intermediate and final outputs in the results/ directory.
• Log the execution of each rule in the logs/ directory.

Input

Data Files

• The pipeline expects both paired-end and single-end FASTQ files to be stored in the data/ directory.
• File naming convention: ERRxxxxxx_1.fastq.gz and ERRxxxxxx_2.fastq.gz, or SRRxxxxxx_1.fastq.gz and SRRxxxxxx_2.fastq.gz, or ERRxxxxxx_1.fastq and ERRxxxxxx_2.fastq, or SRRxxxxxx_1.fastq and SRRxxxxxx_2.fastq, or ERRxxxxxx_trimmed.fastq.
• A configuration file config.yaml is used to specify directory paths, database locations, and tool parameters.

Output

After running the pipeline, the results/ directory will contain:

• Merged FASTQ Files: e.g., xxxxxx_merged.fastq
• FASTA Files: e.g., xxxxxx_merged.fasta
• BLASTn Output Files: e.g., xxxxxx_blastn_ssu_r220_LAB.out
• Genus Match Files: e.g., xxxxxx_genus_match.csv
• Filtered Results: e.g., xxxxxx_filtered.csv
• Frequency Tables: e.g., xxxxxx_frequency.csv
• Combined Frequency Table: combined_genera_frequency.csv

Logs for each rule are stored in the logs/ directory.

Options

• Conda Flag: --use-conda must be specified when running Snakemake to use the Conda environment files.
• Cores: --cores <N> specifies the number of cores to utilize.
• Config File: The config.yaml file controls input/output paths and parameters. Modify this file to adjust database paths, tool parameters, or directory settings.

Dependencies

• Snakemake
• Conda/Mamba
• BLAST+
• VSEARCH

Example Data

In the data/ directory.

Tips 🐯

To download SRR files from NCBI SRA database:

fasterq-dump SRR24916211 --split-files --gzip -O data/

Or

tail -n +2 SraAccList.csv | while read srr; do
  fasterq-dump "$srr" --split-files -O data/
done