LABhabitPred is a reproducible and scalable Snakemake pipeline for predicting environmental habitat preferences of 16S rRNA sequences, specifically targeting the Lactobacillaceae family.

Contents

• Introduction
• Installation
• Usage
• Input
• Output
• Options
• Dependencies
• Example Data

Introduction

The LABhabitPred Pipeline is designed for researchers who need to infer the environmental habitat preferences of Lactobacillaceae sequences using 16S rRNA gene data. The pipeline:

• Runs BLASTn against a curated LAB-specific 16S reference database.
• Filters BLAST results by percent identity (≥97%) and alignment length (≥150 bp).
• Maps sequences to environmental metadata (isolation sources, taxonomy).
• Scores habitat preferences using category-specific weighted scoring adapted from the ProkAtlas method.

Installation

Prerequisites

• Conda/Mamba: Miniconda is required. Mamba is recommended for faster dependency resolution.
• Git: To clone the repository.

Steps

1. Clone the repository:

git clone https://github.com/nanzhen102/LABhabitatPre.git
cd LABhabitatPre

2. Create Conda Environment:

Install dependencies manually:

conda create -n lab_habitat -c bioconda snakemake pandas numpy blast biopython
conda activate lab_habitat

Usage

From the main project directory, execute the pipeline with:

snakemake --cores 8

This command will:

• Process each sample from the data/ directory.
• Run BLAST and filtering steps.
• Map metadata and generate habitat profiles.
• Log execution in the logs/ directory.

Input

Data Files:

• Place your query 16S rRNA FASTA sequences in the data/ directory.
• Filename format: sample1.fasta, sample2.fasta, etc.

Database Files:

• LAB-specific BLAST database files should be placed in 16S_database/.
• Ensure ssu_all_r220_Lactobacillaceae_deduplicated_1200bp_noN_matched_metadata.csv and source_class_weight.csv are available in this directory.

Output

After pipeline completion, the results/ directory will contain:

• Raw BLAST results: results/blast_raw/sample_blast_results.tsv
• Filtered BLAST results: results/blast_filtered/sample_blast_results_filtered.tsv
• Metadata-mapped results: results/mapped_metadata/sample_blast_results_filtered_metadata.csv
• Habitat profiles: results/habitat_profiles/sample_habitat_profile.csv

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

Options

• Conda environment:
  Use --use-conda flag to activate Conda environments.
• Cores:
  --cores <N> specifies the number of cores.

Dependencies

• Snakemake
• Conda/Mamba
• BLAST+
• Python ≥3.8
• Pandas
• NumPy
• Biopython

Example Data

Available in the data/ directory.