BactSeq

Introduction

BactSeq is a Nextflow pipeline for performing bacterial RNA-Seq analysis. The pipeline supports both BWA alignment and Kallisto pseudo-alignment approaches, with optional differential expression and functional enrichment analysis.

Quick Start

# Run with test data
nextflow run BactSeq -profile test,docker

# Run with your own data
nextflow run BactSeq \
  --data_dir /path/to/fastq/files \
  --sample_file samples.tsv \
  --ref_genome genome.fasta \
  --ref_ann genome.gff3 \
  -profile docker

Pipeline Summary

The pipeline performs the following steps:

Core Analysis

1. Quality Control & Trimming

   • Trim adaptors from reads (Trim Galore!)
   • Read QC (FastQC)

2. Read Alignment (choose one)

   • BWA: Align reads to reference genome (BWA-MEM)
   • Kallisto: Pseudo-align reads to coding sequences (Kallisto)

3. Expression Quantification & Normalization

   • Size-factor scaling using DESeq2
   • TMM normalization using edgeR
   • RPKM scaling for gene length normalization

4. Exploratory Analysis

   • Principal component analysis (PCA) of normalized expression values
   • Sample clustering and visualization

Optional Analysis

5. Differential Expression (DESeq2)

   • Pairwise comparisons based on provided contrasts
   • Volcano plots and summary statistics

6. Functional Enrichment (topGO)

   • GO term enrichment analysis of differentially expressed genes
   • Enrichment plots and gene lists

Installation

You will need to install Nextflow (version 21.10.3+).

# Install Nextflow
curl -s https://get.nextflow.io | bash

# Or via conda
conda install -c bioconda nextflow

Usage

Basic Usage

nextflow run BactSeq \
  --data_dir /path/to/fastq/files \
  --sample_file samples.tsv \
  --ref_genome genome.fasta \
  --ref_ann genome.gff3 \
  -profile docker

Parameters

Required Parameters

Parameter	Description
--data_dir	Path to directory containing FastQ files
--sample_file	Path to file containing sample information
--ref_genome	Path to FASTA file containing reference genome sequence (BWA) or coding gene sequences (Kallisto)
-profile	Configuration profile to use: conda, docker, singularity

Optional Parameters

Parameter	Default	Description
--aligner	bwa	Aligner to use: bwa, kallisto
--ref_ann	-	Path to GFF file containing reference genome annotation (required for BWA)
--contrast_file	-	Path to TSV file containing contrasts for differential expression
--func_file	-	Path to functional annotation file for enrichment analysis
--strandedness	reverse	Data strandedness: unstranded, forward, reverse
--p_thresh	0.05	Adjusted p-value threshold for differential expression
--l2fc_thresh	1	Absolute log2(FoldChange) threshold for differential expression
--fragment_len	150	Average fragment length for Kallisto (single-end only)
--fragment_sd	20	Fragment length standard deviation for Kallisto (single-end only)
--skip_trimming	false	Skip adapter trimming
--outdir	./results	Output directory for results

Standard Nextflow Parameters

Parameter	Description
-resume	Resume a previous run
-name	Name for the pipeline run
-work-dir	Work directory for temporary files

Input Files

Note: See the test data folder for example inputs.

Required Files

1. Sample Sheet (samples.tsv)

   • TSV file containing sample information with the following columns:
     • sample: Sample ID
     • file1: Name of R1 FastQ file
     • file2: Name of R2 FastQ file (leave blank for single-end)
     • group: Grouping factor for differential expression
     • rep_no: Replicate number

   Example:

   sample	file1	file2	group	rep_no
   AS_1	SRX1607051_T1.fastq.gz		Artificial_Sputum	1
   AS_2	SRX1607052_T1.fastq.gz		Artificial_Sputum	2
   MB_1	SRX1607054_T1.fastq.gz		Middlebrook	1
   MB_2	SRX1607055_T1.fastq.gz		Middlebrook	2

2. Reference Genome (genome.fasta)

   • FASTA file containing the reference genome sequence
   • Can be downloaded from NCBI RefSeq

3. Gene Annotation (genome.gff3) - Required for BWA only

   • GFF3 file containing gene annotations
   • Can be downloaded from NCBI RefSeq

Optional Files

1. Contrasts Table (contrasts.tsv) - For differential expression

   • TSV file with 2 columns defining comparisons to perform
   • Column names: Condition1, Condition2

   Example:

   Condition1	Condition2
   Artificial_Sputum	Middlebrook
   Artificial_Sputum	Kanamycin
   Middlebrook	Kanamycin

2. Functional Annotation File (functional_annotation.csv) - For enrichment analysis

   • CSV file containing GO terms for genes
   • Column 1: Gene ID (must match locus_tag in GFF)
   • Column 2: GO terms (comma-separated)

   Example:

   Gene,GO_terms
   MAB_0001,"GO:0005737,GO:0008150"
   MAB_0002,"GO:0016020,GO:0006810"
   

Output

The pipeline generates the following output directories:

Directory	Contents
trim_galore/	Adapter-trimmed FastQ files and FastQC reports
read_counts/	Raw and normalized gene count matrices
PCA_samples/	Principal component analysis plots and coordinates
diff_expr/	Differential expression results and volcano plots
func_enrich/	Functional enrichment analysis results
pipeline_info/	Pipeline execution reports and metadata

Key Output Files

Count Matrices:

• gene_counts.tsv: Raw read counts per gene
• deseq_counts.tsv: DESeq2 normalized counts (log2 transformed)
• cpm_counts.tsv: Counts per million (CPM) normalized
• rpkm_counts.tsv: RPKM normalized counts

Analysis Results:

• DGE_*.tsv: Differential expression results for each contrast
• volcano_plot_*.png: Volcano plots for each contrast
• pca_grouped.png: PCA plot colored by sample groups
• *_enrich.tsv: GO enrichment results (if functional annotation provided)

Citation

If you use BactSeq in your research, please cite:

@software{bactseq,
  author = {Adam Dinan},
  title = {BactSeq: A Nextflow pipeline for bacterial RNA-seq analysis},
  url = {https://github.com/adamd3/BactSeq},
  version = {dev},
  year = {2024}
}

Changelog

Recent Updates

• 2025-07-19: Docker image is now compatible with nextflow 25.04.0+
• 2023-10-06: Docker and Singularity support restored
• 2023-09-28: Added example contrasts table and functional enrichment file