BOTAS

An Integrated Bacterial RNA-seq Analysis Framework with Circular-Aware Alignment and Operon Inference

Clabe Simiyu Wekesa, Kelvin Kiprotich, John Muoma, Axel Mithöfer

BOTAS (Bacterial Operon-Aware Transcriptome Alignment System) is an integrated framework for bacterial RNA-seq analysis that combines native reference indexing, read alignment, gene quantification, and operon inference within a single command-line application.

Unlike conventional RNA-seq workflows that require multiple independent software packages, BOTAS provides an end-to-end bacterial transcriptomics workflow specifically designed for prokaryotic genomes. The framework natively supports circular chromosomes and plasmids, paired-end sequencing, strand-specific expression analysis, and operon-aware transcriptome analysis while maintaining compatibility with standard genomic file formats.

BOTAS is implemented entirely in Python and is designed for reproducible, modular and high-throughput bacterial transcriptomic analyses.

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Features

Native Reference Indexing

• Native minimizer-based reference indexing
• Reusable BOTAS index format (*.botas.idx)
• Configurable k-mer and minimizer window sizes
• Support for linear and circular bacterial genomes
• Circular overhang indexing for origin-spanning reads

RNA-seq Alignment

• Native bacterial read alignment engine
• Single-end and paired-end read alignment
• Automatic handling of circular genome boundary crossings
• Edit-distance alignment using Edlib
• Mapping quality estimation
• Coordinate-sorted BAM output
• Multi-threaded execution

Gene Quantification

• Gene-level read and fragment counting
• Accurate paired-end fragment reconstruction
• Strand-specific quantification
• TPM and RPKM normalization
• Multi-sample quantification
• Assignment statistics for mapped, ambiguous, unmapped and unassigned fragments
• Gene counts compatible with featureCounts

Operon Inference

• RNA-seq-guided operon prediction
• Integration of genomic organization and transcriptional evidence
• Strand consistency analysis
• Intergenic distance evaluation
• Expression continuity assessment
• Paired-end connectivity analysis
• Consensus operon prediction across multiple samples
• TSV and GFF outputs

General

• Pure Python implementation
• Minimal external dependencies
• Reproducible command-line workflow
• Modular architecture
• Easily integrated into automated pipelines

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Installation

Install from PyPI

pip install botas-rnaseq

Install the latest development version

pip install git+https://github.com/clabe-wekesa/botas.git

Install from source

git clone https://github.com/clabe-wekesa/botas.git
cd botas
pip install .

Development installation

pip install -e ".[dev]"

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Workflow

A typical BOTAS analysis consists of four steps.

Reference FASTA
       │
       ▼
botas index
       │
       ▼
BOTAS index (.botas.idx)
       │
       ▼
botas align
       │
       ▼
Coordinate-sorted BAM
       │
       ├──────────────┐
       ▼              ▼
botas quantify   botas getOperons
       │              │
       ▼              ▼
 Gene counts      Operon predictions

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Quick Start

1. Build a reference index

botas index -r reference.fasta -o reference.botas.idx

For circular bacterial genomes:

botas index -r reference.fasta -o reference.botas.idx --circular

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2. Align sequencing reads

Paired-end

botas align -x reference.botas.idx -1 reads_R1.fastq.gz -2 reads_R2.fastq.gz --sort-bam

Single-end

botas align -x reference.botas.idx -U reads.fastq.gz --sort-bam

BOTAS automatically creates a project directory containing intermediate files, logs and final alignment results.

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3. Quantify gene expression

botas quantify -b alignment.bam -g annotation.gff --feature-type gene --gff-gene-attribute locus_tag

BOTAS reports

• raw gene counts
• TPM
• RPKM
• fragment assignment statistics
• summary reports

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4. Infer operons

botas getOperons -b alignment.bam -g annotation.gff

Predicted operons are exported as

• TSV tables
• GFF annotations

for downstream visualization and comparative genomics analyses.

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Output Structure

Each BOTAS analysis creates a dedicated working directory.

sample.botas/
│
├── logs/
├── temp/
├── results/
│   ├── alignment.bam
│   ├── alignment.bam.bai
│   ├── gene_counts.tsv
│   ├── summary.tsv
│   ├── operons.tsv
│   └── operons.gff
│
└── config.json

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Command Overview

botas index         Build a BOTAS reference index

botas align         Align RNA-seq reads

botas quantify      Quantify gene expression

botas getOperons    Predict bacterial operons

Detailed help is available for every command.

botas --help
botas index --help
botas align --help
botas quantify --help
botas getOperons --help

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Supported Input Formats

Analysis	Input
Indexing	FASTA
Alignment	FASTQ, FASTQ.GZ
Quantification	BAM, GFF3
Operon inference	BAM, GFF3

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Supported Output Formats

Analysis	Output
Indexing	BOTAS index (*.botas.idx)
Alignment	BAM
Quantification	TSV
Operon inference	TSV, GFF

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Requirements

• Python 3.10 or later
• pysam
• edlib
• Biopython

Optional

• tqdm

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Citation

If you use BOTAS in published research, please cite:

Wekesa CS, Kiprotich K, Muoma J, Mithöfer A. BOTAS: An Integrated Bacterial RNA-seq Analysis Framework with Circular-Aware Alignment and Operon Inference. (Manuscript under review.)

Citation information will be updated following publication.

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License

BOTAS is distributed under the MIT License.

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Author

Clabe Simiyu Wekesa

GitHub: https://github.com/clabe-wekesa