SIGNAL: A high-throughput pipeline for large-scale analysis of microbial signal transduction systems

🚧 This project is under active development. Expect frequent changes.

SIGNAL (Systematic Investigation of Genomic Networks for Analysis of Logic-based signaling) is a pipeline designed to perform high-throughput analysis of bacterial and archaeal genomes to uncover patterns in signal transduction systems across genomes, taxonomic groups, and functional architectures. The current dataset includes 26,221 representative genomes.

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System Requirements

Hardware

A standard computer with sufficient RAM for in-memory processing should be adequate.

Software

• Python 3.6 or higher

Tested Operating Systems

• Ubuntu 20.04
• Linux Mint 20.2

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Installation

Clone the repository using Git:

git clone https://github.com/ToshkaDev/signal-transduction.git

This will download the repository and set up the pipeline for use.

Usage

To launch the pipeline, use the master script:

cd signal-transduction
./analyze.sh

The script will first check whether the initial long-running step has already been completed by examining the presence of files in results/obtain_and_process_st/. Based on this, it will either start the entire pipeline or skip the completed steps.

Pipeline Steps

1. Preparation

• Unpacks archived input files
• Extracts genome lists (bacterial and archaeal)
• Assigns genome sources (MiST genomes or MiST MAGs databases)
• Creates all necessary input and output directories

Input files include:

• A dataset of 26,221 bacterial and archaeal genomes. It is also possible to use your own list of genomes prepared in accordance with the format used.
• Signal transduction domain definitions from the MiST (Microbial Signal Transduction) database
• A metadata file from the Genome Taxonomy Database (GTDB, release r214)

2. ST Extraction and Processing (obtain_and_process_st.py)

• Fetches signal transduction systems (two-component and one-component) from the MiST database using its API
• Analyzes protein domain compositions and architectures
• Outputs tabulated results listing:
  • Genomes
  • Histidine kinases (HKs), response regulators (RRs), and one-component systmes (OCP)
  • Their protein domain compositions and architectures

3. Genome-Level Analysis (analyze_st_per_genome.py)

• Analyzes and reports domain composition statistics for HKs, RRs, and OCPs per genome
• Reports:
  • Number and type of input domains in HKs and OCPs
  • Additional domains in RRs
• Normalizes statistics by genome size and total number of encoded proteins

4. Taxonomy-Level Analysis (analyze_st_per_taxon.py)

• Analyzes domain composition statistics for HKs, RRs, and OCPs at each taxonomic level:
  • Species
  • Genus
  • Family
  • Order
  • Class
  • Phylum
  • Kingdom
• Normalizes results by the number of genomes per taxonomic level

The GTDB taxonomy is used.

Future Work

• Visualization modules for domain architecture patterns