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Trypanosoma cruzi Replication Landscape 🧬

This repository contains the analytical frameworks, pipelines, and scripts developed for the study:
"Genome compartmentalization is linked to the replication program and mutational outcomes in Trypanosoma cruzi"
Franco, T. A., Freitas, R. P., Pires, D. S., Souza, V. C., Damasceno, J. D., McCulloch, R., & Elias, M. C. (2026).


🔬 Project Overview

Our research investigates how the spatial organization of the Trypanosoma cruzi genome influences its evolution. By integrating high-resolution D-NAscent mapping (Nanopore sequencing) with variant calling, we delineate the contrasting replication dynamics between the Core (conserved) and Disruptive (rapidly evolving) compartments and their impact on the mutational landscape (SNPs and Loss of Heterozygosity - LOH).

👥 Contributors

  • Thiago Andrade Franco (Lead Developer/First Author)
  • David da Silva Pires
  • Vinícius Carius de Souza
  • Maria Carolina Elias (Principal Investigator/Corresponding Author)

🛠️ Repository Content

This repository is organized to provide the specific workflows used for:

  • Replication Mapping: Implementation of Replication Fork Directionality (RFD) and Origin Efficiency Metrics (OEM).
  • Genomic Landmarks: Identification of Initiation (IZD) and Termination (TZD) Zone Domains.
  • Compartment Analysis: Classification of Polycistronic Units (PTUs) and genomic regions (Core vs. Disruptive/GpDR).
  • Mutational Load: Pipelines for variant calling (SNPs) and quantification of allelic imbalance (LOH index).

🛠️ Technical Stack

The project utilizes a multi-language approach and industry-standard bioinformatics tools:

  • Languages: Python, R, C++, Shell Script
  • Bioinformatics Tools:
    • Freebayes: Variant calling (SNPs/Indels)
    • BCFtools: SNP analysis and filtering
    • BEDtools: Genomic coordinate processing
    • D-Nascent: Replication fork identification
    • Winnowmap: Long-read mapping
    • Picard & Samtools: Data processing and quality control
    • SnpEff: Variant annotation and dN/dS estimation

⚙️ Pipelines Description

The analysis is divided into three specialized workflows:

1. DNA Replication Dynamics Identification

This pipeline processes raw outputs to identify and classify replication events at base-pair resolution.

  • Functionality: Employs C++ programs and ad-hoc scripts to construct and classify datasets for replication origins (IZDs) and termination sites (TZDs).
  • Key Output: A curated, high-precision dataset of genomic replication landmarks.

2. Genomic Mapping & Enrichment Analysis

Focuses on the spatial distribution of replication events across genomic compartments and polycistronic units.

  • Mapping: Origins and terminations are intersected with genomic features using BEDtools and custom Shell/R scripts.
  • Polycistron Definition: Dedicated pipeline for defining polycistronic regions based on gene family and GC content (as detailed in the paper’s Methods).
  • Statistics: Calculates fold-enrichment of replication events relative to genome-wide expected frequencies.

3. Variant Analysis & Mutational Asymmetry

A comprehensive workflow for analyzing genetic diversity and mutational bias.

  • Variant Calling: High-quality read selection and mapping followed by variant discovery.
  • Asymmetry Analysis: Calculates strand-specific SNP density at replication landmarks and performs Loss of Heterozygosity (LOH) analysis.
  • Functional Impact: Variant annotation via SnpEff to provide the basis for dN/dS ratio calculations and evolutionary interpretation.

📊 Data Availability

The raw sequencing data analyzed in this study are publicly available in the NCBI Sequence Read Archive (SRA) under:

  • BioProject: PRJNA1002335
  • Accession Numbers: SAMN36840645 to SAMN36840649.

🚀 How to Use

  1. Clone the repository:
    git clone https://github.com/Tryp-Rep-Laboratory/tcruzi-replication-landscape.git
  2. Environment: Ensure all tools listed in the "Technical Stack" are installed (we recommend using Conda for environment management).
  3. Scripts: Detailed execution instructions for each script can be found within their respective directories.

📝 Citation

If you use these pipelines or data in your research, please cite:

Franco, T. A., et al. (2026). Genome compartmentalization is linked to the replication program and mutational outcomes in Trypanosoma cruzi. [Journal Name/DOI Link].


© 2024 Tryp-rep Lab - Butantan Institute

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Analytical frameworks for high-resolution DNA replication mapping and mutational landscape analysis in Trypanosoma cruzi (Core vs. Disruptive compartments).

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