ANOMALY: a Snakemake pipeline for identifying NuMTs from long-read sequencing data.

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Nuclear mitochondrial DNA segments (NuMTs) are implicated in cancer development and complicate mitochondrial variant analysis.
• Existing detection methods using short-read sequencing data are insufficient for resolving complex NuMTs.

Purpose of the Study:

• To develop and validate a novel workflow for accurate NuMT detection from long-read sequencing data.
• To address the limitations of current NuMT detection methods.

Main Methods:

• Introduction of ANOMALY, an easy-to-use workflow for NuMT detection from long-read sequencing data.
• The pipeline processes raw or aligned sequencing data to identify and visualize NuMTs.
• Utilizes Python, Bash, and R, built on the Snakemake framework.

Main Results:

• ANOMALY demonstrated high accuracy on 50 simulated datasets, achieving precision of 1.000, recall of 0.989, and an F1-score of 0.994.
• Confirmed the superiority of long-read sequencing data for resolving and capturing complex NuMTs compared to short-read data.
• The workflow is published under an open-source GNU GPL v3 license with code available on GitHub.

Conclusions:

• Long-read sequencing data, processed by ANOMALY, enables accurate identification of NuMTs.
• The ANOMALY workflow provides a robust solution for NuMT detection, crucial for cancer research and diagnostics.
• Highlights the limitations of short-read sequencing for complex genomic structural variations.