DNAscan: personal computer compatible NGS analysis, annotation and visualisation.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Next Generation Sequencing (NGS) is crucial for genetic research and precision medicine.
• Significant challenges exist in NGS data analysis, including pipeline design, computational intensity, and result interpretation.
• Limited high-performance computing and privacy concerns hinder cloud-based NGS analysis.

Purpose of the Study:

• To develop a fast and efficient bioinformatics pipeline for DNA sequencing data analysis.
• To address the computational and interpretational challenges in NGS data processing.
• To provide an accessible tool for biomedical research and diagnostic medicine.

Main Methods:

• Developed DNAscan, a Python-based bioinformatics pipeline.
• Implemented efficient algorithms for minimal computational effort and memory usage.
• Integrated customizable database annotation and on-the-fly visualization with gene.iobio.

Main Results:

• DNAscan analyzes whole exome data in 1 hour and 40x whole genome data in 13 hours on a midrange computer.
• The pipeline detects single nucleotide variants, small indels, structural variants, repeat expansions, and viral genetic material.
• Results are annotated and visualized locally, facilitating interpretation.

Conclusions:

• DNAscan offers extremely fast and computationally efficient analysis, visualization, and interpretation of NGS data.
• It serves as a powerful, easy-to-use tool for biomedical research and diagnostics at minimal computational cost.
• The pipeline broadens access to NGS data analysis for non-specialist labs and institutions with limited funding.