NGSpop: A desktop software that supports population studies by identifying sequence variations from next-generation...

Area of Science:

• Bioinformatics
• Genetics
• Computational Biology

Background:

• Next-generation sequencing (NGS) generates vast amounts of genetic data, necessitating efficient variant identification.
• Current variant calling processes are complex, repetitive, and require significant bioinformatics expertise, posing a challenge for many researchers.

Purpose of the Study:

• To develop a fully automated desktop software, NGSpop, to simplify and streamline the identification of sequence variations from large NGS datasets.
• To provide researchers, particularly those less familiar with bioinformatics, with an accessible tool for variant calling and visualization.

Main Methods:

• NGSpop integrates functionalities for quality control, mapping, filtering, and variant calling from NGS data.
• The software allows users to select between the GATK or DeepVariant algorithms for variant calling, with options for pre-set or customized pipelines.
• Implemented in JavaFX, NGSpop is compatible with Unix-like operating systems and supports batch processing for population-level studies.

Main Results:

• NGSpop offers an easy-to-use interface for rapid analysis of multiple NGS datasets, facilitating population studies.
• Benchmark tests indicate NGSpop reduces the carbon footprint of bioinformatics analysis by minimizing CPU heat and power consumption.
• The software enhances the flexibility and efficiency of using GATK and DeepVariant algorithms.

Conclusions:

• NGSpop provides an automated, user-friendly solution for sequence variant identification in NGS data, addressing a critical need in genetic research.
• The software supports efficient population-level studies and offers a more flexible and energy-efficient approach to variant calling.
• Future development may expand support for additional sequencing platforms and read formats.