NGSNGS: next-generation simulator for next-generation sequencing data

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• The increasing volume of DNA sequencing data necessitates efficient bioinformatics tools.
• In silico data are vital for validating genomic analysis pipelines and models.
• Existing sequencing simulators may lack speed or specific features for diverse applications.

Purpose of the Study:

• To introduce NGSNGS, a novel multithreaded simulator for next-generation sequencing data.
• To enhance the speed and capabilities of in silico genomic data generation.
• To provide a flexible tool for simulating various genomic contexts, including ancient DNA.

Main Methods:

• Development of a multithreaded simulation framework for rapid data generation.
• Incorporation of platform-specific nucleotide quality score profiles.
• Implementation of a post-mortem damage model for ancient DNA simulation.
• Sampling from reference genomes, including polyploid assemblies and population haplotypes.
• Direct simulation of known variable sites.

Main Results:

• NGSNGS achieves significantly faster read simulation speeds compared to current methods.
• The simulator accurately models platform-specific sequencing characteristics.
• The inclusion of a damage model enables realistic simulation of ancient DNA data.
• The software supports diverse genomic inputs and allows direct simulation of variations.

Conclusions:

• NGSNGS offers a faster and more versatile solution for generating simulated next-generation sequencing data.
• The tool is valuable for validating genomic analyses and exploring diverse sequencing scenarios.
• The open-source availability promotes its adoption and further development in the bioinformatics community.