NanoPack2: population-scale evaluation of long-read sequencing data.

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Long-read sequencing projects are generating increasingly large datasets.
• Efficient quality assessment and data processing are crucial for handling these large cohorts.
• Existing software may not meet the demands of modern, large-scale sequencing studies.

Purpose of the Study:

• To introduce novel software tools for summarizing experiments, filtering datasets, and visualizing phased alignments.
• To update and improve the NanoPack software suite for enhanced performance.
• To provide efficient solutions for processing and assessing long-read sequencing data.

Main Methods:

• Development of new tools (cramino, chopper, kyber, phasius) in Rust, distributed as standalone binaries.
• Updates to existing Python 3 tools (NanoPlot, NanoComp) within the NanoPack suite.
• Ensuring broad compatibility across operating systems (Linux, macOS, Windows WSL) and ease of installation via conda.

Main Results:

• The new tools facilitate efficient summarization, filtering, and visualization of long-read sequencing data.
• NanoPack software suite has been updated with improved functionalities.
• The tools are readily available, require no complex installation, and are compatible with major operating systems.

Conclusions:

• The developed software provides efficient solutions for quality assessment and data processing in large-scale long-read sequencing projects.
• These tools will aid researchers in managing and analyzing complex genomic datasets.
• The accessibility and compatibility of the tools promote their widespread adoption in the research community.