LongReadSum: A fast and flexible quality control and signal summarization tool for long-read sequencing data

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Existing quality control (QC) tools are insufficient for the diverse formats of long-read sequencing data.
• Long-read sequencing data (e.g., Oxford Nanopore, PacBio) includes varied information like raw signals, base modifications, and alignments.
• There is a need for high-performance computational tools to process large volumes of long-read sequencing data.

Purpose of the Study:

• To develop a high-performance computational tool for comprehensive QC of major long-read sequencing data formats.
• To provide a unified solution for analyzing diverse long-read data types, including POD5, FAST5, unaligned BAM, and FASTQ.

Main Methods:

• Introduction of LongReadSum, a novel software tool.
• Development of efficient algorithms to process various long-read sequencing file formats.
• Demonstration of LongReadSum's application on diverse sequencing data types.

Main Results:

• LongReadSum generates a summary QC report for multiple long-read sequencing data types.
• The tool efficiently processes large datasets, addressing performance requirements.
• Successful analysis examples include cDNA, direct RNA, methylation sequencing, and whole genome sequencing data.

Conclusions:

• LongReadSum is a valuable tool for comprehensive quality control of long-read sequencing data.
• The software enhances the analysis of diverse long-read data, supporting various applications.
• This tool addresses a critical gap in bioinformatics resources for long-read sequencing analysis.