Sequali: efficient and comprehensive quality control of short- and long-read sequencing data

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Quality control is essential for sequencing data analysis.
• Existing tools inadequately support both short-read and long-read sequencing technologies.
• Specific features like adapter searching are missing in some tools for Oxford Nanopore Technologies data.

Purpose of the Study:

• To develop a unified quality control tool for both short- and long-read sequencing data.
• To address limitations in current sequencing quality control software.
• To provide essential features such as adapter searching and duplication analysis.

Main Methods:

• Developed Sequali, an open-source Python application with C extensions.
• Implemented adapter search, overrepresented sequence analysis, and duplication analysis.
• Ensured support for FASTQ and uBAM input file formats.

Main Results:

• Sequali provides comprehensive quality control features for diverse sequencing technologies.
• The application demonstrates significantly faster performance compared to existing tools.
• Supports both short-read and long-read sequencing data analysis.

Conclusions:

• Sequali offers a robust and efficient solution for sequencing data quality control.
• The tool enhances workflows by integrating essential features for both short- and long-read data.
• Sequali is freely available, promoting accessibility in bioinformatics research.