long-read-tools.org: an interactive catalogue of analysis methods for long-read sequencing data.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Long-read sequencing data possesses unique characteristics distinct from short-read data.
• Specialized analysis tools are necessary for various genomics applications, including quality control and downstream processing.
• The proliferation of software for long-read analysis poses challenges for tool selection and benchmarking.

Purpose of the Study:

• To introduce long-read-tools.org, an open-source database for organizing and exploring long-read data analysis tools.
• To provide a centralized, searchable resource for the genomics research community.

Main Methods:

• Implementation of an open-source database, long-read-tools.org.
• Interactive browsing and filtering functionalities for exploring the tool collection.
• Categorization of tools based on analysis tasks and programming languages.

Main Results:

• The database currently contains 478 tools across 32 categories.
• Python is the predominant development language for these tools.
• Common analysis tasks include base calling, de novo assembly, error correction, and quality filtering.
• Areas with fewer available tools include long-read single-cell data analysis and transcriptomics.

Conclusions:

• long-read-tools.org serves as a vital resource for researchers navigating the expanding landscape of long-read sequencing software.
• The database aids in selecting appropriate tools for specific genomics research needs.
• It supports researchers in staying updated with both established and emerging software solutions.