HTSinfer: inferring metadata from bulk Illumina RNA-Seq libraries
Máté Balajti1,2, Rohan Kandhari1, Boris Jurič3
1Biozentrum, University of Basel, Basel 4056, Switzerland.
Bioinformatics (Oxford, England)
|February 19, 2025
Summary
HTSinfer is a new tool that automatically infers sequencing metadata from bulk RNA-sequencing data. This tool improves data accuracy and usability for the scientific community.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- The Sequencing Read Archive is a major repository for sequencing data, crucial for scientific research.
- Accurate metadata is essential for reusing sequencing data, but manual entry is error-prone and often incomplete.
- Existing tools for verifying metadata completeness and consistency are limited.
Purpose of the Study:
- To introduce HTSinfer, a Python-based tool for inferring metadata from bulk RNA-sequencing data.
- To address the limitations of manual metadata entry and improve data discoverability and reuse.
Main Methods:
- HTSinfer analyzes bulk RNA-sequencing data from Illumina platforms.
- It uses genome sequence information and diagnostic genes to infer library source, type, read orientation, adapter sequences, and read length statistics.
- The tool is modular and open-source to encourage community contributions.
Main Results:
- HTSinfer accurately infers critical metadata directly from sequencing data.
- It automates a previously manual and error-prone process, enhancing data reliability.
- The tool provides insights into library source, type, and technical parameters.
Conclusions:
- HTSinfer offers a robust solution for automated metadata inference in RNA-sequencing.
- By improving metadata quality, HTSinfer facilitates broader and more accurate data reuse.
- The open-source nature of HTSinfer promotes its adoption and further development within the scientific community.
