NanoPrePro: a fully equipped, fast, and memory-efficient preprocessor for nanopore transcriptomic sequencing

Area of Science:

• Bioinformatics
• Genomics
• Molecular Biology

Background:

• Preprocessing Oxford Nanopore Technology (ONT) sequencing reads for transcriptomics is crucial but lacks systematic investigation.
• Current methods for ONT read preprocessing are ambiguous and require improvement for high precision.

Purpose of the Study:

• To develop a streamlined and highly precise read preprocessor for ONT transcriptomic data.
• To systematically investigate and improve the preprocessing of ONT sequencing reads.

Main Methods:

• Developed NanoPrePro, a novel bioinformatics tool for identifying full-length ONT reads.
• Utilized sequence similarity, adapter/primer location, and length for precise adapter/primer identification.
• Implemented a self-optimizing parameter extraction function for customized user analysis.

Main Results:

• NanoPrePro demonstrated superior performance compared to Pychopper on both simulated and real datasets.
• Achieved a 38-fold increase in speed and reduced memory consumption.
• Successfully identified full-length reads with high precision through accurate adapter/primer detection.

Conclusions:

• NanoPrePro represents a state-of-the-art solution for ONT read preprocessing.
• Offers enhanced speed, efficiency, and adaptability for ONT sequencing data analysis.
• Provides a robust and customizable tool for transcriptomic studies using ONT technology.