Comparative assessment of long-read error correction software applied to Nanopore RNA-sequencing data

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Nanopore long-read sequencing offers advantages for RNA sequencing but suffers from high error rates.
• These errors complicate critical analyses like isoform identification and gene catalog creation.
• Limited computational tools exist for Nanopore RNA sequencing error correction.

Purpose of the Study:

• To evaluate the effectiveness of existing long-read DNA error correction methods on Nanopore cDNA reads.
• To develop a benchmarking tool for assessing error correction impacts on various genomic features.
• To provide guidelines for applying error correction to Nanopore RNA sequencing data.

Main Methods:

• Benchmarking existing long-read DNA error correction tools using Nanopore cDNA sequencing data.
• Utilizing an automated tool to assess error correction metrics.
• Evaluating the impact of correction on gene families, isoform diversity, and splice site detection.

Main Results:

• Long-read DNA error correction tools are suitable for improving base pair accuracy in Nanopore RNA sequencing data.
• The error correction process can inadvertently alter gene family sizes and isoform diversity.
• The study provides a comprehensive analysis of correction effects on various genomic metrics.

Conclusions:

• DNA error correction tools can enhance Nanopore RNA sequencing accuracy but require careful consideration of their effects.
• Users must weigh the benefits of increased accuracy against potential perturbations in biological data.
• Guidelines are provided to help researchers select appropriate error correction strategies based on their specific application needs.