Restrander: rapid orientation and artefact removal for long-read cDNA data

Area of Science:

• Bioinformatics
• Genomics
• Molecular Biology

Background:

• Transcriptomic analysis requires accurate RNA molecule strandedness.
• Oxford Nanopore long-read cDNA sequencing protocols necessitate bioinformatic inference of transcript strandedness.
• Reverse transcription and PCR can introduce artifacts into sequencing data.

Purpose of the Study:

• Introduce Restrander, a tool for restranding and removing artifacts in long-read cDNA sequencing data.
• Improve the accuracy and speed of bioinformatic analysis for long-read RNA sequencing.
• Enhance the discovery of novel transcript isoforms and improve data quality control.

Main Methods:

• Developed Restrander using C++ for high performance.
• Implemented a strategy searching for polyA/T tails and primer sequences for restranding.
• Integrated artifact detection for reads lacking correct 5' and 3' ends.

Main Results:

• Restrander demonstrated faster performance compared to existing tools like Pychopper.
• Achieved higher accuracy in restranding reads by utilizing polyA/T tails and primer sequences.
• Improved visualization, data exploration, and increased novel isoform discovery, especially in complex genomic regions.

Conclusions:

• Restrander is an efficient and accurate tool for processing long-read cDNA sequencing data.
• The tool aids in quality control by quantifying sequencing artifacts.
• Restrander facilitates enhanced transcriptomic analysis and discovery of novel RNA isoforms.