SQANTI-SIM: a simulator of controlled transcript novelty for lrRNA-seq benchmark

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Long-read RNA sequencing (lrRNA-seq) is crucial for discovering novel transcripts, even in well-annotated genomes.
• Evaluating the accuracy of lrRNA-seq methods for identifying known and new transcripts is challenging.
• Existing simulation tools lack precise control over transcript novelty based on structural categories.

Purpose of the Study:

• To introduce SQANTI-SIM, a novel simulation tool for lrRNA-seq data.
• To enable precise management of transcript novelty using SQANTI3 structural categories.
• To facilitate the assessment of transcript discovery accuracy in lrRNA-seq studies.

Main Methods:

• SQANTI-SIM integrates with existing long-read simulators.
• It allows selective exclusion of reference transcripts to emulate unannotated scenarios.
• The tool supports customizable simulation parameters and multi-omics data generation.

Main Results:

• SQANTI-SIM provides a standardized method for simulating lrRNA-seq data with controlled transcript novelty.
• It enables researchers to benchmark different lrRNA-seq analysis pipelines.
• The tool facilitates the study of novel transcript identification accuracy.

Conclusions:

• SQANTI-SIM is the first multi-omics simulation tool specifically designed for the lrRNA-seq field.
• It addresses a critical need for accurate benchmarking of transcript discovery methods.
• The tool will advance the reliability and accuracy of lrRNA-seq-based transcriptomics research.