Long-read RNA sequencing has emerged as a powerful tool for transcript discovery, even in well-annotated organisms. However, assessing the accuracy of different methods in identifying annotated and novel transcripts remains a challenge. Here, we present SQANTI-SIM, a versatile tool that wraps around popular long-read simulators to allow precise management of transcript novelty based on the structural categories defined by SQANTI3. By selectively excluding specific transcripts from the reference dataset, SQANTI-SIM effectively emulates scenarios involving unannotated transcripts. Furthermore, the tool provides customizable features and supports the simulation of additional types of data, representing the first multi-omics simulation tool for the lrRNA-seq field.
CITATION STYLE
Mestre-Tomás, J., Liu, T., Pardo-Palacios, F., & Conesa, A. (2023). SQANTI-SIM: a simulator of controlled transcript novelty for lrRNA-seq benchmark. Genome Biology, 24(1). https://doi.org/10.1186/s13059-023-03127-0
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