Quality assessment of long read data in multisample lrRNA-seq experiments using SQANTI-reads
- 1Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, Florida 32610, USA;
- 2University of Florida Genetics Institute, University of Florida, Gainesville, Florida 32610, USA;
- 3Institute for Integrative Systems Biology (I2SysBio), Spanish National Research Council (CSIC), Paterna 46980, Spain;
- 4UF Health Cancer Center, University of Florida, Gainesville, Florida 32610, USA
Abstract
SQANTI-reads leverages SQANTI3, a tool for the analysis of the quality of transcript models, to develop a read-level quality control framework for replicated long-read RNA-seq experiments. The number and distribution of reads, as well as the number and distribution of unique junction chains (transcript splicing patterns), in SQANTI3 structural categories are informative of raw data quality. Multisample visualizations of QC metrics are presented by experimental design factors to identify outliers. We introduce new metrics for (1) the identification of potentially under-annotated genes and putative novel transcripts and for (2) quantifying variation in junction donors and acceptors. We applied SQANTI-reads to two different data sets, a Drosophila developmental experiment and a multiplatform data set from the LRGASP project and demonstrate that the tool effectively reveals the impact of read coverage on data quality, and readily identifies strong and weak splicing sites.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.280021.124.
- Received September 12, 2024.
- Accepted February 1, 2025.
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