The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification
Open accessAbstract
High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in well-annotated mammalian species. The advances in sequencing technology have created a need for studies and tools that can characterize these novel variants. Here, we present SQANTI, an automated pipeline for the classification of long-read transcripts that can assess the quality of data and the preprocessing pipeline using 47 unique descriptors. We apply SQANTI to a neuronal mouse transcriptome using Pacific Biosciences (PacBio) long reads and illustrate how the tool is effective in characterizing and describing the composition of the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, resulting more frequently in novel ORFs than novel UTRs, and are enriched in both general metabolic and neural-specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read-based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases, we find that alternative isoforms are elusive to proteogenomics detection. SQANTI allows the user to maximize the analytical outcome of long-read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Gene ontology analysis for RNA-seq: accounting for selection bias
Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-2-r14 · Cited by 7,107
Cell type- and brain region-resolved mouse brain proteome
Sharma, K., Schmitt, S., Bergner, C. G., Tyanova, S., Kannaiyan, N., Manrique-Hoyos, N., Kongi, K., Cantuti, L., Hanisch, U.-K., Philips, M.-A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4160 · Cited by 785
A single-molecule long-read survey of the human transcriptome
Sharon, D., Tilgner, H., Grubert, F., & Snyder, M. (2013) · Nature Biotechnology · doi:10.1038/nbt.2705 · Cited by 701
Assessment of transcript reconstruction methods for RNA-seq
The RGASP Consortium, Steijger, T., Abril, J. F., Engström, P. G., Kokocinski, F., Hubbard, T. J., Guigó, R., Harrow, J., & Bertone, P. (2013) · Nature Methods · doi:10.1038/nmeth.2714 · Cited by 699
A Highly Conserved Program of Neuronal Microexons Is Misregulated in Autistic Brains
Irimia, M., Weatheritt, R. J., Ellis, J. D., Parikshak, N. N., Gonatopoulos-Pournatzis, T., Babor, M., Quesnel-Vallières, M., Tapial, J., Raj, B., O’Hanlon, D., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.035 · Cited by 693
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Amarasinghe, S. L., Su, S., Dong, X., Zappia, L., Ritchie, M. E., & Gouil, Q. (2020) · Genome Biology · doi:10.1186/s13059-020-1935-5 · Cited by 2,530
Piercing the dark matter: bioinformatics of long-range sequencing and mapping
Sedlazeck, F. J., Lee, H., Darby, C. A., & Schatz, M. C. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0003-4 · Cited by 530
High throughput error corrected Nanopore single cell transcriptome sequencing
Lebrigand, K., Magnone, V., Barbry, P., & Waldmann, R. (2020) · Nature Communications · doi:10.1038/s41467-020-17800-6 · Cited by 236
Altered cell and RNA isoform diversity in aging Down syndrome brains
Palmer, C. R., Liu, C. S., Romanow, W. J., Lee, M.-H., & Chun, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2114326118 · Cited by 89
The annotation of GBA1 has been concealed by its protein-coding pseudogene GBAP1
Gustavsson, E. K., Sethi, S., Gao, Y., Brenton, J. W., García-Ruiz, S., Zhang, D., Garza, R., Reynolds, R. H., Evans, J. R., Chen, Z., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk1296 · Cited by 30
Arendt-Tranholm, A., Mwirigi, J. M., & Price, T. J. (2024) · Pain · doi:10.1097/j.pain.0000000000003255 · Cited by 10
Volkening, J. D., Spatz, S. J., Garcia, M., Ross, T. A., Maekawa, D. A., Rosenthal, K. S., Zamora, A. C., Skipper, A., Blakey, J. R., & Paudel, R. (2025) · PLOS Pathogens · doi:10.1371/journal.ppat.1013219 · Cited by 1
Metadata
- Authors
- Tardaguila, M., de la Fuente, L., Marti, C., Pereira, C., Pardo-Palacios, F. J., del Risco, H., Ferrell, M., Mellado, M., Macchietto, M., Verheggen, K., Edelmann, M., Ezkurdia, I., Vazquez, J., Tress, M., Mortazavi, A., Martens, L., Rodriguez-Navarro, S., Moreno-Manzano, V., & Conesa, A.
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2018-02-09
- Volume
- 28
- Issue
- 3
- Pages
- 396-411
- ISSN
- 1088-9051 (ISSN-L)
- 1549-5469
- OpenAlex
- W2952666313
- Cited by
- 454
- References
- 14
- Type
- posted content
- Language
- en
- Open access
- bronze
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