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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Characterization of the human ESC transcriptome by hybrid sequencing
Au, K. F., Sebastiano, V., Afshar, P. T., Durruthy, J. D., Lee, L., Williams, B. A., van Bakel, H., Schadt, E. E., Reijo-Pera, R. A., Underwood, J. G., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320101110 · Cited by 341
Improving PacBio Long Read Accuracy by Short Read Alignment
Au, K. F., Underwood, J. G., Lee, L., & Wong, W. H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0046679 · Cited by 335
Cytoplasmic Rbfox1 Regulates the Expression of Synaptic and Autism-Related Genes
Lee, J.-A., Damianov, A., Lin, C.-H., Fontes, M., Parikshak, N. N., Anderson, E. S., Geschwind, D. H., Black, D. L., & Martin, K. C. (2016) · Neuron · doi:10.1016/j.neuron.2015.11.025 · Cited by 232
The ribosome-engaged landscape of alternative splicing
Weatheritt, R. J., Sterne-Weiler, T., & Blencowe, B. J. (2016) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3317 · Cited by 162
Cited by
Opportunities and challenges in long-read sequencing data analysis
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
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.