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SQANTI : extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification

(2018) GENOME RESEARCH. 28(3). p.396-411
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Abstract
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.
Keywords
TANDEM MASS-SPECTRA, RNA-SEQ, MESSENGER-RNA, ANNOTATION, ISOFORM, ACCURATE, GENE, PLURIPOTENCY, MECHANISMS, RESOLUTION

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Citation

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MLA
Tardaguila, Manuel et al. “SQANTI : Extensive Characterization of Long-read Transcript Sequences for Quality Control in Full-length Transcriptome Identification and Quantification.” GENOME RESEARCH 28.3 (2018): 396–411. Print.
APA
Tardaguila, M., de la Fuente, L., Marti, C., Pereira, C., Pardo-Palacios, F. J., del Risco, H., Ferrell, M., et al. (2018). SQANTI : extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification. GENOME RESEARCH, 28(3), 396–411.
Chicago author-date
Tardaguila, Manuel, Lorena de la Fuente, Cristina Marti, Cécile Pereira, Francisco Jose Pardo-Palacios, Hector del Risco, Marc Ferrell, et al. 2018. “SQANTI : Extensive Characterization of Long-read Transcript Sequences for Quality Control in Full-length Transcriptome Identification and Quantification.” Genome Research 28 (3): 396–411.
Chicago author-date (all authors)
Tardaguila, Manuel, Lorena de la Fuente, Cristina Marti, Cécile Pereira, Francisco Jose Pardo-Palacios, Hector del Risco, Marc Ferrell, Maravillas Mellado, Marissa Macchietto, Kenneth Verheggen, Mariola Edelmann, Iakes Ezkurdia, Jesus Vazquez, Michael Tress, Ali Mortazavi, Lennart Martens, Susana Rodriguez-Navarro, Victoria Moreno-Manzano, and Ana Conesa. 2018. “SQANTI : Extensive Characterization of Long-read Transcript Sequences for Quality Control in Full-length Transcriptome Identification and Quantification.” Genome Research 28 (3): 396–411.
Vancouver
1.
Tardaguila M, de la Fuente L, Marti C, Pereira C, Pardo-Palacios FJ, del Risco H, et al. SQANTI : extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification. GENOME RESEARCH. 2018;28(3):396–411.
IEEE
[1]
M. Tardaguila et al., “SQANTI : extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification,” GENOME RESEARCH, vol. 28, no. 3, pp. 396–411, 2018.
@article{8572751,
  abstract     = {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.},
  author       = {Tardaguila, Manuel and de la Fuente, Lorena and Marti, Cristina and Pereira, Cécile and Pardo-Palacios, Francisco Jose and del Risco, Hector and Ferrell, Marc and Mellado, Maravillas and Macchietto, Marissa and Verheggen, Kenneth and Edelmann, Mariola and Ezkurdia, Iakes and Vazquez, Jesus and Tress, Michael and Mortazavi, Ali and Martens, Lennart and Rodriguez-Navarro, Susana and Moreno-Manzano, Victoria and Conesa, Ana},
  issn         = {1088-9051},
  journal      = {GENOME RESEARCH},
  keywords     = {TANDEM MASS-SPECTRA,RNA-SEQ,MESSENGER-RNA,ANNOTATION,ISOFORM,ACCURATE,GENE,PLURIPOTENCY,MECHANISMS,RESOLUTION},
  language     = {eng},
  number       = {3},
  pages        = {396--411},
  title        = {SQANTI : extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification},
  url          = {http://dx.doi.org/10.1101/gr.222976.117},
  volume       = {28},
  year         = {2018},
}

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