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

  1. Lorena de la Fuente2,11,
  2. Cristina Marti2,
  3. Cécile Pereira1,
  4. Francisco Jose Pardo-Palacios2,
  5. Hector del Risco1,
  6. Marc Ferrell1,
  7. Maravillas Mellado3,
  8. Marissa Macchietto4,
  9. Kenneth Verheggen5,6,
  10. Mariola Edelmann1,
  11. Iakes Ezkurdia7,
  12. Jesus Vazquez7,
  13. Michael Tress8,
  14. Ali Mortazavi4,
  15. Lennart Martens5,6,
  16. Susana Rodriguez-Navarro9,10,
  17. Victoria Moreno-Manzano3 and
  18. Ana Conesa1,2
  19. 1Department of Microbiology and Cell Science, Institute for Food and Agricultural Sciences, Genetics Institute, University of Florida, Gainesville, Florida 32611, USA;
  20. 2Genomics of Gene Expression Laboratory, Centro de Investigaciones Principe Felipe (CIPF), 46012 Valencia, Spain;
  21. 3Neural Regeneration Laboratory, CIPF, 46012 Valencia, Spain;
  22. 4Department of Developmental and Cell Biology, University of California, Irvine, California 92617, USA;
  23. 5VIB-UGent Center for Medical Biotechnology, VIB, B-9000 Ghent, Belgium;
  24. 6Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium;
  25. 7Centro Nacional de Investigaciones Cardiovasculares CNIC, 28029 Madrid, Spain;
  26. 8Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain;
  27. 9Gene Expression and mRNA Metabolism Laboratory, CSIC, IBV, 46010 Valencia, Spain;
  28. 10Gene Expression and mRNA Metabolism Laboratory, CIPF, 46012 Valencia, Spain
  29. ↵11 Joint first authorship.

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.

Footnotes

This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.