RT Journal
A1 Tardaguila, Manuel
A1 de la Fuente, Lorena
A1 Marti, Cristina
A1 Pereira, Cécile
A1 Pardo-Palacios, Francisco Jose
A1 del Risco, Hector
A1 Ferrell, Marc
A1 Mellado, Maravillas
A1 Macchietto, Marissa
A1 Verheggen, Kenneth
A1 Edelmann, Mariola
A1 Ezkurdia, Iakes
A1 Vazquez, Jesus
A1 Tress, Michael
A1 Mortazavi, Ali
A1 Martens, Lennart
A1 Rodriguez-Navarro, Susana
A1 Moreno-Manzano, Victoria
A1 Conesa, Ana
T1 SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification
JF Genome Research 
JO Genome Research 
YR 2018 
FD March 01 
VO 28 
IS 3 
SP 396 
OP 411 
DO 10.1101/gr.222976.117 
UL http://genome.cshlp.org/content/28/3/396.abstract 
AB 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.