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

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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.

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              Random Forests

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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                March 2018
                March 2018
                : 28
                : 3
                : 396-411
                Affiliations
                [1 ]Department of Microbiology and Cell Science, Institute for Food and Agricultural Sciences, Genetics Institute, University of Florida, Gainesville, Florida 32611, USA;
                [2 ]Genomics of Gene Expression Laboratory, Centro de Investigaciones Principe Felipe (CIPF), 46012 Valencia, Spain;
                [3 ]Neural Regeneration Laboratory, CIPF, 46012 Valencia, Spain;
                [4 ]Department of Developmental and Cell Biology, University of California, Irvine, California 92617, USA;
                [5 ]VIB-UGent Center for Medical Biotechnology, VIB, B-9000 Ghent, Belgium;
                [6 ]Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium;
                [7 ]Centro Nacional de Investigaciones Cardiovasculares CNIC, 28029 Madrid, Spain;
                [8 ]Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain;
                [9 ]Gene Expression and mRNA Metabolism Laboratory, CSIC, IBV, 46010 Valencia, Spain;
                [10 ]Gene Expression and mRNA Metabolism Laboratory, CIPF, 46012 Valencia, Spain
                Author notes
                [11]

                Joint first authorship.

                Article
                9509184
                10.1101/gr.222976.117
                5848618
                29440222
                e59427cc-b511-40de-9b6d-ea5e441e86ce
                © 2018 Tardaguila et al.; Published by Cold Spring Harbor Laboratory Press

                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/.

                History
                : 17 March 2017
                : 8 January 2018
                Page count
                Pages: 16
                Funding
                Funded by: University of Florida , open-funder-registry 10.13039/100007698;
                Funded by: Spanish Ministry of Economy and Competitiveness
                Award ID: BIO2015-71658-R
                Funded by: Spanish Ministry of Education
                Award ID: FPU2013/02348
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                Method

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