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A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification

Dana Wyman, View ORCID ProfileGabriela Balderrama-Gutierrez, View ORCID ProfileFairlie Reese, Shan Jiang, Sorena Rahmanian, Weihua Zeng, Brian Williams, Diane Trout, Whitney England, Sophie Chu, Robert C. Spitale, Andrea Tenner, Barbara Wold, View ORCID ProfileAli Mortazavi
doi: https://doi.org/10.1101/672931
Dana Wyman
1University of California, Irvine, Department of Developmental and Cell Biology, Irvine, CA 92697, USA
2University of California, Irvine, Center for Complex Biological Systems, Irvine, CA 92697, USA
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Gabriela Balderrama-Gutierrez
1University of California, Irvine, Department of Developmental and Cell Biology, Irvine, CA 92697, USA
2University of California, Irvine, Center for Complex Biological Systems, Irvine, CA 92697, USA
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  • ORCID record for Gabriela Balderrama-Gutierrez
Fairlie Reese
1University of California, Irvine, Department of Developmental and Cell Biology, Irvine, CA 92697, USA
2University of California, Irvine, Center for Complex Biological Systems, Irvine, CA 92697, USA
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Shan Jiang
1University of California, Irvine, Department of Developmental and Cell Biology, Irvine, CA 92697, USA
2University of California, Irvine, Center for Complex Biological Systems, Irvine, CA 92697, USA
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Sorena Rahmanian
1University of California, Irvine, Department of Developmental and Cell Biology, Irvine, CA 92697, USA
2University of California, Irvine, Center for Complex Biological Systems, Irvine, CA 92697, USA
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Weihua Zeng
1University of California, Irvine, Department of Developmental and Cell Biology, Irvine, CA 92697, USA
2University of California, Irvine, Center for Complex Biological Systems, Irvine, CA 92697, USA
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Brian Williams
3California Institute of Technology, Division of Biology, Pasadena, CA 91125, USA
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Diane Trout
3California Institute of Technology, Division of Biology, Pasadena, CA 91125, USA
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Whitney England
4University of California, Irvine, Department of Pharmaceutical Sciences, Irvine, CA 92697, USA
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Sophie Chu
5University of California, Irvine, Department of Molecular Biology and Biochemistry, Irvine, CA 92697, USA
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Robert C. Spitale
4University of California, Irvine, Department of Pharmaceutical Sciences, Irvine, CA 92697, USA
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Andrea Tenner
5University of California, Irvine, Department of Molecular Biology and Biochemistry, Irvine, CA 92697, USA
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Barbara Wold
3California Institute of Technology, Division of Biology, Pasadena, CA 91125, USA
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Ali Mortazavi
1University of California, Irvine, Department of Developmental and Cell Biology, Irvine, CA 92697, USA
2University of California, Irvine, Center for Complex Biological Systems, Irvine, CA 92697, USA
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  • ORCID record for Ali Mortazavi
  • For correspondence: ali.mortazavi@uci.edu
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ABSTRACT

Alternative splicing is widely acknowledged to be a crucial regulator of gene expression and is a key contributor to both normal developmental processes and disease states. While cost-effective and accurate for quantification, short-read RNA-seq lacks the ability to resolve full-length transcript isoforms despite increasingly sophisticated computational methods. Long-read sequencing platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore (ONT) bypass the transcript reconstruction challenges of short-reads. Here we describe TALON, the ENCODE4 pipeline for analyzing PacBio cDNA and ONT direct-RNA transcriptomes. We apply TALON to three human ENCODE Tier 1 cell lines and show that while both technologies perform well at full-transcript discovery and quantification, each technology has its distinct artifacts. We further apply TALON to mouse cortical and hippocampal transcriptomes and find that a substantial proportion of neuronal genes have more reads associated with novel isoforms than annotated ones. The TALON pipeline for technology-agnostic, long-read transcriptome discovery and quantification tracks both known and novel transcript models as well as expression levels across datasets for both simple studies and larger projects such as ENCODE that seek to decode transcriptional regulation in the human and mouse genomes to predict more accurate expression levels of genes and transcripts than possible with short-reads alone.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted June 18, 2019.
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A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification
Dana Wyman, Gabriela Balderrama-Gutierrez, Fairlie Reese, Shan Jiang, Sorena Rahmanian, Weihua Zeng, Brian Williams, Diane Trout, Whitney England, Sophie Chu, Robert C. Spitale, Andrea Tenner, Barbara Wold, Ali Mortazavi
bioRxiv 672931; doi: https://doi.org/10.1101/672931
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A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification
Dana Wyman, Gabriela Balderrama-Gutierrez, Fairlie Reese, Shan Jiang, Sorena Rahmanian, Weihua Zeng, Brian Williams, Diane Trout, Whitney England, Sophie Chu, Robert C. Spitale, Andrea Tenner, Barbara Wold, Ali Mortazavi
bioRxiv 672931; doi: https://doi.org/10.1101/672931

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