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Isolator: accurate and stable analysis of isoform-level expression in RNA-Seq experiments

Daniel C. Jones, Kavitha T. Kuppusamy, Nathan J. Palpant, Xinxia Peng, Charles E. Murry, Hannele Ruohola-Baker, Walter L. Ruzzo
doi: https://doi.org/10.1101/088765
Daniel C. Jones
1Department of Computer Science and Engineering, University of Washington
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Kavitha T. Kuppusamy
2Institute for Stem Cell and Regenerative Medicine
6Department of Biochemistry, University of Washington
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Nathan J. Palpant
3Institute for Molecular Bioscience, The University of Queensland
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Xinxia Peng
4Department of Microbiology, University of Washington
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Charles E. Murry
2Institute for Stem Cell and Regenerative Medicine
5Department of Pathology, University of Washington
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Hannele Ruohola-Baker
2Institute for Stem Cell and Regenerative Medicine
6Department of Biochemistry, University of Washington
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Walter L. Ruzzo
1Department of Computer Science and Engineering, University of Washington
7Department of Genome Sciences, University of Washington
8Fred Hutchinson Cancer Research Center
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Abstract

While RNA-Seq has enabled great progress towards the goal of wide-scale isoform-level mRNA quantification, short reads have limitations when resolving complex or similar sets of isoforms. As a result, estimates of isoform abundance carry far more uncertainty than those made at the gene level. When confronted with this uncertainty, commonly used methods produce estimates that are often high-variance—small perturbations in the data often produce dramatically different results, confounding downstream analysis. We introduce a new method, Isolator, which analyzes all samples in an experiment in unison using a simple Bayesian hierarchical model. Combined with aggressive bias correction, it produces estimates that are simultaneously accurate and show high agreement between samples. In a comprehensive comparison of accuracy and variance, we show that this property is unique to Isolator. We further demonstrate that the approach of modeling an entire experiment enables new analyses, which we demonstrate by examining splicing monotonicity across several time points in the development of human cardiomyocyte cells.

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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 4.0 International license.
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Posted November 20, 2016.
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Isolator: accurate and stable analysis of isoform-level expression in RNA-Seq experiments
Daniel C. Jones, Kavitha T. Kuppusamy, Nathan J. Palpant, Xinxia Peng, Charles E. Murry, Hannele Ruohola-Baker, Walter L. Ruzzo
bioRxiv 088765; doi: https://doi.org/10.1101/088765
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Isolator: accurate and stable analysis of isoform-level expression in RNA-Seq experiments
Daniel C. Jones, Kavitha T. Kuppusamy, Nathan J. Palpant, Xinxia Peng, Charles E. Murry, Hannele Ruohola-Baker, Walter L. Ruzzo
bioRxiv 088765; doi: https://doi.org/10.1101/088765

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