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Accounting for fragments of unexpected origin improves transcript quantification in RNA-seq simulations focused on increased realism

View ORCID ProfileAvi Srivastava, View ORCID ProfileMohsen Zakeri, View ORCID ProfileHirak Sarkar, View ORCID ProfileCharlotte Soneson, View ORCID ProfileCarl Kingsford, View ORCID ProfileRob Patro
doi: https://doi.org/10.1101/2021.01.17.426996
Avi Srivastava
1New York Genome Center and NYU Center for Genomics and Systems Biology, New York City, NY, USA
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Mohsen Zakeri
2Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
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Hirak Sarkar
3Harvard Medical School, Boston, Massachusetts, USA
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Charlotte Soneson
4Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
5SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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Carl Kingsford
6Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
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  • For correspondence: carlk@cs.cmu.edu rob@cs.umd.edu
Rob Patro
2Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
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  • For correspondence: carlk@cs.cmu.edu rob@cs.umd.edu
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Abstract

Transcript and gene quantification is the first step in many RNA-seq analyses. While many factors and properties of experimental RNA-seq data likely contribute to differences in accuracy between various approaches to quantification, it has been demonstrated (1) that quantification accuracy generally benefits from considering, during alignment, potential genomic origins for sequenced fragments that reside outside of the annotated transcriptome.

Recently, Varabyou et al. (2) demonstrated that the presence of transcriptional noise leads to systematic errors in the ability of tools — particularly annotation-based ones — to accurately estimate transcript expression. Here, we confirm the findings of Varabyou et al. (2) using the simulation framework they have provided. Using the same data, we also examine the methodology of Srivastava et al.(1) as implemented in recent versions of salmon (3), and show that it substantially enhances the accuracy of annotation-based transcript quantification in these data.

Competing Interest Statement

CK and RP are co-founders of Ocean Genomics Inc.

Footnotes

  • https://github.com/COMBINE-lab/quant-tx-diversity

Copyright 
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-NC-ND 4.0 International license.
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Posted January 19, 2021.
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Accounting for fragments of unexpected origin improves transcript quantification in RNA-seq simulations focused on increased realism
Avi Srivastava, Mohsen Zakeri, Hirak Sarkar, Charlotte Soneson, Carl Kingsford, Rob Patro
bioRxiv 2021.01.17.426996; doi: https://doi.org/10.1101/2021.01.17.426996
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Accounting for fragments of unexpected origin improves transcript quantification in RNA-seq simulations focused on increased realism
Avi Srivastava, Mohsen Zakeri, Hirak Sarkar, Charlotte Soneson, Carl Kingsford, Rob Patro
bioRxiv 2021.01.17.426996; doi: https://doi.org/10.1101/2021.01.17.426996

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