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Making sense of RNA-Seq data: from low-level processing to functional analysis

Oleg V. Moskvin, Sean McIlwain, Irene M. Ong
doi: https://doi.org/10.1101/010488
Oleg V. Moskvin
1Great Lakes Bioenergy Research Center at University of Wisconsin-Madison, Madison, WI
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  • For correspondence: moskvin@wisc.edu
Sean McIlwain
1Great Lakes Bioenergy Research Center at University of Wisconsin-Madison, Madison, WI
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Irene M. Ong
1Great Lakes Bioenergy Research Center at University of Wisconsin-Madison, Madison, WI
2UW Carbone Cancer Center at University of Wisconsin-Madison, Madison, WI
3Department of Biostatistics and Medical Informatics at University of Wisconsin-Madison, Madison, WI
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Abstract

Numerous methods of RNA-Seq data analysis have been developed, and there are more under active development. In this paper, our focus is on evaluating the impact of each processing stage; from pre-processing of sequencing reads to alignment/counting to count normalization to differential expression testing to downstream functional analysis, on the inferred functional pattern of biological response. We assess the impact of 6,912 combinations of technical and biological factors on the resulting signature of transcriptomic functional response. Given the absence of the ground truth, we use two complementary evaluation criteria: a) consistency of the functional patterns identified in two similar comparisons, namely effects of a naturally-toxic medium and a medium with artificially reconstituted toxicity, and b) consistency of results in RNA-Seq and microarray versions of the same study. Our results show that despite high variability at the low-level processing stage (read pre-processing, alignment and counting) and the differential expression calling stage, their impact on the inferred pattern of biological response was surprisingly low; they were instead overshadowed by the choice of the functional enrichment method. The latter have an impact comparable in magnitude to the impact of biological factors per se.

  • Abbreviations

    DE
    differential expression
    ANOVA
    analysis of variance
    FDR
    false discovery rate
    FPKM
    fragments per kilobase of exon per million mapped fragments
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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. All rights reserved. No reuse allowed without permission.
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    Posted October 17, 2014.
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    Making sense of RNA-Seq data: from low-level processing to functional analysis
    Oleg V. Moskvin, Sean McIlwain, Irene M. Ong
    bioRxiv 010488; doi: https://doi.org/10.1101/010488
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    Making sense of RNA-Seq data: from low-level processing to functional analysis
    Oleg V. Moskvin, Sean McIlwain, Irene M. Ong
    bioRxiv 010488; doi: https://doi.org/10.1101/010488

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