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Comparative evaluation of full-length isoform quantification from RNA-Seq

Authors
  • Sarantopoulou, Dimitra1, 2
  • Brooks, Thomas G.1
  • Nayak, Soumyashant1
  • Mrčela, Antonijo1
  • Lahens, Nicholas F.1
  • Grant, Gregory R.1, 1
  • 1 University of Pennsylvania, Philadelphia, PA, USA , Philadelphia (United States)
  • 2 National Institutes of Health, Baltimore, MD, USA , Baltimore (United States)
Type
Published Article
Journal
BMC Bioinformatics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
May 25, 2021
Volume
22
Issue
1
Identifiers
DOI: 10.1186/s12859-021-04198-1
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundFull-length isoform quantification from RNA-Seq is a key goal in transcriptomics analyses and has been an area of active development since the beginning. The fundamental difficulty stems from the fact that RNA transcripts are long, while RNA-Seq reads are short.ResultsHere we use simulated benchmarking data that reflects many properties of real data, including polymorphisms, intron signal and non-uniform coverage, allowing for systematic comparative analyses of isoform quantification accuracy and its impact on differential expression analysis. Genome, transcriptome and pseudo alignment-based methods are included; and a simple approach is included as a baseline control.ConclusionsSalmon, kallisto, RSEM, and Cufflinks exhibit the highest accuracy on idealized data, while on more realistic data they do not perform dramatically better than the simple approach. We determine the structural parameters with the greatest impact on quantification accuracy to be length and sequence compression complexity and not so much the number of isoforms. The effect of incomplete annotation on performance is also investigated. Overall, the tested methods show sufficient divergence from the truth to suggest that full-length isoform quantification and isoform level DE should still be employed selectively.

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