Evidence map›Paper›PMID 34034652›Full record

ReviewBMC bioinformatics2021

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

Dimitra Sarantopoulou, Thomas G Brooks, Soumyashant Nayak, Antonijo Mrčela, Nicholas F Lahens, Gregory R Grant

Abstract readReview
In one paragraph

Review in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed.

  1. Review
  2. Article
  3. BATF3 regulates differentiation of CD8Life science alliance · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Perplexity as a Metric for Isoform Diversity in the Human Transcriptome.bioRxiv : the preprint server for biology · 2025
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Dimitra Sarantopoulou *Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
Thomas G Brooks *Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
Soumyashant NayakInstitute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
Antonijo MrčelaInstitute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
Nicholas F LahensInstitute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
Gregory R GrantInstitute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA. ggrant@pennmedicine.upenn.edu.ORCID http://orcid.org/0000-0002-0139-7658

Funding

Institutional Clinical and Translational Science AwardUL1TR000003 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2012 to 2015
$38.9M
High performance IBM iDataPlex/SONAS computing cluster for genomicsS10OD012312 · OD · UNIVERSITY OF PENNSYLVANIA · PI KIM, JUNHYONG · 2013 to 2013
$2.0M
NCATS NIH HHS 5UL1TR000003NCATS NIH HHS UL1 TR000003NIH HHS S10 OD012312
6 · The paper itself

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.

Indexed as

Gene Expression ProfilingTranscriptomeProtein IsoformsRNA-SeqSequence Analysis, RNAProtein IsoformsBenchmarkingIsoform quantificationPseudo-alignmentRNA-seqShort readsSimulated data

Identifiers

PMID34034652
PMCPMC8145802

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.