Evidence map›Paper›PMID 34991664›Full record

ArticleGenome biology2022

JAFFAL: detecting fusion genes with long-read transcriptome sequencing.

Nadia M Davidson, Ying Chen, Teresa Sadras, Georgina L Ryland, Piers Blombery, Paul G Ekert, Jonathan Göke, Alicia Oshlack

Abstract read
In one paragraph

Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing 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

56 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Characteristics of fusion genes in breast cancer.Cancer cell international · 2026
    Review
  6. Article
  7. Review
  8. Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases.Computational and structural biotechnology journal · 2026
    Review
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

8 authors.

Nadia M DavidsonPeter MacCallum Cancer Centre, Victoria, Australia. davidson.n@wehi.edu.au.
Ying ChenGenome Institute of Singapore, Singapore, Singapore.
Teresa SadrasPeter MacCallum Cancer Centre, Victoria, Australia.
Georgina L RylandPeter MacCallum Cancer Centre, Victoria, Australia.
Piers BlomberyPeter MacCallum Cancer Centre, Victoria, Australia.
Paul G EkertPeter MacCallum Cancer Centre, Victoria, Australia.
Jonathan GökeGenome Institute of Singapore, Singapore, Singapore.
Alicia OshlackPeter MacCallum Cancer Centre, Victoria, Australia. alicia.oshlack@petermac.org.ORCID 0000-0001-9788-5690

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In cancer, fusions are important diagnostic markers and targets for therapy. Long-read transcriptome sequencing allows the discovery of fusions with their full-length isoform structure. However, due to higher sequencing error rates, fusion finding algorithms designed for short reads do not work. Here we present JAFFAL, to identify fusions from long-read transcriptome sequencing. We validate JAFFAL using simulations, cell lines, and patient data from Nanopore and PacBio. We apply JAFFAL to single-cell data and find fusions spanning three genes demonstrating transcripts detected from complex rearrangements. JAFFAL is available at https://github.com/Oshlack/JAFFA/wiki .

Indexed as

High-Throughput Nucleotide SequencingTranscriptomeAlgorithmsGene FusionHumansSequence Analysis, DNAFusionsLong readsNanoporePacBioRNA sequencingTranslocations

Identifiers

PMID34991664
PMCPMC8739696

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.