Evidence map›Paper›PMID 35164688›Full record

ArticleBMC genomics2022

Genion, an accurate tool to detect gene fusion from long transcriptomics reads.

Fatih Karaoglanoglu, Cedric Chauve, Faraz Hach

Abstract read
In one paragraph

Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Review
  2. Characteristics of fusion genes in breast cancer.Cancer cell international · 2026
    Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Long-read RNA sequencing: A transformative technology for exploring transcriptome complexity in human diseases.Molecular therapy : the journal of the American Society of Gene Therapy · 2025
    Review
  8. Article
  9. Article
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  11. 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

3 authors.

Fatih KaraoglanogluSchool of Computing Science, Simon Fraser University, Burnaby, BC, Canada.
Cedric ChauveDepartment of Mathematics, Simon Fraser University, Burnaby, BC, Canada. cedric.chauve@sfu.ca.
Faraz HachVancouver Prostate Centre, Vancouver, BC, Canada. faraz.hach@ubc.ca.ORCID http://orcid.org/0000-0003-1143-0172

Funding

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada RGPIN-03986Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada RGPIN-05952Michael Smith Foundation for Health Research SCH-2020-0370
6 · The paper itself

Abstract

backgroundThe advent of next-generation sequencing technologies empowered a wide variety of transcriptomics studies. A widely studied topic is gene fusion which is observed in many cancer types and suspected of having oncogenic properties. Gene fusions are the result of structural genomic events that bring two genes closely located and result in a fused transcript. This is different from fusion transcripts created during or after the transcription process. These chimeric transcripts are also known as read-through and trans-splicing transcripts. Gene fusion discovery with short reads is a well-studied problem, and many methods have been developed. But the sensitivity of these methods is limited by the technology, especially the short read length. Advances in long-read sequencing technologies allow the generation of long transcriptomics reads at a low cost. Transcriptomic long-read sequencing presents unique opportunities to overcome the shortcomings of short-read technologies for gene fusion detection while introducing new challenges.

resultsWe present Genion, a sensitive and fast gene fusion detection method that can also detect read-through events. We compare Genion against a recently introduced long-read gene fusion discovery method, LongGF, both on simulated and real datasets. On simulated data, Genion accurately identifies the gene fusions and its clustering accuracy for detecting fusion reads is better than LongGF. Furthermore, our results on the breast cancer cell line MCF-7 show that Genion correctly identifies all the experimentally validated gene fusions.

conclusionsGenion is an accurate gene fusion caller. Genion is implemented in C++ and is available at https://github.com/vpc-ccg/genion .

Indexed as

SoftwareTranscriptomeGene FusionGenomicsHigh-Throughput Nucleotide SequencingDynamic programmingGene fusion detectionLong-read sequencingTranscriptomics

Identifiers

PMID35164688
PMCPMC8842519

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LicenceCC BY
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Registered trials

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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.