Evidence map›Paper›PMID 40695274›Full record

ArticleCell reports methods2025

Combining panel-based and whole-transcriptome-based gene fusion detection by long-read sequencing.

Karleena Rybacki, Feng Xu, Hannah M Deutsch, Mian Umair Ahsan, Joe Chan, Zizhuo Liang, Yuanquan Song, Marilyn Li, Kai Wang

Abstract read
In one paragraph

Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Karleena RybackiDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Feng XuDivision of Genomic Diagnostics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Hannah M DeutschNeuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA.
Mian Umair AhsanRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Joe ChanRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Zizhuo LiangRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Yuanquan SongRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Marilyn LiDivision of Genomic Diagnostics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Kai WangDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: wangk@chop.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a comprehensive gene fusion (GF) detection and analysis workflow that combines targeted panel-based and whole-transcriptome long-read sequencing. We first adapted libraries from the short-read CHOP Cancer Fusion Panel, which targets 119 oncogenes commonly implicated in cancer fusions, for use on Oxford Nanopore Technologies' long-read sequencing platform. Long-read sequencing successfully detected known GFs in panel-positive samples, confirming compatibility, and enabled reduced turnaround times. To expand GF discovery in clinically challenging cases, we analyzed 24 glioma samples with negative short-read fusion panel results using whole-transcriptome long-read sequencing. This identified 20 candidate GFs in panel-negative samples that were absent from current fusion databases, all of which were experimentally validated. In summary, we introduce a computational workflow that combines panel-based and whole-transcriptome long-read sequencing with tailored analysis pipelines to enable fast and comprehensive GF detection in cancer.

Indexed as

Gene Expression ProfilingGene FusionHigh-Throughput Nucleotide SequencingTranscriptomeGliomaHumanscomputational pipelineCP: cancer biologyCP: geneticsgene fusionslong-read sequencingOxford Nanopore Technologiestranscriptome analysis

Identifiers

PMID40695274
PMCPMC12461587

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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.