Evidence map›Paper›PMID 33441414›Full record

ArticleGenome research2021

Accurate and efficient detection of gene fusions from RNA sequencing data.

Sebastian Uhrig, Julia Ellermann, Tatjana Walther, Pauline Burkhardt, Martina Fröhlich, Barbara Hutter, Umut H Toprak, Olaf Neumann, Albrecht Stenzinger, Claudia Scholl and 2 more

Abstract read
In one paragraph

Article in Genome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 326 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
326citing papers in PubMed, 4 pooled it
–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

326 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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266 more citing papers are in PubMed but not listed here.

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

12 authors.

Sebastian UhrigDivision of Applied Bioinformatics, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT) Heidelberg, 69120 Heidelberg, Germany.ORCID 0000-0002-0068-5957
Julia EllermannFaculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany.
Tatjana WaltherDivision of Translational Medical Oncology, NCT Heidelberg and DKFZ, 69120 Heidelberg, Germany.
Pauline BurkhardtDivision of Applied Bioinformatics, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT) Heidelberg, 69120 Heidelberg, Germany.
Martina FröhlichDivision of Applied Bioinformatics, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT) Heidelberg, 69120 Heidelberg, Germany.
Barbara HutterDivision of Applied Bioinformatics, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT) Heidelberg, 69120 Heidelberg, Germany.
Umut H ToprakGerman Cancer Consortium (DKTK), 69120 Heidelberg, Germany.
Olaf NeumannInstitute of Pathology, University Hospital Heidelberg, 69120 Heidelberg, Germany.
Albrecht StenzingerGerman Cancer Consortium (DKTK), 69120 Heidelberg, Germany.
Claudia SchollGerman Cancer Consortium (DKTK), 69120 Heidelberg, Germany.
Stefan FröhlingGerman Cancer Consortium (DKTK), 69120 Heidelberg, Germany.
Benedikt BrorsDivision of Applied Bioinformatics, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT) Heidelberg, 69120 Heidelberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The identification of gene fusions from RNA sequencing data is a routine task in cancer research and precision oncology. However, despite the availability of many computational tools, fusion detection remains challenging. Existing methods suffer from poor prediction accuracy and are computationally demanding. We developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime. When applied to a large collection of published pancreatic cancer samples (

Indexed as

Sequence Analysis, RNAGene FusionHumansOncogene Proteins, FusionPancreatic NeoplasmsPrecision MedicineProto-Oncogene ProteinsRNAOncogene Proteins, FusionProto-Oncogene ProteinsRNA

Identifiers

PMID33441414
PMCPMC7919457

What OpenQuestion holds

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LicenceCC BY
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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.