Evidence map›Paper›PMID 36778136›Full record

ArticleCell genomics2022

A multi-platform reference for somatic structural variation detection.

Jose Espejo Valle-Inclan, Nicolle J M Besselink, Ewart de Bruijn, Daniel L Cameron, Jana Ebler, Joachim Kutzera, Stef van Lieshout, Tobias Marschall, Marcel Nelen, Peter Priestley and 8 more

Abstract read
In one paragraph

Article in Cell genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

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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

18 authors.

Jose Espejo Valle-InclanCenter for Molecular Medicine and Oncode Institute, UMC Utrecht, Utrecht, the Netherlands.
Nicolle J M BesselinkCenter for Molecular Medicine and Oncode Institute, UMC Utrecht, Utrecht, the Netherlands.
Ewart de BruijnHartwig Medical Foundation, Amsterdam, the Netherlands.
Daniel L CameronHartwig Medical Foundation, Amsterdam, the Netherlands.
Jana EblerInstitute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Joachim KutzeraCenter for Molecular Medicine and Oncode Institute, UMC Utrecht, Utrecht, the Netherlands.
Stef van LieshoutHartwig Medical Foundation, Amsterdam, the Netherlands.
Tobias MarschallInstitute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Marcel NelenDepartment of Human Genetics, Radboud UMC, Nijmegen, the Netherlands.
Peter PriestleyHartwig Medical Foundation, Amsterdam, the Netherlands.
Ivo RenkensCenter for Molecular Medicine and Oncode Institute, UMC Utrecht, Utrecht, the Netherlands.
Margaretha G M RoemerDepartment of Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, the Netherlands.
Markus J van RoosmalenCenter for Molecular Medicine and Oncode Institute, UMC Utrecht, Utrecht, the Netherlands.
Aaron M WengerPacific Biosciences, Menlo Park, CA, USA.
Bauke YlstraDepartment of Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, the Netherlands.
Remond J A FijnemanDepartment of Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
Wigard P KloostermanCenter for Molecular Medicine and Oncode Institute, UMC Utrecht, Utrecht, the Netherlands.
Edwin CuppenCenter for Molecular Medicine and Oncode Institute, UMC Utrecht, Utrecht, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality, gold-standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines. Here, we performed somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different sequencing technologies. Based on the evidence from multiple technologies combined with extensive experimental validation, we compiled a comprehensive set of carefully curated and validated somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects. The truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.

Indexed as

benchmarkingcancerlong sequencing readshort sequencing readstructural varianttruth setwhole-genome sequencing

Identifiers

PMID36778136
PMCPMC9903816

What Socratic holds

Textmetadata
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 Socratic graph.