Evidence map›Paper›PMID 39955561›Full record

ArticleBMC research notes2025

Refined variant calling pipeline on RNA-seq data of breast cancer cell lines without matched-normal samples.

Sonja Eberth, Julia Koblitz, Laura Steenpaß, Claudia Pommerenke

Abstract read
In one paragraph

Article in BMC research notes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

3 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

4 authors.

Sonja EberthHuman and Animal Cell Lines, Leibniz-Institute DSMZ-DSMZ-German Collection of Microorganisms and Cell Cultures GmbH, Inhoffenstraße 7B, 38124, Braunschweig, Germany.
Julia KoblitzBioinformatics, IT and Databases, Leibniz-Institute DSMZ-DSMZ-German Collection of Microorganisms and Cell Cultures GmbH, Inhoffenstraße 7B, 38124, Braunschweig, Germany.
Laura SteenpaßHuman and Animal Cell Lines, Leibniz-Institute DSMZ-DSMZ-German Collection of Microorganisms and Cell Cultures GmbH, Inhoffenstraße 7B, 38124, Braunschweig, Germany.
Claudia PommerenkeBioinformatics, IT and Databases, Leibniz-Institute DSMZ-DSMZ-German Collection of Microorganisms and Cell Cultures GmbH, Inhoffenstraße 7B, 38124, Braunschweig, Germany. claudia.pommerenke@dsmz.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveRNA-seq delivers valuable insights both to transcriptional patterns and mutational landscapes for transcribed genes. However, as tumour cell lines frequently lack their matched-normal counterpart, variant calling without the paired normal sample is still challenging. In order to exclude variants of common genetic variation without a matched-normal control, filtering strategies need to be developed to identify tumour relevant variants in cell lines.

resultsHere, variants of 29 breast cancer cell lines were called on RNA-seq data via HaplotypeCaller. Low read depth sites, RNA-edit sites, and low complexity regions in coding regions were excluded. Common variants were filtered using 1000 genomes, gnomAD, and dbSNP data. Starting from hundred thousands of single nucleotide variants and small insertions and deletions, about thousand variants remained after filtering for each sample. Extracted variants were validated against the Catalogue of Somatic Mutations in Cancer (COSMIC) for 10 cell lines included in both data sets. Approximately half of the COSMIC variants were successfully called. Importantly, missing variants could mainly be attributed to sites with low read depth. Moreover, filtered variants also included all 10 cancer gene census COSMIC variants, a condensed hallmark variant set.

Indexed as

Breast NeoplasmsGenetic VariationRNA-SeqSequence Analysis, RNACell Line, TumorFemaleHumansMutationPolymorphism, Single NucleotideBreast cancerCancer cell linesCOSMICDSMZCellDiveRNA-seqVariant calling

Identifiers

PMID39955561
PMCPMC11829467

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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.