Evidence map›Paper›PMID 39946094›Full record

ArticleBioinformatics (Oxford, England)2025

Single-cell copy number calling and event history reconstruction.

Jack Kuipers, Mustafa Anıl Tuncel, Pedro F Ferreira, Katharina Jahn, Niko Beerenwinkel

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

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

38 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

5 authors.

Jack KuipersDepartment of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland.ORCID 0000-0001-5357-2705
Mustafa Anıl TuncelDepartment of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland.ORCID 0000-0003-0317-2556
Pedro F FerreiraDepartment of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland.ORCID 0000-0003-0559-6125
Katharina JahnDepartment of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland.
Niko BeerenwinkelDepartment of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland.ORCID 0000-0002-0573-6119

Funding

European Research Council Synergy 609883
6 · The paper itself

Abstract

motivationCopy number alterations are driving forces of tumour development and the emergence of intra-tumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations.

resultsWe developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to two breast cancer samples from different sequencing protocols. AVAILABILITY AND IMPLEMENTATION: SCICoNE is available at https://github.com/cbg-ethz/SCICoNE.

Indexed as

DNA Copy Number VariationsSingle-Cell AnalysisSoftwareAlgorithmsBreast NeoplasmsFemaleHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNA

Identifiers

PMID39946094
PMCPMC11897432

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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.