Evidence map›Paper›PMID 38981681›Full record

ArticleGenome research2024

CoRAL accurately resolves extrachromosomal DNA genome structures with long-read sequencing.

Kaiyuan Zhu, Matthew G Jones, Jens Luebeck, Xinxin Bu, Hyerim Yi, King L Hung, Ivy Tsz-Lo Wong, Shu Zhang, Paul S Mischel, Howard Y Chang and 1 more

Abstract read
In one paragraph

Article in Genome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Targeting extrachromosomal DNA in human cancers.Nature reviews. Drug discovery · 2026
    Review
  12. Article
  13. Fast and accurate resolution of ecDNA sequence using Cycle-Extractor.bioRxiv : the preprint server for biology · 2026
    Article
  14. scAmp analyzes focal gene amplifications at single-cell resolution.bioRxiv : the preprint server for biology · 2026
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Kaiyuan Zhu *Department of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, USA.ORCID 0000-0001-9976-6505
Matthew G Jones *Center for Personal Dynamic Regulomes, Stanford University, Stanford, California 94305, USA.
Jens LuebeckDepartment of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, USA.
Xinxin BuBioinformatics Undergraduate Program, School of Biological Sciences, University of California San Diego, La Jolla, California 92093, USA.
Hyerim YiCenter for Personal Dynamic Regulomes, Stanford University, Stanford, California 94305, USA.
King L HungCenter for Personal Dynamic Regulomes, Stanford University, Stanford, California 94305, USA.ORCID 0000-0002-3662-4662
Ivy Tsz-Lo WongDepartment of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA.
Shu ZhangCenter for Personal Dynamic Regulomes, Stanford University, Stanford, California 94305, USA.
Paul S MischelDepartment of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA.
Howard Y ChangCenter for Personal Dynamic Regulomes, Stanford University, Stanford, California 94305, USA; vbafna@ucsd.edu howchang@stanford.edu.
Vineet BafnaDepartment of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, USA; vbafna@ucsd.edu howchang@stanford.edu.ORCID 0000-0002-5810-6241

Funding

eDyNAmiC - STANFORDOT2CA278688 · NCI · STANFORD UNIVERSITY · PI PAUL S MISCHEL · 2022 to 2026
$7.7M
Software and algorithms for elucidating the structure, function, and evolution of extrachromosomal DNAU24CA264379 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BAFNA, VINEET, MESIROV, JILL P. · 2021 to 2025
$3.5M
Computational methods for detecting patterns of complex genomic variationR01GM114362 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Vineet Bafna · 2016 to 2026
$3.1M
eDyNAmiC - UCSDOT2CA278635 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Vineet Bafna · 2022 to 2026
$1.8M
Quantitative modeling of extrachromosomal DNA (ecDNA) evolution in tumorsK99CA286968 · NCI · STANFORD UNIVERSITY · PI JONES, MATTHEW GREGORY · 2024 to 2025
$335k
Regulation and retention of extrachromosomal oncogene amplifications in cancerF99CA274692 · NCI · STANFORD UNIVERSITY · PI HUNG, KING L. · 2022 to 2023
$82k
NCI NIH HHS F99 CA274692NCI NIH HHS K99 CA286968NCI NIH HHS OT2 CA278635NCI NIH HHS OT2 CA278688NCI NIH HHS U24 CA264379NIGMS NIH HHS R01 GM114362
6 · The paper itself

Abstract

Extrachromosomal DNA (ecDNA) is a central mechanism for focal oncogene amplification in cancer, occurring in ∼15% of early-stage cancers and ∼30% of late-stage cancers. ecDNAs drive tumor formation, evolution, and drug resistance by dynamically modulating oncogene copy number and rewiring gene-regulatory networks. Elucidating the genomic architecture of ecDNA amplifications is critical for understanding tumor pathology and developing more effective therapies. Paired-end short-read (Illumina) sequencing and mapping have been utilized to represent ecDNA amplifications using a breakpoint graph, in which the inferred architecture of ecDNA is encoded as a cycle in the graph. Traversals of breakpoint graphs have been used to successfully predict ecDNA presence in cancer samples. However, short-read technologies are intrinsically limited in the identification of breakpoints, phasing together complex rearrangements and internal duplications, and deconvolution of cell-to-cell heterogeneity of ecDNA structures. Long-read technologies, such as from Oxford Nanopore Technologies, have the potential to improve inference as the longer reads are better at mapping structural variants and are more likely to span rearranged or duplicated regions. Here, we propose Complete Reconstruction of Amplifications with Long reads (CoRAL) for reconstructing ecDNA architectures using long-read data. CoRAL reconstructs likely cyclic architectures using quadratic programming that simultaneously optimizes parsimony of reconstruction, explained copy number, and consistency of long-read mapping. CoRAL substantially improves reconstructions in extensive simulations and 10 data sets from previously characterized cell lines compared with previous short- and long-read-based tools. As long-read usage becomes widespread, we anticipate that CoRAL will be a valuable tool for profiling the landscape and evolution of focal amplifications in tumors.

Indexed as

High-Throughput Nucleotide SequencingDNAGene AmplificationGenome, HumanHumansNeoplasmsSequence Analysis, DNASoftwareDNA

Identifiers

PMID38981681
PMCPMC11529860

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.