Evidence map›Paper›PMID 37930031›Full record

ArticleBriefings in bioinformatics2023

FLED: a full-length eccDNA detector for long-reads sequencing data.

Fuyu Li, Wenlong Ming, Wenxiang Lu, Ying Wang, Xiaohan Li, Xianjun Dong, Yunfei Bai

Open access · hybridAbstract read
In one paragraph

Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.3field-weighted citation impact, top 7% of its field
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

9 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Chromothripsis in cancer.Nature reviews. Cancer · 2025
    Review
  6. Article
  7. Review
  8. Extrachromosomal DNA in cancer.Nature reviews. Cancer · 2024
    Review
  9. Article
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

7 authors at 3 institutions in 2 countries.

Fuyu LiState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, P. R. China.
Wenlong MingInstitute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, P. R. China.
Wenxiang LuState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, P. R. China.
Ying WangState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, P. R. China.
Xiaohan LiState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, P. R. China.
Xianjun DongGenomics and Bioinformatics Hub, Brigham and Women's Hospital, Boston, MA 02115, USA.ORCID 0000-0002-8052-9320
Yunfei BaiState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, P. R. China.ORCID 0000-0002-4088-4117
State Key Laboratory of Digital Medical EngineeringBrigham and Women's Hospital · USNanjing University of Information Science and Technology · CN

Funding

Systematic Study of Extracellular Vesicles and their Integrative Analysis with Parkinson's Organoids MAPR01NS124916 · NINDS · BRIGHAM AND WOMEN'S HOSPITAL · PI Xianjun Dong, Luke P. Lee · 2022 to 2026
$3.7M
AI2AMP-PD: Accelerating Parkinsons Diagnosis using Multi-omics and Artificial IntelligenceU01NS120637 · NINDS · BRIGHAM AND WOMEN'S HOSPITAL · PI DONG, XIANJUN, SCHERZER, CLEMENS R · 2020 to 2020
$537k
NINDS NIH HHS R01 NS124916NINDS NIH HHS U01 NS120637
6 · The paper itself

Abstract

Reconstructing the full-length sequence of extrachromosomal circular DNA (eccDNA) from short sequencing reads has proved challenging given the similarity of eccDNAs and their corresponding linear DNAs. Previous sequencing methods were unable to achieve high-throughput detection of full-length eccDNAs. Herein, a novel algorithm was developed, called Full-Length eccDNA Detection (FLED), to reconstruct the sequence of eccDNAs based on the strategy that combined rolling circle amplification and nanopore long-reads sequencing technology. Seven human epithelial and cancer cell line samples were analyzed by FLED and over 5000 full-length eccDNAs were identified per sample. The structures of identified eccDNAs were validated by both Polymerase Chain Reaction (PCR) and Sanger sequencing. Compared to other published nanopore-based eccDNA detectors, FLED exhibited higher sensitivity. In cancer cell lines, the genes overlapped with eccDNA regions were enriched in cancer-related pathways and cis-regulatory elements can be predicted in the upstream or downstream of intact genes on eccDNA molecules, and the expressions of these cancer-related genes were dysregulated in tumor cell lines, indicating the regulatory potency of eccDNAs in biological processes. The proposed method takes advantage of nanopore long reads and enables unbiased reconstruction of full-length eccDNA sequences. FLED is implemented using Python3 which is freely available on GitHub (https://github.com/FuyuLi/FLED).

Indexed as

DNADNA, CircularCell LineHumansPolymerase Chain ReactionDNADNA, Circularextrachromosomal circular DNAfull-length detectionlong-read sequencing technology

Identifiers

PMID37930031
PMCPMC10632013
OpenAlexW4388423764

What OpenQuestion holds

Textmetadata
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.