Evidence map›Paper›PMID 40410787›Full record

ArticleGenome biology2025

A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data.

Haichao Wang, Paulius D Mennea, Yu Kiu Elkie Chan, Zhao Cheng, Maria C Neofytou, Arif Anwer Surani, Aadhitthya Vijayaraghavan, Emma-Jane Ditter, Richard Bowers, Matthew D Eldridge and 7 more

Abstract read
In one paragraph

Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  12. Fragmentation signatures in cancer patients resemble those of patients with vascular or autoimmune diseases.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
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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

17 authors.

Haichao Wang *Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
Paulius D Mennea *Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
Yu Kiu Elkie Chan *LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Zhao Cheng *Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
Maria C NeofytouCancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
Arif Anwer SuraniCancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
Aadhitthya VijayaraghavanCancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
Emma-Jane DitterCancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
Richard BowersCancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
Matthew D EldridgeCancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
Dmitry S ShcherboCancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
Christopher G SmithCancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
Florian MarkowetzCancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
Wendy N CooperCancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
Tommy KaplanSchool of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
Nitzan RosenfeldCancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. n.rosenfeld@qmul.ac.uk.
Hui ZhaoCancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. hui.zhao@cruk.cam.ac.uk.

Funding

Cancer Research UK C1287/A26886Cancer Research UK C36857/A27548Cancer Research UK G101107European Research Council under the European Union's Seventh Framework Programme FP/2007-2013infrastructure grants within the CRUK City of London Major Centre Awards C7893/A26233 and CTRQQR-2021\100004Joint Royal College of Surgeons & Cancer Research UK C64667/A27958
6 · The paper itself

Abstract

Fragmentomics features of cell-free DNA represent promising non-invasive biomarkers for cancer diagnosis. A lack of systematic evaluation of biases in feature quantification hinders the adoption of such applications. We compare features derived from whole-genome sequencing of ten healthy donors using nine library kits and ten data-processing routes and validated in 1182 plasma samples from published studies. Our results clarify the variations from library preparation and feature quantification methods. We design the Trim Align Pipeline and cfDNAPro R package as unified interfaces for data pre-processing, feature extraction, and visualization to standardize multi-modal feature engineering and integration for machine learning.

Indexed as

Cell-Free Nucleic AcidsSequence Analysis, DNAHigh-Throughput Nucleotide SequencingHumansMachine LearningSoftwareWhole Genome SequencingCell-Free Nucleic AcidsCancer genomicsCfDNAFeature extractionFragmentomics

Identifiers

PMID40410787
PMCPMC12100915

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.