Evidence map›Paper›PMID 33542447›Full record

ArticleScientific reports2021

Adaptor Template Oligo-Mediated Sequencing (ATOM-Seq) is a new ultra-sensitive UMI-based NGS library preparation technology for use with cfDNA and cfRNA.

Thomas L Dunwell, Simon C Dailey, Anine L Ottestad, Jihang Yu, Philipp W Becker, Sarah Scaife, Susan D Richman, Henry M Wood, Hayley Slaney, Daniel Bottomley and 6 more

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. AccurateInternational journal of molecular sciences · 2025
    Article
  3. Review
  4. Review
  5. Article
  6. Rare molecular subtypes of lung cancer.Nature reviews. Clinical oncology · 2023
    Review
  7. 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

16 authors.

Thomas L DunwellGeneFirst Ltd, Building E5, Culham Science Centre, Abingdon, OX14 3DB, UK.
Simon C DaileyGeneFirst Ltd, Building E5, Culham Science Centre, Abingdon, OX14 3DB, UK.
Anine L OttestadDepartment of Oncology, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
Jihang YuGeneFirst Ltd, Building E5, Culham Science Centre, Abingdon, OX14 3DB, UK.
Philipp W BeckerGeneFirst Ltd, Building E5, Culham Science Centre, Abingdon, OX14 3DB, UK.
Sarah ScaifeGeneFirst Ltd, Building E5, Culham Science Centre, Abingdon, OX14 3DB, UK.
Susan D RichmanPathology and Data Analytics, Leeds Institute of Medical Research, University of Leeds, St James University Hospital, Leeds, LS9 7TF, UK.
Henry M WoodPathology and Data Analytics, Leeds Institute of Medical Research, University of Leeds, St James University Hospital, Leeds, LS9 7TF, UK.
Hayley SlaneyPathology and Data Analytics, Leeds Institute of Medical Research, University of Leeds, St James University Hospital, Leeds, LS9 7TF, UK.
Daniel BottomleyPathology and Data Analytics, Leeds Institute of Medical Research, University of Leeds, St James University Hospital, Leeds, LS9 7TF, UK.
Xiangsheng YangGuangzhou Biotron Technology Co., Ltd, Room 204, Zone C, Science and Technology Innovation Base, No. 80, Lanyue Road, Science City, Guangzhou, China.
Hui XiaoGuangzhou Biotron Technology Co., Ltd, Room 204, Zone C, Science and Technology Innovation Base, No. 80, Lanyue Road, Science City, Guangzhou, China.
Sissel G F WahlDepartment of Oncology, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
Bjørn H GrønbergDepartment of Oncology, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
Hongyan DaiDepartment of Oncology, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
Guoliang FuGeneFirst Ltd, Building E5, Culham Science Centre, Abingdon, OX14 3DB, UK. Guoliang.Fu@genefirst.com.

Funding

Department of Health NIHR200989Wellcome TrustWellcome Trust 20314/Z/16/Z
6 · The paper itself

Abstract

Liquid biopsy testing utilising Next Generation Sequencing (NGS) is rapidly moving towards clinical adoption for personalised oncology. However, before NGS can fulfil its potential any novel testing approach must identify ways of reducing errors, allowing separation of true low-frequency mutations from procedural artefacts, and be designed to improve upon current technologies. Popular NGS technologies typically utilise two DNA capture approaches; PCR and ligation, which have known limitations and seem to have reached a development plateau with only small, stepwise improvements being made. To maximise the ultimate utility of liquid biopsy testing we have developed a highly versatile approach to NGS: Adaptor Template Oligo Mediated Sequencing (ATOM-Seq). ATOM-Seq's strengths and versatility avoid the major limitations of both PCR- and ligation-based approaches. This technology is ligation free, simple, efficient, flexible, and streamlined, and it offers novel advantages that make it perfectly suited for use on highly challenging clinical material. Using reference and clinical materials, we demonstrate detection of known SNVs down to allele frequencies of 0.1% using as little as 20-25 ng of cfDNA, as well as the ability to detect fusions from RNA. We illustrate ATOM-Seq's suitability for clinical testing by showing high concordance rates between paired cfDNA and FFPE clinical samples.

Indexed as

AllelesBase SequenceCirculating Tumor DNAColonic NeoplasmsDNA PrimersGene FrequencyGene LibraryHigh-Throughput Nucleotide SequencingHumansLiquid BiopsyLung NeoplasmsPolymorphism, Single NucleotideRNA, NeoplasmSensitivity and SpecificityCirculating Tumor DNADNA PrimersRNA, Neoplasm

Identifiers

PMID33542447
PMCPMC7862664

What OpenQuestion holds

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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.