Evidence map›Paper›PMID 28530222›Full record

ArticleNature communications2017

Ultrasensitive and high-efficiency screen of de novo low-frequency mutations by o2n-seq.

Kaile Wang, Shujuan Lai, Xiaoxu Yang, Tianqi Zhu, Xuemei Lu, Chung-I Wu, Jue Ruan

Abstract read
In one paragraph

Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  11. Low-level parental mosaicism affects the recurrence risk of holoprosencephaly.Genetics in medicine : official journal of the American College of Medical Genetics · 2019
    Article
  12. Review
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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

7 authors.

Kaile WangAgricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Pengfei Road No. 7, Dapeng New District, Shenzhen, Guangdong 518120, China.
Shujuan LaiKey Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Chaoyang, Beijing 100101, China.
Xiaoxu YangCenter for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Haidian, Beijing 100871, China.ORCID 0000-0003-0219-0023
Tianqi ZhuInstitute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Haidian, Beijing 100190, China.
Xuemei LuKey Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Chaoyang, Beijing 100101, China.
Chung-I WuKey Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Chaoyang, Beijing 100101, China.
Jue RuanAgricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Pengfei Road No. 7, Dapeng New District, Shenzhen, Guangdong 518120, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detection of de novo, low-frequency mutations is essential for characterizing cancer genomes and heterogeneous cell populations. However, the screening capacity of current ultrasensitive NGS methods is inadequate owing to either low-efficiency read utilization or severe amplification bias. Here, we present o2n-seq, an ultrasensitive and high-efficiency NGS library preparation method for discovering de novo, low-frequency mutations. O2n-seq reduces the error rate of NGS to 10

Indexed as

DNA FragmentationDNA Mutational AnalysisDNA, NeoplasmGene FrequencyGene LibraryGenome, HumanHigh-Throughput Nucleotide SequencingHumansMutationNeoplasmsPolymerase Chain ReactionReproducibility of ResultsSensitivity and SpecificityDNA, Neoplasm

Identifiers

PMID28530222
PMCPMC5458117

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Registered trials

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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.