Evidence map›Paper›PMID 32451390›Full record

ArticleScientific reports2020

A Method for Identification of the Methylation Level of CpG Islands From NGS Data.

Leonid A Uroshlev, Eldar T Abdullaev, Iren R Umarova, Irina A Il'icheva, Larisa A Panchenko, Robert V Polozov, Fyodor A Kondrashov, Yury D Nechipurenko, Sergei L Grokhovsky

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In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 38% 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

3 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Advances in the role of GPX3 in ovarian cancer (Review).International journal of oncology · 2024
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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

9 authors at 5 institutions in 3 countries.

Leonid A UroshlevEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia. leoniduroshlev@gmail.com.
Eldar T AbdullaevMax Planck Institute for Molecular Genetics, Berlin, Germany.
Iren R UmarovaFaculty of Computational Mathematics and Cybernetics, Moscow State University, Moscow, Russia.
Irina A Il'ichevaEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.
Larisa A PanchenkoFaculty of Computational Mathematics and Cybernetics, Moscow State University, Moscow, Russia.
Robert V PolozovInstitute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, Puschino, Russia.
Fyodor A KondrashovInstitute of Science and Technology Austria, Klosterneuburg, Austria.
Yury D NechipurenkoEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia. yurii.nechiporenko@gmail.com.
Sergei L GrokhovskyEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.
Engelhardt Institute of Molecular Biology · RULomonosov Moscow State University · RUInstitute of Science and Technology Austria · ATInstitute of Theoretical and Experimental Biophysics · RUMax Planck Institute for Molecular Genetics · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the course of sample preparation for Next Generation Sequencing (NGS), DNA is fragmented by various methods. Fragmentation shows a persistent bias with regard to the cleavage rates of various dinucleotides. With the exception of CpG dinucleotides the previously described biases were consistent with results of the DNA cleavage in solution. Here we computed cleavage rates of all dinucleotides including the methylated CpG and unmethylated CpG dinucleotides using data of the Whole Genome Sequencing datasets of the 1000 Genomes project. We found that the cleavage rate of CpG is significantly higher for the methylated CpG dinucleotides. Using this information, we developed a classifier for distinguishing cancer and healthy tissues based on their CpG islands statuses of the fragmentation. A simple Support Vector Machine classifier based on this algorithm shows an accuracy of 84%. The proposed method allows the detection of epigenetic markers purely based on mechanochemical DNA fragmentation, which can be detected by a simple analysis of the NGS sequencing data.

Indexed as

DNA MethylationCell Line, TumorCpG IslandsDatabases, GeneticDNA FragmentationHigh-Throughput Nucleotide SequencingHumansMedulloblastomaNeoplasmsSequence Analysis, DNASupport Vector Machine

Identifiers

PMID32451390
PMCPMC7248081
OpenAlexW3026121854

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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.