Evidence map›Paper›PMID 32631226›Full record

ArticleBMC bioinformatics2020

HPG-DHunter: an ultrafast, friendly tool for DMR detection and visualization.

Lisardo Fernández, Mariano Pérez, Ricardo Olanda, Juan M Orduña, Joan Marquez-Molins

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 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 37% 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, 12 citations in OpenAlex.

  1. Article
  2. fSuSiE enables fine-mapping of QTLs from genome-scale molecular profiles.bioRxiv : the preprint server for biology · 2025
    Article
  3. Ten Years of EWAS.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021
    Review
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

5 authors at 1 institution in 1 country.

Lisardo FernándezDepartamento de Informática, Universidad de Valencia, Avda. Universidad, s/n, Burjassot (Valencia), Spain.
Mariano PérezDepartamento de Informática, Universidad de Valencia, Avda. Universidad, s/n, Burjassot (Valencia), Spain.
Ricardo OlandaDepartamento de Informática, Universidad de Valencia, Avda. Universidad, s/n, Burjassot (Valencia), Spain.
Juan M OrduñaDepartamento de Informática, Universidad de Valencia, Avda. Universidad, s/n, Burjassot (Valencia), Spain.ORCID http://orcid.org/0000-0002-2932-0214
Joan Marquez-MolinsI2SysBio, CSIC-Universidad de Valencia, Cat. Agustín Escardino, Paterna (Valencia), Spain.
Universitat de València · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSoftware tools for analyzing DNA methylation do not provide graphical results which can be easily identified, but huge text files containing the alignment of the samples and their methylation status at a resolution of base pairs. There have been proposed different tools and methods for finding Differentially Methylated Regions (DMRs) among different samples, but the execution time required by these tools is large, and the visualization of their results is far from being interactive. Additionally, these methods show more accurate results when identifying simulated DM regions that are long and have small within-group variation, but they have low concordance when used with real datasets, probably due to the different approaches they use for DMR identification. Thus, a tool which automatically detects DMRs among different samples and interactively visualizes DMRs at different scales (from a bunch to ten of millions of DNA locations) can be the key for shortening the DNA methylation analysis process in many studies.

resultsIn this paper, we propose a software tool based on the wavelet transform. This mathematical tool allows the fast automatic DMR detection by simple comparison of different signals at different resolution levels. Also, it allows an interactive visualization of the DMRs found at different resolution levels. The tool is publicly available at https://grev-uv.github.io/ , and it is part of a complete suite of tools which allow to carry out the complete process of DNA alignment and methylation analysis, creation of methylation maps of the whole genome, and the detection and visualization of DMRs between different samples.

conclusionsThe validation of the developed software tool shows similar concordance with other well-known and extended tools when used with real and synthetic data. The batch mode of the tool is capable of automatically detecting the existing DMRs for half (twelve) of the human chromosomes between two sets of six samples (whose.csv files after the alignment and mapping procedures have an aggregated size of 108 Gigabytes) in around three hours and a half. When compared to other well-known tools, HPG-DHunter only requires around 15% of the execution time required by other tools for detecting the DMRs.

Indexed as

DNA MethylationHumansSequence Analysis, DNASoftwareDNA methylationGPU computingHigh performance computingWavelet transform

Identifiers

PMID32631226
PMCPMC7336418
OpenAlexW3038141998

What OpenQuestion holds

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LicenceCC BY
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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.