Evidence map›Paper›PMID 24627790›Full record

ArticleF1000Research2013

MethylExtract: High-Quality methylation maps and SNV calling from whole genome bisulfite sequencing data.

Guillermo Barturen, Antonio Rueda, José L Oliver, Michael Hackenberg

Open access · greenAbstract read
In one paragraph

Article in F1000Research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
0.6field-weighted citation impact, top 30% 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

32 citing papers in PubMed, 26 citations in OpenAlex.

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  7. The lack of negative association between TE load and subgenome dominance in synthesizedProceedings of the National Academy of Sciences of the United States of America · 2023
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  11. 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

4 authors at 2 institutions in 1 country.

Guillermo BarturenDpto. de Genética, Facultad de Ciencias, Universidad de Granada, Granada, 18071, Spain ; Lab. de Bioinformática, Inst. de Biotecnología, Centro de Investigación Biomédica, Granada, 18016, Spain.
Antonio RuedaDpto. de Genética, Facultad de Ciencias, Universidad de Granada, Granada, 18071, Spain ; Lab. de Bioinformática, Inst. de Biotecnología, Centro de Investigación Biomédica, Granada, 18016, Spain.
José L OliverDpto. de Genética, Facultad de Ciencias, Universidad de Granada, Granada, 18071, Spain ; Lab. de Bioinformática, Inst. de Biotecnología, Centro de Investigación Biomédica, Granada, 18016, Spain.
Michael HackenbergDpto. de Genética, Facultad de Ciencias, Universidad de Granada, Granada, 18071, Spain ; Lab. de Bioinformática, Inst. de Biotecnología, Centro de Investigación Biomédica, Granada, 18016, Spain.
Centro de Investigación Biomédica en Red · ESUniversidad de Granada · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Whole genome methylation profiling at a single cytosine resolution is now feasible due to the advent of high-throughput sequencing techniques together with bisulfite treatment of the DNA. To obtain the methylation value of each individual cytosine, the bisulfite-treated sequence reads are first aligned to a reference genome, and then the profiling of the methylation levels is done from the alignments. A huge effort has been made to quickly and correctly align the reads and many different algorithms and programs to do this have been created. However, the second step is just as crucial and non-trivial, but much less attention has been paid to the final inference of the methylation states. Important error sources do exist, such as sequencing errors, bisulfite failure, clonal reads, and single nucleotide variants. We developed MethylExtract, a user friendly tool to: i) generate high quality, whole genome methylation maps and ii) detect sequence variation within the same sample preparation. The program is implemented into a single script and takes into account all major error sources. MethylExtract detects variation (SNVs - Single Nucleotide Variants) in a similar way to VarScan, a very sensitive method extensively used in SNV and genotype calling based on non-bisulfite-treated reads. The usefulness of MethylExtract is shown by means of extensive benchmarking based on artificial bisulfite-treated reads and a comparison to a recently published method, called Bis-SNP. MethylExtract is able to detect SNVs within High-Throughput Sequencing experiments of bisulfite treated DNA at the same time as it generates high quality methylation maps. This simultaneous detection of DNA methylation and sequence variation is crucial for many downstream analyses, for example when deciphering the impact of SNVs on differential methylation. An exclusive feature of MethylExtract, in comparison with existing software, is the possibility to assess the bisulfite failure in a statistical way. The source code, tutorial and artificial bisulfite datasets are available at http://bioinfo2.ugr.es/MethylExtract/ and http://sourceforge.net/projects/methylextract/, and also permanently accessible from 10.5281/zenodo.7144.

Identifiers

PMID24627790
PMCPMC3938178
OpenAlexW4237242764

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.