Evidence map›Paper›PMID 27884103›Full record

ArticleBMC bioinformatics2016

ampliMethProfiler: a pipeline for the analysis of CpG methylation profiles of targeted deep bisulfite sequenced amplicons.

Giovanni Scala, Ornella Affinito, Domenico Palumbo, Ermanno Florio, Antonella Monticelli, Gennaro Miele, Lorenzo Chiariotti, Sergio Cocozza

Abstract read
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
  2. Detection of Brain-Derived Cell-Free DNA in Plasma.Diagnostics (Basel, Switzerland) · 2024
    Article
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  7. Article
  8. A novel workflow for the qualitative analysis of DNA methylation data.Computational and structural biotechnology journal · 2022
    Article
  9. Article
  10. Article
  11. Ten Years of EWAS.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021
    Review
  12. Article
  13. Review
  14. DNA Methylation Profiles ofBiomolecules · 2021
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. TheCancers · 2019
    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

8 authors.

Giovanni ScalaIstituto Nazionale di Fisica Nucleare, Sezione di Napoli, Naples, Italy. gscala@na.infn.it.ORCID http://orcid.org/0000-0002-9619-9426
Ornella AffinitoDipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli "Federico II", Naples, Italy.
Domenico PalumboDipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli "Federico II", Naples, Italy.
Ermanno FlorioDipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli "Federico II", Naples, Italy.
Antonella MonticelliIstituto di Endocrinologia ed Oncologia Sperimentale (IEOS) "Gaetano Salvatore", Consiglio Nazionale delle Ricerche CNR, Naples, Italy.
Gennaro MieleIstituto Nazionale di Fisica Nucleare, Sezione di Napoli, Naples, Italy.
Lorenzo ChiariottiDipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli "Federico II", Naples, Italy.
Sergio CocozzaDipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli "Federico II", Naples, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCpG sites in an individual molecule may exist in a binary state (methylated or unmethylated) and each individual DNA molecule, containing a certain number of CpGs, is a combination of these states defining an epihaplotype. Classic quantification based approaches to study DNA methylation are intrinsically unable to fully represent the complexity of the underlying methylation substrate. Epihaplotype based approaches, on the other hand, allow methylation profiles of cell populations to be studied at the single molecule level. For such investigations, next-generation sequencing techniques can be used, both for quantitative and for epihaplotype analysis. Currently available tools for methylation analysis lack output formats that explicitly report CpG methylation profiles at the single molecule level and that have suited statistical tools for their interpretation.

resultsHere we present ampliMethProfiler, a python-based pipeline for the extraction and statistical epihaplotype analysis of amplicons from targeted deep bisulfite sequencing of multiple DNA regions.

conclusionsampliMethProfiler tool provides an easy and user friendly way to extract and analyze the epihaplotype composition of reads from targeted bisulfite sequencing experiments. ampliMethProfiler is written in python language and requires a local installation of BLAST and (optionally) QIIME tools. It can be run on Linux and OS X platforms. The software is open source and freely available at http://amplimethprofiler.sourceforge.net .

Indexed as

DNA MethylationSoftwareAnimalsCpG IslandsD-Aspartate OxidaseDNAGastrointestinal TractHigh-Throughput Nucleotide SequencingHumansMiceSequence Analysis, DNASulfitesD-Aspartate OxidaseDNAhydrogen sulfiteSulfitesBisulfite sequencingDNA methylationEpihaplotypeEpihaplotype based analysisMethylation profiles

Identifiers

PMID27884103
PMCPMC5123276

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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.