Evidence map›Paper›PMID 26934703›Full record

ArticlePloS one2016

Performance of Different Analytical Software Packages in Quantification of DNA Methylation by Pyrosequencing.

Chiara Grasso, Morena Trevisan, Valentina Fiano, Valentina Tarallo, Laura De Marco, Carlotta Sacerdote, Lorenzo Richiardi, Franco Merletti, Anna Gillio-Tos

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Chiara GrassoCancer Epidemiology Unit - C.E.R.M.S, Department of Medical Sciences, University of Turin, Turin, Italy.
Morena TrevisanCancer Epidemiology Unit - C.E.R.M.S, Department of Medical Sciences, University of Turin, Turin, Italy.
Valentina FianoCancer Epidemiology Unit - C.E.R.M.S, Department of Medical Sciences, University of Turin, Turin, Italy.
Valentina TaralloCancer Epidemiology Unit - C.E.R.M.S, Department of Medical Sciences, University of Turin, Turin, Italy.
Laura De MarcoCancer Epidemiology Unit - C.E.R.M.S, Department of Medical Sciences, University of Turin, Turin, Italy.
Carlotta SacerdoteCancer Epidemiology Unit, Department of Medical Sciences, City of Health and Science Hospital, Turin, Italy.
Lorenzo RichiardiCancer Epidemiology Unit - C.E.R.M.S, Department of Medical Sciences, University of Turin, Turin, Italy.
Franco MerlettiCancer Epidemiology Unit - C.E.R.M.S, Department of Medical Sciences, University of Turin, Turin, Italy.
Anna Gillio-TosCancer Epidemiology Unit - C.E.R.M.S, Department of Medical Sciences, University of Turin, Turin, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPyrosequencing has emerged as an alternative method of nucleic acid sequencing, well suited for many applications which aim to characterize single nucleotide polymorphisms, mutations, microbial types and CpG methylation in the target DNA. The commercially available pyrosequencing systems can harbor two different types of software which allow analysis in AQ or CpG mode, respectively, both widely employed for DNA methylation analysis.

objectiveAim of the study was to assess the performance for DNA methylation analysis at CpG sites of the two pyrosequencing software which allow analysis in AQ or CpG mode, respectively. Despite CpG mode having been specifically generated for CpG methylation quantification, many investigations on this topic have been carried out with AQ mode. As proof of equivalent performance of the two software for this type of analysis is not available, the focus of this paper was to evaluate if the two modes currently used for CpG methylation assessment by pyrosequencing may give overlapping results.

methodsWe compared the performance of the two software in quantifying DNA methylation in the promoter of selected genes (GSTP1, MGMT, LINE-1) by testing two case series which include DNA from paraffin embedded prostate cancer tissues (PC study, N = 36) and DNA from blood fractions of healthy people (DD study, N = 28), respectively.

resultsWe found discrepancy in the two pyrosequencing software-based quality assignment of DNA methylation assays. Compared to the software for analysis in the AQ mode, less permissive criteria are supported by the Pyro Q-CpG software, which enables analysis in CpG mode. CpG mode warns the operators about potential unsatisfactory performance of the assay and ensures a more accurate quantitative evaluation of DNA methylation at CpG sites.

conclusionThe implementation of CpG mode is strongly advisable in order to improve the reliability of the methylation analysis results achievable by pyrosequencing.

Indexed as

CpG IslandsDNA MethylationSoftwareBase SequenceDNA Modification MethylasesDNA Repair EnzymesGlutathione S-Transferase piHumansLong Interspersed Nucleotide ElementsMalePromoter Regions, GeneticProstateProstatic NeoplasmsSequence Analysis, DNATumor Suppressor ProteinsDNA Modification MethylasesDNA Repair EnzymesGlutathione S-Transferase piGSTP1 protein, humanMGMT protein, humanTumor Suppressor Proteins

Identifiers

PMID26934703
PMCPMC4775062

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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.