The Real-time PCR Data Markup Language (RDML) is a structured and universal data standard for exchanging quantitative PCR (qPCR) data. Together with the accompanying guidelines for Minimal Information (MIQPCR), the data standard should contain sufficient information to understand the experimental setup, re-analyse the data and interpret the results. The data standard is a flat text file in Extensible Markup Language (XML) and enables transparent exchange of annotated qPCR data between instrument software and third-party data analysis packages, between colleagues and collaborators, and between authors, peer reviewers, journals and readers. To support the public acceptance of this standard, both an on-line RDML file generator is available for end users, as well as RDML software libraries to be used by software developers, enabling import and export of RDML data files.

RDML-Ninja and RDMLdb for standardized exchange of qPCR data.
Ruijter JM, Lefever S, Anckaert J, Hellemans J, Pfaffl MW, Benes V, Bustin SA, Vandesompele J, Untergasser A; and RDML consortium.
BMC Bioinformatics. 2015 16: 197

BACKGROUND: The universal qPCR data exchange file format RDML is today well accepted by the scientific community, part of the MIQE guidelines and implemented in many qPCR instruments. With the increased use of RDML new challenges emerge. The flexibility of the RDML format resulted in some implementations that did not meet the expectations of the consortium in the level of support or the use of elements.
RESULTS: In the current RDML version 1.2 the description of the elements was sharpened. The open source editor RDML-Ninja was released ( ). RDML-Ninja allows to visualize, edit and validate RDML files and thus clarifies the use of RDML elements. Furthermore RDML-Ninja serves as reference implementation for RDML and enables migration between RDML versions independent of the instrument software. The database RDMLdb will serve as an online repository for RDML files and facilitate the exchange of RDML data ( ). Authors can upload their RDML files and reference them in publications by the unique identifier provided by RDMLdb. The MIQE guidelines propose a rich set of information required to document each qPCR run. RDML provides the vehicle to store and maintain this information and current development aims at further integration of MIQE requirements into the RDML format.
CONCLUSIONS: The editor RDML-Ninja and the database RDMLdb enable scientists to evaluate and exchange qPCR data in the instrument-independent RDML format. We are confident that this infrastructure will build the foundation for standardized qPCR data exchange among scientists, research groups, and during publication.

Link to MIQE sub-domain

RDML:  structured language and reporting guidelines for real-time quantitative PCR data.
Lefever S, Hellemans J, Pattyn F, Przybylski DR, Taylor C, Geurts R, Untergasser A, Vandesompele J; on behalf of the RDML consortium.
Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium.
Nucleic Acids Res. 2009 Apr;37(7): 2065-2069

The XML-based Real-Time PCR Data Markup Language (RDML) has been developed by the RDML consortium ( to enable straightforward exchange of qPCR data and related information between qPCR instruments and third party data analysis software, between colleagues and collaborators and between experimenters and journals or public repositories. We here also propose data related guidelines as a subset of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) to guarantee inclusion of key data information when reporting experimental results.

The RDML-consortium was founded to develop a universal data format for real-time PCR data, named RDML (Real-time PCR Data Markup Language). The intention to design an universal data format is based on the experience that it is difficult to share qPCR data between different laboratories and users, or exchange data between different software packages or analysis tools.

The problem is founded in the data collection software packages that, depending on the company that provides these with their instrument(s), save data in a proprietary format and allow to export information in various file formats (.CSV, .TXT, .XLS), with different layout and data field terminology.

A common universal format would allow easy exchange of raw annotated data between different laboratories. It would make it possible to include qPCR data in scientific papers, allowing both reviewers and readers to re-analyse the data, similar to the MIAME guidelines propose for microarray experiments (Brazma et al., Nat Genet., 2001; see publication below).

In principle, the universal data format should contain sufficient information to understand the experimental setup, re-analyse the data and interpret the results. The data format is a flat text file in Extensible Markup Language (XML), termed RDML, an acronym for "Real-time PCR Data Markup Language". The file extension is *.rdml or *.rdm. The format is independent of computer hardware, operating system or available software package, and can be extended in the future to include additional information if required.

You can find the RDML schema  here.

Contact information  here

Dear LinRegPCR user,

We recently updated LinRegPCR to implement the import and export of RDML files

RDML was developed as a  standard for export, exchange, and storage of quantitative PCR data and is supported by several large qPCR system suppliers as well as by data analysis software like qbase-plus. LinRegPCR now forms a link between your qPCR system and such statistical analysis software. LinRegPCR can handle RDML versions 1.0 and 1.1, as well as RDML files in which floating point values are written with decimals points and decimal commas. LinRegPCR will write the analysis results to an RDML file, version 1.1, with decimal points to maintain compatibilty with the current RDML specification.

The RDML input option is the main addition to LinRegPCR that was implemented in 2012. There were also several qPCR systems added to the list of input formats from Excel files. For other minor changes in the program, please have a look at the recent updates listed on the LinRegPCR website (

On our site you will also find a link to a recent paper (Ruijter et al., Methods 2012), in which LinRegPCR and other publicly available PCR amplification curve analysis programs were compared. This paper is unique in the field of qPCR because all analysis methods were applied by their original developers, and thus in the currently recommended way. The paper was co-authored by the developers of these curve analysis programs and members of the geNorm team, who performed the statistical analysis. The datasets used for this comparison, and the analysis results, can be downloaded from  

I hope you continue to enjoy the use of LinRegPCR.

Best wishes for the coming festive season and your future scientific endeavours,

Jan M Ruijter

Minimum information about a microarray experiment (MIAME)-toward standards for microarray data.

Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M.
Nat Genet. 2001 29(4): 365-3671

Comment in:  Nat Genet. 2001 Dec;29(4):373. Nat Genet. 2006 38(10):1089.

European Bioinformatics Institute, EMBL outstation, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.

Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.

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