The Impact of MIQE Guidelines in the Plant Science Community.

Ellen De Keyser, Laurence Desmet, Jan De Riek
ILVO, Belgium

Abstract
Currently in plant research, validated reliable RT-qPCR protocols are still rare. The last decade gene expression studies have been implemented widely in plant science. However, the methods used are often only semi-quantitative or quantification was not at all performed according to the MIQE-guidelines. The necessity of using multiple reference genes has become obvious in quite some cases, but assay-specific validation of these genes is often lacking. Still too often reference genes are selected for a species-wide application, no matter what treatment is given. Also RNA quality control is a crucial bottleneck. Machines for capillary electrophoresis allow to determine RNA quality quite easily, but how do you deal with this information? RIN or RQI values do not apply on plant material, since the training software was only developed using human/animal material. Plant material does not contain a 28SrRNA band but a 25S band. In addition, total RNA in chloroplast-containing plant tissues also consists of 16S and 23S rRNA adding 2 extra peaks. An alternative approach (visual evaluation) needs to be taken to decide on the quality of plant RNA samples. The use of noRT samples is another delicate point. Hardly any paper reports on the use of noRTs and in those cases noRTs were used after all, no information is available on the outcome. In our experience, the appearance of samples of which the Cq-value of the noRT is within 5 units of the actual sample are common in most experiments. Ignoring this information can lead to a severe overestimation of the gene expression in a specific sample. These three problems will be discussed more profoundly in view of the necessary application of the MIQE-guidelines in plant research.


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Four Years of RDML qPCR Data Format – Achievements and Opportunities

Andreas Untergasser1, Steve Lefever2, Jan M Ruijter3, Jan Hellemans4, Jo Vandesompele2,4
1University Heidelberg, Heidelberg, Germany; 2Ghent University, Ghent, Belgium; 3Academic Medical Center, Amsterdam, The Netherlands;4Biogazelle, Zwijnaarde, Belgium

Abstract
Quantitative PCR (qPCR) is the gold standard method for accurate and sensitive nucleic acid quantification. To improve the quality and transparency of experiment design, data-analysis and reporting of results, the MIQE guidelines were established in 2009 (Bustin et al., Clinical Chemistry). One of the recommended items was making the raw qPCR data available under the form of a universal data format.
The Real-time PCR Data Markup Language (RDML) was designed to establish a vendor independent, freely available XML based file format to store and exchange qPCR data. RDML stores the raw data acquired by the machine as well as the information required for its interpretation, such as sample annotation, primer and probe sequences and cycling protocol. When provided with publications, RDML-files should enable readers to re-evaluate the data and confirm the conclusions. The first version of RDML was published in 2009 and has already been cited 50 times. Furthermore, the RDML file format was supported by instrument manufacturers realizing its potential and today Bio-Rad (CFX96 and CFX384), Life Technologies (StepOne, ViiA7 and QuantStudio) and Roche (LC96) have enabled their instrument software to export data in the RDML-format. Additionally, third party software supporting RDML has started to emerge. The software solutions include primer design tools (primer3plus), assay databases (RTPrimerDB) and data analysis software (LinRegPCR and qbasePLUS). Even though more and more qPCR instruments are able to store data in the RDML format and RDML is being increasingly used in instrument independent data analysis, still too few publications make raw data available in the RDML format.
As qPCR continues to develop, so does RDML. The latter development is coordinated by the RDML consortium, a group of scientists, software developers and instrument manufacturers (http://www.rdml.org). The joined efforts resulted in an improved 1.1 version, and version 1.2 is currently being drafted. This consortium is not limited to its current members; it invites all interested parties to join the effort. [On behalf of the RDML consortium]


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The use and usefulness of amplification curve analysis in quantitative PCR.

Jan M Ruijter1, Michael W Pfaffl2, Sheng Zhao3, Andrej N Spiess4, Gregory Boggy5, Jochen Blom6, Robert G Rutledge7, Davide Sisti8, Antoon Lievens9, Katleen De Preter10, Stefaan Derveaux11, Jan Hellemans12, Jo Vandesompele10
1Academic Medical Centre, Amsterdam, the Netherlands; 2Technical University of Munich, Weihenstephan, Germany; 3University of California, Berkeley, USA; 4University Hospital Hamburg-Eppendorf, Germany; 5eDNA Software Inc., Ann Arbor, USA; 6Center for Biotechnology, Bielefeld University, Germany; 7Laurentian Forestry Centre, Quebec, Canada; 8University of Urbino, Urbino, Italy; 9Department of Applied Mathematics and Computer Science, Ghent, Belgium; 10Center for Medical Genetics, Ghent, Belgium; 11Wafergen, Fremont, CA, USA; 12Biogazelle, Zwijnaarde, Belgium

Abstract
RNA transcripts such as mRNA or microRNA are frequently used as biomarkers to determine disease state or response to therapy. Reverse transcription (RT) in combination with quantitative PCR (qPCR) has become the method of choice to quantify small amounts of such RNA molecules. In parallel with the democratization of RT-qPCR and its increasing use in biomedical research or biomarker discovery, we witnessed a growth in the number of gene expression data analysis methods. Most of these methods are based on the principle that the position of the amplification curve with respect to the cycle-axis is a measure for the initial target quantity: the later the curve, the lower the target quantity. However, most methods differ in the mathematical algorithms used to determine this position, as well as in the way the efficiency of the PCR reaction (the fold increase of product per cycle) is determined and applied in the calculations. Moreover, there is dispute about whether the PCR efficiency is constant or continuously decreasing. Together this has lead to the development of different methods to analyze amplification curves. In published comparisons of these methods, available algorithms were typically applied in a restricted or outdated way, which does not do them justice. Therefore, we aimed at development of a framework for robust and unbiased assessment of curve analysis performance whereby various publicly available curve analysis methods were thoroughly compared using a previously published large clinical data set. The original developers of these methods applied their algorithms and are co-author on this study. We assessed the curve analysis methods’ impact on transcriptional biomarker identification in terms of expression level, statistical significance, and patient-classification accuracy. The concentration series per gene, together with data sets from unpublished technical performance experiments, were analyzed in order to assess the algorithms’ precision, bias, and resolution. While large differences exist between methods when considering the technical performance experiments, most methods perform relatively well on the biomarker data. The data and the analysis results per method are made available to serve as benchmark for further development and evaluation of qPCR curve analysis methods (Data set).


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Management and Automation of qPCR Diagnostic Workflows

Matjaz Hren
BioSistemika, Slovenia

Abstract
In the molecular diagnostics field, qPCR (Real-Time Polymerase Chain Reaction) is one of the leading methods because it allows detection and precise quantification of specific DNA / RNA sequences. The core advantages that ensure qPCR’s broad applicability are high sensitivity and specificity and a broad dynamic range. Sample preparation and data analysis are complex qPCR steps, mostly performed by high-level experts. There are several solutions available for automation of the so called “wet-lab” part of the workflow (which includes sample preparation, reagent & sample loading onto qPCR plates). However when it comes to the so called “dry-lab”, which covers experiment design, data analysis and interpretation, quality control and reporting, there are no simple solutions available. In addition to that there are no internationally accepted guidelines or standards for diagnostic use of qPCR such as MIQE for research qPCR community.
This problem can be solved by different approaches. One of the most common is in-house development of solutions by preparing a series of macro-based spreadsheet documents in combination with modifications of Laboratory Information Management Systems (LIMS) to automate as much of the qPCR workflow as possible. However users still have to use different program environments to design analyses, create templates for lab work (e.g. mastermix calculation) and finally to analyse and interpret results and to prepare reports. This approach is still quite tedious and includes some repetitive work, which is somewhat contradictory to the demand for obtaining fast results without compromising quality in qPCR diagnostics. So we see that these solutions only partially address the demand for automation, unification and simplification of the qPCR workflow in diagnostic environments. Therefore we designed easy to use software that manages complete diagnostic qPCR workflow. The software guides users from the initial experiment design, to the final reports utilizing powerful expert knowledge. This easy to use software unifies the entire qPCR workflow. It calculates reagent concentrations for selected analyses, prepares wet lab outputs, analyses raw data and interprets the results while taking into account the hierarchical positions among lab employees. It communicates with LIMS and qPCR thermal cyclers and is quality assurance compliant. It has been designed to turn complex and unconsolidated approach into one simple, controlled environment. Its basic purpose is easing the pressure on high-level scientists and to make qPCR diagnostics and efficient process.


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Applying the MIQE Guidelines to Screens Utilizing qPCR Focused Arrays

Gregory L Shipley
Shipley Consulting, LLC, United States of America

Abstract
Performing a screening experiment utilizing qPCR and focused miRNA or mRNA plate arrays is not like running a standard qPCR experiment. Experimental design and execution require some compromises that immediately precludes the implementation of the full set of recommendations as outlined in the MIQE Guidelines. In this talk I will show the differences between the two experimental procedures and how best to setup qPCR focused array screening experiments utilizing automation. I will then discuss post-screen validation of screening results, present some tips on running these experiments and show it’s importance in completing a MIQE compliant screening experiment.


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Applying The MIQE guidelines to clinical and pre-clinical trials

Maxime Dooms2, Abalo Chango2, Essam Azhar1, Steve Harakeh1, Elie Barbour3, Flore Depint2, Afif Michel Abdel Nour1
1KAU/KFRMC/ Special Infectious Agent unit Biosafety Level 3, Saudi Arabia; 2Institut Polytechnique LaSalle Beauvais, Beauvais, France;3American University of Beirut, Beirut, Lebanon

Abstract
The ‘‘Minimum Information for the Publication of qPCR Experiments’’ guidelines are targeted at gene expression experiments and have to our knowledge not been applied to qPCR assays carried out in the context of clinical trials. This report details the use of the MIQE qPCR app for iPhone (App Store, Apple) to assess the MIQE compliance of one clinical and five pre-clinical trials. This resulted in the need to include 14 modifications that make the guidelines more relevant for the assessment of this special type of application. We also discuss the need for flexibility, since while some parameters increase experimental quality, they also require more reagents and more time, which is not always feasible in a clinical setting. The second part of my talk will be an update on the MIQE-qPCR app through numbers since 2011. I will finish by a quick review on the implementation of the MIQE guideline in the Middle East challenges and opportunities.


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Quality control in Quantitative PCR

Kristina Lind, Jennifer Pettersson, Robert Sjöback, Mikael Kubista
TATAA Biocenter, Sweden

Abstract
When working with qPCR it is very important to have control over all the different steps that are included in the process. It reaches from the experimental design all the way to the data analysis. Leaving out a part of the process from your quality control may result in erronous conclusions and and decisions. To help researchers and reviewers to have a better overview of the different parts that needs to be taken into consideration, the MIQE guidelines have been published. One important part is the qPCR assay itself. In this talk I will describe the process of validating new qPCR assays, how to determine efficiency, linear dynamic range, limit of detecion (LOD), limit of quantification (LOQ) and precision.


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MIQE 2009-2013 – its impact four years after publication

Stephen Bustin
Postgraduate Medical Institute, Anglia Ruskin University, Chelmsford, UK

Abstract
Approximately four years that have passed since the publication of the MIQE guidelines, allowing an objective assessment of their impact on qPCR-based research. The picture is rather mixed: on the plus-side, the MIQE paper itself is seeing significantly increased month-on-month citation rates and there has been and continues to be widespread publicity around MIQE, with numerous web seminars, workshops and information leaflets spreading the message of transparency and standardisation. In addition, the keen engagement by many instrument and reagent manufacturers, led by BioRad and Agilent, has been a very positive development, resulting in a very high level of expertise amongst their field application specialists. How well researchers implement the MIQE guidelines is another issue, and here the picture is far less positive. Not all manufacturers take the guidelines seriously and many researchers, especially at the principal investigation level, are perfectly content to continue publishing data of questionable biological relevance. Most frustratingly, the editors of most high impact factor journals have not seen the need to encourage the use of the guidelines by their contributors, the BMC group of journals being the honourable exception. Ultimately, the technical standard of scientific publications will not increase until there is some incentive to follow guidelines and, although areas of qPCR-based research continue to spread, improved reagents are launched and analysis methods are becoming increasingly sophisticated, the quality of the research output remains suspect.


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