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Introduction

Quantitative polymerase chain reaction (qPCR) assays measure the copies of a specific DNA target in a sample as that sample is repeatedly passed through the polymerase chain reaction. Special qPCR machines are required to quantify the amplification products at each step of the cycle. MIQE specifies the minimum information needed for a correct interpretation of the experiment.

Checklist for Quantitative PCR Assays

1. Sample
   • Fresh - How rapidly processed?
   • Frozen - How frozen?
   • Whole vs. microdissected
   • Sample storage conditions and duration
   • Fixed - How fixed, how old?


2. Nucleic acid
   • Quantification
   • Quality/integrity
   • Inhibition dilution or spike
   • DNA contamination assessment of RNA sample
   • DNase treatment
   • Manufacturer of reagents used
   • Amount of sample used for extraction


3. Reverse treanscriptions
   • cDNA priming method + concentration
   • Amount of RNA used per reaction
   • Enzyme type and concentration
   • Detailed reaction conditions
   • Manufacturer of reagents used
   • Reaction volume
   • Storage of cDNA


4. Target
   • Database name and target gene accession number
   • Intronless, targeting of all splice variants/splice variant-specific targeting
   • Official gene symbol
   • Location of amplicon with respect to reference sequence
   • Information about (retro)pseudogenes


5. Primers and probes
   • Primer sequences
   • Location of modification
   • End concentration of primers and optional probe(s) used
   • Primer purification method
   • Manufacturer of oligonucleotides
   • Probe sequence


6. Assay details
   • Amplicon length
   • Specific BLAST or equivalent in silico specific screen
   • Experimental validation of specificity
   • NTC; Sensitivity
   • PCR efficiency, PCR efficiency standard curve slope and r-squared value
   • RTPrimerDB ID
   • Secondary structure analysis around priming sites


7. PCR Cycling
   • Amount of cDNA/DNA used per reaction
   • Detailed reaction conditions, thermocycling parameters
   • Manufacturer of reagents used
   • Manual/robotic dispensing of reagents
   • Manufacturer of plates/tubes
   • Manufacturer of real-time instrument


8. Data analysis
   • Cq value determination method
   • Treatment of NTCs and technical replicates
   • Normalisation method
   • Is r-squared value of regression curve satisfactory?
   • Has assay sensitivity been adequately evaluated and described?
   • Has assay specificity been adequately described?
   • Is the dynamic range of the assay acceptable?
   • Is the coefficient of variation for inter and intra-assay reproducibility reasonable?
   • Concordance of biological replicates
   • Analysis program
   • Assay carried out by core lab or investigator's lab
   • Acknowledgement of author's contribution to analysis and interpretation
   • Submission of Cq values of raw data using RDML

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RDML guidelines (formaly known as MIqPCR)
Working draft, 4th April, 2008.

It is crucial that data acquisition, analysis and reporting become more transparent to allow reinterpretation and to guarantee compliance with quality standards. Therefore, following the example of the microarray community and their MIAME (Minimum Information About a Microarray Experiment) guidelines, we propose guidelines specifying the minimal information about qPCR experiments. A RDML guidelines compliant RDML file should contain all measured data as well as information about the samples and targets being analyzed.

In addition, data must be linked to samples and targets in an unequivocal way. Due to the complexity and diversity of experiments in which qPCR is utilized, the scope of the RDML guidelines is limited to the technology itself, which means that these guidelines can easily be integrated into other minimum information guidelines that focus on the wider experimental context. To coordinate this effort, the RDML consortium recently joined the MIBBI project (Minimum Information for Biological and Biomedical Investigations). The minimum information guidelines have been kept minimal to facilitate the creation of a compliant RDML files that make the least demand on researchers’ time, while requiring sufficient information for other researchers to interpret and reanalyze the data contained within an RDML guidelines compliant RDML file.

All information needed for the MIQE checklist can be stored in specialy designed elements or description strings inside an RDML file.

Reporting requirement for Quantitative PCR Assays

1. Administrative information
   1. Experiment description
      • Experiment description
      • Responsible person and contact details


2. Sample annotation
   1. Sample description
      • Sample ID
      • Sample description
      • cDNA synthesis method and DNAse treatment (cDNA samples only)
      • Template quantity (standard and optical calibrator samples only)
   2. Sample role in qPCR assay
      • Sample type
      • Inter run calibrator (true or false)
      • Calibrator sample (true or false)


3. Target annotation
   1. Target description
      • Target ID
      • Sequence of primers OR commercial assay description
   2. Target role in qPCR assay
      • Target type


4. Thermal Cycling Conditions Information
   1. PCR program
      • Complete description of the cycling conditions


5. Run data
   1. Instrument information
      • Plate format
      • Instrument description
      • Software description and version
   2. Information required for each well
      • Well ID
      • Sample ID
      • Target ID
      • Amplification curve fluorescence values for each data point
      • Melting curve fluorescence values for each data point
      • Quantification Cycle


6. Software requirements
   1. RDML-Support
      • Software solutions, including databases, must support the import and export of RDML files.
      • qPCR machines must allow the export of raw data for the amplification as well as for melting curves.

More detailed information about the terms used in the RDML guidelines can be found here
Download a document about the RDML guidelines.

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qPCR Assay Quality assessment
05 January 2009
by Stephen Bustion on

Guidelines for minimum information required for publication of qPCR data are currently being assembled and will be published in Clinical Chemistry.

qPCR quality assessment relates mainly to the reverse transcription -qPCR (RT-qPCR) variant of the technology. This is widely used to measure pathogen as well as cellular RNA copy numbers; the former, given appropriate standard operating procedures and technical expertise, is fairly straightforward. The latter can be highly problematic. For both types of assay, however, RNA quality is a major consideraton.

Quality assessment is a big fat elephant sitting in the room: everyone knows what needs to be done, but most researchers do not follow basic quality control guidelines. This serves to undermine the integrity of the scientific literature to such an extent, that a high proportion of publications are reporting technical or analytic artifacts.

Incredibly, many researchers are not bothered by this; indeed some have been heard to remark that they can't be bothered assessing RNA quality, worrying about reverse transcription or determining what normalisdation strategy to follow. However, efforts are underway to establish a checklist for journal editors and reviewers, with the aim of introducing a minumum standard of assay reporting.

What are the problems?

*** LATEST NEWS ***

PCR inhibition assessment generally depends on the assumption that inhibitors affect all PCR reactions to the same extent; i.e. that the reaction of interest and the control reaction are equally susceptible to inhibition. However, it appears that when copurified inhibitors are assessed in different PCR reactions, differential inhibition is observed and susceptibility to inhibition is highly variable between reactions. This has serious implications for all PCR-based gene expression studies, including the relatively new PCR array method, and for both qualitative and quantitative PCR-based molecular diagnostic assays, suggesting that careful consideration should be given to inhibition compatibility when conducting PCR analyses. Clearly, it is not safe to assume that different PCR reactions are equally susceptible to inhibition by substances co-purified in nucleic acid extracts.

Reference: Huggett JF, Novak T, Garson JA, Green C, Morris-Jones SD, Miller RF, Zumla A. Differential susceptibility of PCR reactions to inhibitors: an important and unrecognised phenomenon. BMC Res Notes 2008;1:70.

1. Inappropriate sample selection, coupled with the complexity and heterogeneity of any tissue biopy, especially from cancer and inconsistent handling procedures, results in variability and inaccurate mRNA quantification. In addition, there can be two sources of error: (i) sampling error, ie even if epithelial cells are being collected, the cell type within the epithelial population may have a different distribution compared with the collected population’ (ii) measurement error, which depends on the quality of instruments, reagents and operator.

2. The conversion of mRNA to cDNA is a major stumbling block and arguably is the single most variable step in the whole quantification procedure. It is well known, although not well publicised, that different reverse transcriptases have significantly different efficiencies of reverse transcription, and that these are target-dependent (1,2). Similarly, the mechanism of cDNA priming has a significant effect on the outcome of any quantification experiment, since gene-specific priming, random priming and oligo-dT all produce diverse results that are distinct for different mRNA targets. The choice of primer location on the target mRNA also can yield significantly different results, as mRNA adopts a tight secondary structure characterised by extensive intra-strand base pairing resulting in stem-loop structures (3). If reverse transcription primers are designed to target stems, rather than loops, or if the amplicon can adopt secondary structures, the efficiency of the RT step is significantly compromised. Characteristically, this results in non-quantitative and non-reproducible results.

3. The accuracy of gene expression profiling is highly dependent on mRNA quality (4,5). Unfortunately, this is an area that is distinguished by a prevalent lack of concern. A 2005 survey of the working practices of 100 experienced qPCR users revealed that attending a worryingly high 37% did not quality assess their RNA, with a further 4% using absorbance ratios which even then were known to be inadequate for quantification of mRNA (6). A survey of BMC publications in 2007/08 reveals that we have regressed since then, with >60% of papers not even mentioning mRNA quality and a substantial 10% continuing to rely on absorbance ratio measurements. Even when RNA quality is assessed, it is evaluated using either gel electrophoresis or microfluidics-based systems; this approach fails to take into account that such measurements only look at ribosomal RNA without relating the results to mRNA integrity, which is, after all, the real target of interest.

4. Splicing is a post-transcriptional modification in which a single gene can specify multiple proteins, allowing the synthesis of protein isoforms that are structurally and functionally distinct. Gene splicing affects most human genes (7) and plays an important role in human pathologies, including cancer (8). This generates significant problems with the interpretation of RT-qPCR and microarray data, since presence or, indeed significant changes in mRNA levels may reflect cell-, tissue- ot treatment-specific adjustments between different isoforms.

5. The increased realisation that allelic imbalance and allele-specific expression patterns are associated with increased disease risk (9,10) poses further problems for the interpretation of mRNA quantification data. Rather than avoiding SNPs when designing primers, it may be necessary to include them as part of an overall assay design strategy so as to be able to quantitate allele-specific expression accurately.

6. It is worth emphasising that in vivo mRNA is subject to constant degradation by complex interactions of deadenylation and decapping enzyme complexes as well as 3’-5, 5’-3’ exonucleases as well as endonucleases (11). This is likely to result in significant natural variability of mRNA levels between genes expressed in different tissues and individuals. This is in addition to any degradation introduced during the extraction of the RNA from tissue samples or during storage. Whilst these comments may seem obvious, their implications have never been explored.

7. Normalisation, known to be an essential component of proper data analysis (12), continues to be used in an inappropriate manner particularly in RT-qPCR applications, with a high proportion of papers still reporting expression patterns of target genes normalised against a single, unvalidated reference gene .

8. Inappropriate experimental designs, improper analyses, subjective interpretation of RT-qPCR data, variability of microarray results depending on the choice of analysis algorithms all combine to compromise the interpretation and confident application of quantitative, mRNA-targeted data (13).

The consequence of these, and other poor standards, is that a large number of publications report data that are at best unreliable, at worst misleading, with a dramatic and damaging effect on the integrity of the scientific literature. For example, a paper published in Science and named as a “breakthrough of the year”, has had to be withdrawn, because its results could not be repeated (14). More seriously, a paper using RT-qPCR technology and purporting to confirm an association between the presence of measles virus and gut pathology in children with developmental disorder (15) was used to claim a link between the MMR vaccine and autism (16). However, the data were significantly flawed as the RT-qPCR assay was applied in an inappropriate manner (ftp://autism.uscfc.uscourts.gov/autism/cedillo.html).

What is the solution?

First, it is essential to step back and concentrate on getting the basic technical problems sorted out. This includes enforcing minimum quality standards for template preparation, validation and consistent use of cDNA priming methods, enzymes, protocols and, equally critically, appropriate analysis of data.

Second, it is entirely unacceptable that most publications do not address the critical issue of RNA quality assessment. It is equally unacceptable that data are not normalised in an appropriate manner. Third

Third, it is essential that data acquisition, analysis and reporting become more transparent. Consequently, it is essential for the editors of scientific and biomedical publications to issue prescriptive checklists specifying the key information to include when reporting experimental results. There are significant efforts underway to organise such ‘minimum information’ checklists, with the “Minimum information for biological and biomedical investigations” (MIBBI) project offering a common portal aimed at promoting gradual data integration (http://mibbi.sourceforge.net).

Another development concerns the problems associated with attempting to share qPCR data between different laboratories and users. A new initiative, the “Real-time PCR Data Markup Language” (RDML) describes a structured and universal data standard for exchanging qPCR data (http://www.rdml.org/). Together with the accompanying guidelines for Minimal Information (MIqPCR), the data standard will contain sufficient information to understand the experimental setup, re-analyse the data and interpret the results. This is of particular importance for transparent exchange of annotated qPCR data between authors, peer reviewers, journals and readers.

Those intimately familiar with the molecular technologies underlying the advances proclaimed by the highest impact factor journals, then taken up by the popular press and finally shaping peoples’ expectations are only too familiar with their serious shortcomings. Unfortunately, it seems that very few researchers are willing to listen and even fewer are willing to change their modi operandi. It really is time to put the horse before the cart, and stop being blinded with ever-more technology.

References

1. Stahlberg, A., Hakansson, J., Xian, X., Semb, H., and Kubista, M. (2004) Clin Chem 50(3), 509-515
2. Stahlberg, A., Kubista, M., and Pfaffl, M. (2004) Clin Chem 50(9), 1678-1680
3. Bustin, S. A., and Nolan, T. (2004) J Biomol Tech 15(3), 155-166
4. Nolan, T., Hands, R. E., Ogunkolade, B. W., and Bustin, S. A. (2006) Anal Biochem 351, 308-310
5. Nolan, T., Hands, R. E., and Bustin, S. A. (2006) Nature Protocols 1(3), 1559-1582
6. Bustin, S. A. (2005) Expert Rev Mol Diagn 5(4), 493-498
7. Ben-Dov, C., Hartmann, B., Lundgren, J., and Valcarcel, J. (2008) J Biol Chem 283(3), 1229-1233
8. Pettigrew, C. A., and Brown, M. A. (2008) Front. Biosci. 13, 1090-1105
9. Meyer, K. B., Maia, A. T., O'Reilly, M., Teschendorff, A. E., Chin, S. F., Caldas, C., and Ponder, B. A. (2008) PLoS biology 6(5), e108
10. Chen, X., Weaver, J., Bove, B. A., Vanderveer, L. A., Weil, S. C., Miron, A., Daly, M. B., and Godwin, A. K. (2008) Hum. Mol. Genet. 17(9), 1336-1348
11. Coller, J., and Parker, R. (2004) Annu. Rev. Biochem. 73, 861-890
12. Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., Van Roy, N., De Paepe, A., and Speleman, F. (2002) Genome Biol 3(7), 0034.0031-0034.0011
13. Bustin, S. A., and Mueller, R. (2005) Clin Sci (Lond) 109(4), 365-379
14. Huang, T., Bohlenius, H., Eriksson, S., Parcy, F., and Nilsson, O. (2005) Science 309(5741), 1694-1696
15. Uhlmann, V., Martin, C. M., Sheils, O., Pilkington, L., Silva, I., Killalea, A., Murch, S. B., Walker-Smith, J., Thomson, M., Wakefield, A. J., and O'Leary, J. J. (2002) Mol Pathol 55(2), 84-90
16. Bradstreet, J. J., El Dahr, J., Anthony, A., Kartzinel, J. J., and Wakefield, A. J. (2004) Journal of American Physicians and Surgeons 9, 38-45

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MIQE - media & press review

• Publishing Data That Conform to the MIQE Guidelines
  Minimum information for publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines help researchers design qPCR experiments.
  Real-time quantitative polymerase chain reaction (qPCR) is a definitive technique for quantifying differences in gene expression levels between samples. However, a lack of consistency in experimental design and reporting combined with inadequate guidelines to review submitted articles with qPCR data greatly increases the potential of reporting statistically insignificant and conflicting results.1 The publication2 and retraction3 of a Science “Breakthrough of the Year 2005” article underlines the issue.
  
• MIQE Guidelines 'Slowly Filtering Through' PCR Community Despite Lack of Journal Enforcement
  by Bernadette Toner Genome Web
  Guidelines proposed in early 2009 to help standardize how qPCR results are reported are "slowly filtering through" the research community, but much work still needs to be done to improve the quality of published qPCR studies, according to one of the authors of the standard.
  
• Are your qPCR experiments compliant with MIQE?
  The MIQE guidelines establish specifications for the minimum information that must be reported for a qPCR experiment in order to ensure its relevance, accuracy, correct interpretation and repeatability.  Comply with MIQE guidelines !
  Learn why Prof. Kubista from the TATAA Biocenter uses the Agilent 2100 Bioanalyzer for RNA quality control  =>  Start webinar
  
• Feature Article - PCR Technology Review: Standardization of qPCR and RT-qPCR - New Guidelines Seek to Promote Accurate Interpretation of Data and Reliable Results  by Stephen A. Bustin, Jo Vandesompele, Michael W. Pfaffl  =>  download PDF
  The perceived ease of use of real-time quantitative PCR (qPCR) and reverse transcription PCR (RT-qPCR) technology has revolutionized life science research. Its effectiveness at amplification and quantification of low levels of nucleic acids has driven the emergence of numerous applications, including cellular mRNA and miRNA quantification, biomarker discovery and validation, microbial quantification, cancer risk assessment, gene dosage determination, and detection of extremely low copy targets for forensic investigations. This, in turn, has resulted in an abundance of publications utilizing qPCR data obtained with diverse reagents, protocols, analysis methods, and reporting formats. Unfortunately, few papers report in detail how these results were obtained. This lack of clarity and transparency has led to concern in the research community over the reliability of qPCR data interpretation and the real danger of the scientific literature being corrupted with publications reporting erroneous and conflicting results. This has already occurred in some cases, resulting, for example, in retraction of a Science “Breakthrough of the Year 2005” report. Now that qPCR has come of age, standardization is needed to ensure its validity, prompting the recent formulation of guidelines to increase experimental transparency, promote consistency between laboratories, and therefore, help assure the publication of valid conclusions.
• A practical approach to RT-qPCR - Publishing data that conforms to the MIQE guidelines (Bio-Rad amplification tech note 5859) by Sean Taylor, et al., Bio-Rad Laboratories, Hercules, CA
  
• MIQE Guidelines - RNA Qualitätskontrolle in der Genexpressionsanalytik – ein Meilenstein auf dem Weg zum Erfolg (in German) by Christiane Becker, Irmgard Riedmaier, and Michael W. Pfaffl
  Abstrakt (D) - Die Qualität des Probenmaterials, also der Gesamt-RNA, hat einen markanten Einfluss auf die Richtigkeit der quantitativen RT-PCR. Die Überprüfung der RNA Qualität vor einer Expressionsmessung ist unabdingbar, um verlässliche RT-qPCR Expressionsergebnisse zu erhalten.
  Abstract (E) - The integrity of total RNA has a distinct influence on the accuracy of RT-qPCR. Quality assessment is an essential step for the evaluation of reliable results in gene expression analysis.
  
• Press release - Standardization of qPCR and RT-qPCR - New Guidelines Seek to Promote Accurate Interpretation of Data and Reliable Results
  http://pressemitteilung.ws/node/166061 
  
• International Scientists Secure Quality in Molecular Diagnostics
  SALT LAKE CITY, March 31, 2009 - ARUP Laboratories and the American Association for Clinical Chemistry (AACC) announced today that a consensus guideline for a key laboratory method called qPCR (or quantitative polymerase chain reaction) was published by a group of international scientists representing the medical and research fields.
• Consensus Guideline Reached For Quantitative Polymerase Chain Reaction
  Press release by TATAA Biocenter
  Gothenburg, March 31, 2009 - TATAA BIOCENTER and the American Association for Clinical Chemistry (AACC), announced today that a consensus guideline for a key laboratory method called qPCR (or quantitative polymerase chain reaction) was published by a group of international scientists representing the medical and research fields.
• Internationale Wissenschaftler sorgen für Qualitätssicherung in der Molekulardiagnostik
  Salt Lake City (ots/PRNewswire) - - Einigung über Konsensus-Richtlinie bezüglich der quantitativen Polymerase-Kettenreaktion erzielt ARUP Laboratories und die American Association for Clinical Chemistry (AACC) gaben heute bekannt, dass eine Konsensus-Richtlinie für die wichtige, qPCR (quantitative Polymerase-Kettenreaktion) genannte Labormethode veröffentlicht worden sei. Verantwortlich für die Veröffentlichung war eine Gruppe internationaler Wissenschaftler als Vertreter der Gebiete Medizin und Forschung.
• Real-timePCR data markup language
  The aim of MIQE, coordinated by a group of research-active scientists and coordinated under the umbrella of MIBBI (Minimum Information for Biological and Biomedical Investigations http://www.mibbi.org) is to provide authors, reviewers and editors specifications for the minimum information that must be reported for a qPCR experiment in order to ensure its relevance, accuracy, correct interpretation and repeatability. A checklist, which should be submitted along with the paper, is available for authors in preparing a manuscript employing qPCR.
  http://www.rdml.org/guidelines.php
• Letter of the MIQE authors
  Letter to leading journals recommending the use of MIQE for quality control of qPCR experiments.
  Download letter PDF
• IBT of the Academy of Sciences of the Czech Republic
  PRAGUE, April 1, 2009 - Institute of Biotechnology of the Academy of Sciences of the Czech Republic, v.v.i. (IBT) and the American Association for Clinical Chemistry (AACC), announced today that a consensus guideline for a key laboratory method called qPCR (or quantitative polymerase chain reaction) was published by a group of international scientists representing the medical and research fields.
• qPCR Grows Up by genome web
  Bustin is now at the forefront of a movement to get researchers to follow a set of guidelines, the minimum information for publication of quantitative real-time PCR experiments, or MIQE, that were published online at Clinical Chemistry in February.
  "In my talks, I always refer to the cowboy stage of qPCR. For quite a while everything went," Bustin says. In particular, he casts a critical eye on how people have been normalizing their gene expression data. In northern blot and standard PCR experiments that didn't give quantitative data, people often used a single reference gene. "People just moved that approach to qPCR without thinking about what they were doing," Bustin says. "Are these reference genes really invariant or are they changing with treatment?"
• qPCR Assay Quality assessment on SciTopics
  8 April 2009 by Prof Stephen Bustin;  Category: Biochemistry, Genetics and Molecular Biology
  Guidelines for minimum information required for publication of qPCR data have now been published in Clinical Chemistry
  qPCR quality assessment relates mainly to the reverse transcription -qPCR (RT-qPCR) variant of the technology. This is widely used to measure pathogen as well as cellular RNA copy numbers; the former, given appropriate standard operating procedures and technical expertise, is fairly straightforward. The latter can be highly problematic. For both types of assay, however, RNA quality is a major consideraton.
  Quality assessment is a big fat elephant sitting in the room: everyone knows what needs to be done, but most researchers do not follow basic quality control guidelines. This serves to undermine the integrity of the scientific literature to such an extent, that a high proportion of publications are reporting technical or analytic artifacts.
  Incredibly, many researchers are not bothered by this; indeed some have been heard to remark that they can't be bothered assessing RNA quality, worrying about reverse transcription or determining what normalisdation strategy to follow. However, efforts are underway to establish a checklist for journal editors and reviewers, with the aim of introducing a minumum standard of assay reporting.
• Quest Agrees to Pay Fine for Misbranding Tests
  First-ever consensus guidelines on quantitative PCR aim to improve the quality and transparency of studies involving qPCR (Clin Chem, 2009;55:611-622). The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines outline the minimum information necessary to evaluate qPCR studies, including all relevant experimental conditions and assay characteristics, and full disclosure of all reagents, sequences, and analysis methods. The guidelines include an 85-item checklist of desirable and essential steps to be followed when using qPCR and information to be divulged from experiments involving qPCR. The purpose of the guideline is to encourage better experimental practice, so as to enable more reliable and unequivocal interpretation of qPCR results.
  Use of qPCR has proliferated, yet studies “invariably use diverse reagents, protocols, analysis methods, and reporting methods,” the authors wrote. “This remarkable lack of consensus on how best to perform qPCR experiments has the adverse consequence of perpetuating a string of serious shortcomings that encumber its status as an independent yardstick.” If researchers follow the guidelines, they should be able to design and report qPCR experiments with greater inherent value, and fellow researchers, editors, and laboratorians should be able to evaluate the technical quality of the published data against an established standard.
• Advancing DNA research safely and securely
  27 May 2009; Dr Jeremy Garson& Dr Jim Huggett - Dr Jeremy Garson (UCL Centre for Virology) and Dr Jim Huggett (UCL Centre for Infectious Diseases and International Health) have been at the heart of developing a new set of guidelines on the way scientists the world over use qPCR – a technology crucial to forensic analysis and diagnosing diseases. Below Dr Huggett explains how and why they went about it.
  What do you hope to achieve with the guidelines?
  By developing the MIQE guidelines, we aim to enable researchers to perform high-quality qPCR that allows their experiments to be easily understood and repeated by workers in laboratories anywhere in the world. For science to advance swiftly and securely it is essential that the results of experiments can be independently reproduced.
• Data that Meets the MIQE Guidelines
  Canadian BioTechnologist2.0 on 27 May 2009 - Key Steps to Generating High Quality Real-Time PCR (RT-qPCR) Data that Meets the MIQE Guidelines Speaker: Sean Taylor, Ph.D., Bio-Rad Laboratories PDF slide deck.
• GLOSSARY OF REAL-TIME PCR TERMS by M.Tevfik Dorak
  MIQE - An initiative by the International Real-time PCR Data Markup Language (RDML) Consortium to generate a structured and universal data standard for exchanging quantitative real-time PCR experiment data. This effort resulted in standard guidelines for reporting qPCR data (publication checklist: XLS, PDF)