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quantGenius: implementation of a decision support system for qPCR-based gene quantification

Overview of attention for article published in BMC Bioinformatics, May 2017
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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Title
quantGenius: implementation of a decision support system for qPCR-based gene quantification
Published in
BMC Bioinformatics, May 2017
DOI 10.1186/s12859-017-1688-7
Pubmed ID
Authors

Špela Baebler, Miha Svalina, Marko Petek, Katja Stare, Ana Rotter, Maruša Pompe-Novak, Kristina Gruden

Abstract

Quantitative molecular biology remains a challenge for researchers due to inconsistent approaches for control of errors in the final results. Due to several factors that can influence the final result, quantitative analysis and interpretation of qPCR data are still not trivial. Together with the development of high-throughput qPCR platforms, there is a need for a tool allowing for robust, reliable and fast nucleic acid quantification. We have developed "quantGenius" ( http://quantgenius.nib.si ), an open-access web application for a reliable qPCR-based quantification of nucleic acids. The quantGenius workflow interactively guides the user through data import, quality control (QC) and calculation steps. The input is machine- and chemistry-independent. Quantification is performed using the standard curve approach, with normalization to one or several reference genes. The special feature of the application is the implementation of user-guided QC-based decision support system, based on qPCR standards, that takes into account pipetting errors, assay amplification efficiencies, limits of detection and quantification of the assays as well as the control of PCR inhibition in individual samples. The intermediate calculations and final results are exportable in a data matrix suitable for further statistical analysis or visualization. We additionally compare the most important features of quantGenius with similar advanced software tools and illustrate the importance of proper QC system in the analysis of qPCR data in two use cases. To our knowledge, quantGenius is the only qPCR data analysis tool that integrates QC-based decision support and will help scientists to obtain reliable results which are the basis for biologically meaningful data interpretation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 68 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 22%
Student > Ph. D. Student 13 19%
Student > Master 9 13%
Professor 5 7%
Student > Bachelor 4 6%
Other 14 21%
Unknown 8 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 37%
Biochemistry, Genetics and Molecular Biology 11 16%
Medicine and Dentistry 5 7%
Engineering 3 4%
Computer Science 2 3%
Other 7 10%
Unknown 15 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 March 2018.
All research outputs
#14,202,635
of 25,079,481 outputs
Outputs from BMC Bioinformatics
#3,823
of 7,644 outputs
Outputs of similar age
#153,363
of 319,162 outputs
Outputs of similar age from BMC Bioinformatics
#48
of 102 outputs
Altmetric has tracked 25,079,481 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,644 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 319,162 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 102 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.