Evidence map›Paper›PMID 24472427›Full record

ArticleBMC bioinformatics2014

A tool for design of primers for microRNA-specific quantitative RT-qPCR.

Peter K Busk

Abstract read
In one paragraph

Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 146 papers.

0numbers the graph read from it
0cells of the map it votes in
146citing 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

146 citing papers in PubMed.

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  18. Differential Expression of Small Non-Coding RNAs in Uterine Leiomyomas.International journal of molecular sciences · 2025
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86 more citing papers are in PubMed but not listed here.

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

1 author.

Peter K BuskDepartment of Biotechnology, Chemistry and Environmental Engineering, Aalborg University Copenhagen, A,C, Meyers Vænge 15, Copenhagen, SV 2450, Denmark. pkb@bio.aau.dk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicroRNAs are small but biologically important RNA molecules. Although different methods can be used for quantification of microRNAs, quantitative PCR is regarded as the reference that is used to validate other methods. Several commercial qPCR assays are available but they often come at a high price and the sequences of the primers are not disclosed. An alternative to commercial assays is to manually design primers but this work is tedious and, hence, not practical for the design of primers for a larger number of targets.

resultsI have developed the software miRprimer for automatic design of primers for the method miR-specific RT-qPCR, which is one of the best performing microRNA qPCR methods available. The algorithm is based on an implementation of the previously published rules for manual design of miR-specific primers with the additional feature of evaluating the propensity of formation of secondary structures and primer dimers. Testing of the primers showed that 76 out of 79 primers (96%) worked for quantification of microRNAs by miR-specific RT-qPCR of mammalian RNA samples. This success rate corresponds to the success rate of manual primer design. Furthermore, primers designed by this method have been distributed to several labs and used successfully in published studies.

conclusionsThe software miRprimer is an automatic and easy method for design of functional primers for miR-specific RT-qPCR. The application is available as stand-alone software that will work on the MS Windows platform and in a developer version written in the Ruby programming language.

Indexed as

AlgorithmsComputational BiologyDNA PrimersMicroRNAsReal-Time Polymerase Chain ReactionRNA ProbesSoftwareDNA PrimersMicroRNAsRNA Probes

Identifiers

PMID24472427
PMCPMC3922658

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Registered trials

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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.