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PrimedRPA: primer design for recombinase polymerase amplification assays

Authors: Matthew Higgins; Matt Ravenhall; Daniel Ward; Jody Phelan; Amy Ibrahim; Matthew S. Forrest; Taane G. Clark; +1 Authors

PrimedRPA: primer design for recombinase polymerase amplification assays

Abstract

Abstract Summary Recombinase polymerase amplification (RPA), an isothermal nucleic acid amplification method, is enhancing our ability to detect a diverse array of pathogens, thereby assisting the diagnosis of infectious diseases and the detection of microorganisms in food and water. However, new bioinformatics tools are needed to automate and improve the design of the primers and probes sets to be used in RPA, particularly to account for the high genetic diversity of circulating pathogens and cross detection of genetically similar organisms. PrimedRPA is a python-based package that automates the creation and filtering of RPA primers and probe sets. It aligns several sequences to identify conserved targets, and filters regions that cross react with possible background organisms. Availability and implementation PrimedRPA was implemented in Python 3 and supported on Linux and MacOS and is freely available from http://pathogenseq.lshtm.ac.uk/PrimedRPA.html. Supplementary information Supplementary data are available at Bioinformatics online.

Related Organizations
Keywords

Recombinases, Computational Biology, Applications Notes, Nucleic Acid Amplification Techniques, Software, DNA Primers

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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
84
Top 1%
Top 10%
Top 10%
Green
gold