AnoPrimer: Primer Design in malaria vectors informed by range-wide genomic variation.

ABSTRACT

The major malaria mosquitoes, Anopheles gambiae s.l and Anopheles funestus, are some of the most studied organisms in medical research and also some of the most genetically diverse. When designing polymerase chain reaction (PCR) or hybridisation-based molecular assays, reliable primer and probe design is crucial. However, single nucleotide polymorphisms (SNPs) in primer binding sites can prevent primer binding, leading to null alleles, or bind suboptimally, leading to preferential amplification of specific alleles. Given the extreme genetic diversity of Anopheles mosquitoes, researchers need to consider this genetic variation when designing primers and probes to avoid amplification problems. In this note, we present a Python package, AnoPrimer, which exploits the Ag1000G and Af1000 datasets and allows users to rapidly design primers in An. gambiae or An. funestus, whilst summarising genetic variation in the primer binding sites and visualising the position of primer pairs. AnoPrimer allows the design of both genomic DNA and cDNA primers and hybridisation probes. By coupling this Python package with Google Colaboratory, AnoPrimer is an open and accessible platform for primer and probe design, hosted in the cloud for free. AnoPrimer is available here https//github.com/sanjaynagi/AnoPrimer and we hope it will be a useful resource for the community to design probe and primer sets that can be reliably deployed across the An. gambiae and funestus species ranges.

The majority of molecular biology applications require synthetic DNA sequences called primers, which bind to DNA and allow us to amplify specific stretches of DNA which we are interested in. Unfortunately, when mutations occur at primer binding sites, primers can fail to bind completely, or even worse, amplify differentially depending on the mutation present. Mutations can therefore bias molecular assays with often undetected effects. This is a particular problem in malaria mosquitoes, as they are some of the most genetically diverse species on earth. We present a user-friendly software tool, AnoPrimer, which allows users to design primers for molecular biology in malaria mosquitoes. AnoPrimer integrates high-quality whole-genome sequence data from the cloud, and creates clear, interactive visualisations, enabling users to avoid mutations that occur in wild malaria mosquitoes. By avoiding these mutations, we can ensure the design of reliable primers which result in robust molecular assays for research into malaria vectors.