Prider - multiplexed primer design using linearly scaling approximation of set coverage

Abstract

Designing oligonucleotide primers and probes is one of the key steps of various laboratory experiments such as multiplexed PCR or digital multiplexed ligation assays. When designing primers and probes to complex, heterogeneous DNA data sets, an optimization problem arises where the smallest number of oligonucleotides covering the largest diversity of the input dataset needs to be identified. Tools that provide this optimization in an efficient manner for large input data are currently lacking. Here we present Prider, an R package for designing primers and probes with a nearly optimal coverage for complex and large sequence sets. Prider initially prepares a full primer coverage of the input sequences, the complexity of which is subsequently reduced by removing components of high redundancy or narrow coverage. The primers from the resulting near-optimal coverage are easily accessible as data frames and their coverage across the input sequences can be visualized as heatmaps using Priders plotting function. Prider scales linearly to increasing sequence data and therefore permits efficient design of primers to large and highly diverse DNA datasets. Prider is available on GitHub under the permissive BSD-3-clause license: https://github.com/manutamminen/prider