Marker discovery in the large

• Published in: Bioinformatics advances 2024, Vol.4 (1), p.vbae113
• Main Authors: Vieira Mourato, Beatriz, Tsers, Ivan, Denker, Svenja, Klötzl, Fabian, Haubold, Bernhard
• Format: Article
• Language: English
• ISSN: 2635-0041; 2635-0041
• DOI: 10.1093/bioadv/vbae113

Markers for diagnostic polymerase chain reactions are routinely constructed by taking regions common to the genomes of a target organism and subtracting the regions found in the targets' closest relatives, their neighbors. This approach is implemented in the published package Fur, which originally required memory proportional to the number of nucleotides in the neighborhood. This does not scale well. Here, we describe a new version of Fur that only requires memory proportional to the longest neighbor. In spite of its greater memory efficiency, the new Fur remains fast and is accurate. We demonstrate this by applying it to simulated sequences and comparing it to an efficient alternative. Then we use the new Fur to extract markers from 120 reference bacteria. To make this feasible, we also introduce software for automatically finding target and neighbor genomes and for assessing markers. We pick the best primers from the 10 most sequenced reference bacteria and show their excellent sensitivity and specificity. Fur is available from github.com/evolbioinf/fur, in the Docker image hub.docker.com/r/beatrizvm/mapro, and in the Code Ocean capsule 10.24433/CO.7955947.v1.