Bernhard Haubold

January 12, 2022

Please refer to and contact Bernhard <> for further information on the project.

Marker Development from Whole Genome Sequences

Similarity searches as implemented in programs like blast are a staple of molecular biology and bioinformatics. The converse, dissimilarity searches, are less well understood. In dissimilarity searches we look for the regions that distinguish a sample of target genomes from all other genomes. Such regions can then be used to design diagnostic PCR markers.

One approach to dissimilarity searching is to compare regions commen to the targets to all known sequences using blast, and keeping the regions not hit. This “search against everything” strategy can be quite time consuming. It might also not be necessary. Together with colleagues from the Lübeck diagnotics company Euroimmun, we recently developed an alternative approach that relies on a comparision to the closest phylogenetic relatives, which we call neighbors. Any target region not found in the neighbors is likely to be diagnostic. We implemented this idea in the program fur for Find Unique genomic Regions. Fur is fast, whole bacterial genomes can be analyzed in seconds, and the primers it proposes are highly specific and sensitive when tested in the lab (Haubold et al.,

So far, users of fur needs to known which genomes might make useful neighbors. The aim of this project is to automate the search for neighbors. This project is suitable for biologists with good computing skills and for computer scientists enthusiastic about genomics.

B. Haubold, F. Klötzl, L. Hellberg, D. Thompson, and M. Cavalar. Fur: Find unique genomic regions for diagnostic PCR. Bioinformatics, /btab059, 2021.

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