SMapper: visualizing spatial prevalence data of all types, including sparse and incomplete datasets

Lynn Khellaf; Arwin Ralf; Khanh Toan Nguyen; Manfred Kayser; Michael Nothnagel

doi:10.1093/bioadv/vbad176

SMapper: visualizing spatial prevalence data of all types, including sparse and incomplete datasets

Bioinform Adv. 2023 Dec 1;3(1):vbad176. doi: 10.1093/bioadv/vbad176. eCollection 2023.

Authors

Lynn Khellaf¹, Arwin Ralf², Khanh Toan Nguyen³, Manfred Kayser², Michael Nothnagel^{1

3}

Affiliations

¹ Cologne Center for Genomics, University of Cologne, 50931 Cologne, Germany.
² Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands.
³ University Hospital Cologne, 50937 Cologne, Germany.

Abstract

Motivation: We introduce SMapper, a novel web and software tool for visualizing spatial prevalence data of all types including those suffering from incomplete geographic coverage and insufficient sample sizes. We demonstrate the benefits of our tool in overcoming interpretational issues with existing tools caused by such data limitations. We exemplify the use of SMapper by applications to human genotype and phenotype data relevant in an epidemiological, anthropological and forensic context.

Availability and implementation: A web implementation is available at https://rhodos.ccg.uni-koeln.de/smapper/. A stand-alone version, released under the GNU General Public License version 3 as published by the Free Software Foundation, is available from https://rhodos.ccg.uni-koeln.de/smapper/software-download.php as a Singularity container (https://docs.sylabs.io/guides/latest/user-guide/index.html) and a native Linux Python installation.