Sequence-based pangenomic core detection

Tizian Schulz; Roland Wittler; Jens Stoye

doi:10.1016/j.isci.2022.104413

Sequence-based pangenomic core detection

iScience. 2022 May 16;25(6):104413. doi: 10.1016/j.isci.2022.104413. eCollection 2022 Jun 17.

Authors

Tizian Schulz^{1

2

3}, Roland Wittler^{1

2}, Jens Stoye^{1

2}

Affiliations

¹ Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany.
² Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Bielefeld University, Bielefeld, Germany.
³ Graduate School "Digital Infrastructure for the Life Sciences" (DILS), Bielefeld University, Bielefeld, Germany.

Abstract

One of the most basic kinds of analysis to be performed on a pangenome is the detection of its core, i.e., the information shared among all members. Pangenomic core detection is classically done on the gene level and many tools focus exclusively on core detection in prokaryotes. Here, we present a new method for sequence-based pangenomic core detection. Our model generalizes from a strict core definition allowing us to flexibly determine suitable core properties depending on the research question and the dataset under consideration. We propose an algorithm based on a colored de Bruijn graph that runs in linear time with respect to the number of k-mers in the graph. An implementation of our method is called Corer. Because of the usage of a colored de Bruijn graph, it works alignment-free, is provided with a small memory footprint, and accepts as input assembled genomes as well as sequencing reads.

Keywords: Bioinformatics; Genomics.