RegCFinder: targeted discovery of genomic subregions with differential read density

Weiß, Elena ; Friedel, Caroline C (2023): RegCFinder: targeted discovery of genomic subregions with differential read density. Bioinformatics Advances, 3 (1). ISSN 2635-0041

Abstract

Motivation
To date, no methods are available for the targeted identification of genomic subregions with differences in sequencing read distributions between two conditions. Existing approaches either only determine absolute read number changes, require predefined subdivisions of input windows or average across multiple genes.

Results
Here, we present RegCFinder, which automatically identifies subregions of input windows with differences in read density between two conditions. For this purpose, the problem is defined as an instance of the all maximum scoring subsequences problem, which can be solved in linear time. Subsequently, statistical significance and differential usage of identified subregions are determined with DEXSeq. RegCFinder allows flexible definition of input windows to target the analysis to any regions of interests, e.g. promoters, gene bodies, peak regions and more. Furthermore, any type of sequencing assay can be used as input; thus, RegCFinder lends itself to a wide range of applications. We illustrate the usefulness of RegCFinder on two applications, where we can both confirm previous results and identify interesting gene subgroups with distinctive changes in read distributions.

Availability and implementation
RegCFinder is implemented as a workflow for the workflow management system Watchdog and available at: https://github.com/watchdog-wms/watchdog-wms-workflows/

Supplementary information
Supplementary data are available at Bioinformatics Advances online.

• Dokumententyp: Artikel (LMU)
• Organisationseinheit (Fakultäten): 16 Mathematik, Informatik und Statistik > Informatik
• DFG-Fachsystematik der Wissenschaftsbereiche: Ingenieurwissenschaften
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 443644894
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892