fourSynergy: Ensemble-based interaction calling on 4C-seq data using gradient-free optimization

Abstract

MotivationChromatin organization plays a crucial role in gene regulation and is associated with various severe diseases like cancer. Since chromatin changes are potentially reversible, a deeper understanding of the alterations needs to be harnessed for the development of new therapies. Circular Chromosome Conformation Capture Sequencing (4C-seq) is a sequencing technique enabling the identification of chromatin interactions between genes and regulatory elements. This work aims to develop an ensemble algorithm that utilizes synergies among available 4C-seq tools, which in turn allows to achieve superior predictive performance in interaction calling. ResultsWe employed existing 4C-seq algorithms using a weighted-voting approach. By optimizing the tool weights according to various predictive metrics using gradient-free optimization strategies, we demonstrate the potential of combining multiple 4C-seq analysis tools for interaction calling. Our results indicate that a weighted-voting based ensemble approach can outperform individual algorithms in various datasets. Although the optimal solutions differ across the 4C-seq datasets, we successfully identified global solutions that outperform the individual algorithms for all datasets analyzed. Availabilityhttps://github.com/sophiewind/fourSynergy, https://github.com/sophiewind/fourSynergy_pip Contactsophie.wind@uni-muenster.de Supplementary informationSupplementary data are available at Journal Name online.