BIOMAPP::CHIP: Large-Scale Motif Analysis

Abstract

AbstractBackgroundDiscovery biological motifs plays a fundamental role in understanding regulatory mechanisms. Computationally, they can be efficiently represented askmers, making the counting of these elEMents a critical aspect for ensuring not only the accuracy but also the efficiency of the analytical process. This is particularly useful in scenarios involving large data volumes, such as those generated by theChIP-seqprotocol. Against this backdrop, we introducebiomapp ::chip, a tool specifically designed to optimize the discovery of biological motifs in large data volumes.ResultsWe conducted a comprehensive set of comparative tests with state-of-the-art algorithms. Our analyses revealed thatbiomapp ::chipoutperforms existing approaches in various metrics, excelling both in terms of performance and accuracy. The tests demonstrated a higher detection rate of significant motifs and also greater agility in the execution of the algorithm. Furthermore, thesmtcomponent played a vital role in the system’s efficiency, proving to be both agile and accurate inkmercounting, which in turn improved the overall efficacy of our tool.Conclusionbiomapp ::chiprepresent real advancements in the discovery of biological motifs, particularly in large data volume scenarios, offering a relevant alternative for the analysis ofChIP-seqdata and have the potential to boost future research in the field. This software can be found at the following address:https://github.com/jadermcg/BIOMAPP-CHIP.