Peak-calling Algorithms (WonderPeaks and PeakStream) as Tools for Improved ChIP-seq and Transcriptomic Analysis in Fungal Pathogens

Characterizing changes in gene expression through transcriptomics and transcription regulator activity has become a fundamental approach for understanding the diverse responses involved in fungal pathogenesis. This paper introduces two computational tools designed to address key challenges in the study of transcriptional regulation in fungal pathogens, particularly non-model with limited genomic annotation. First, we present WonderPeaks, a novel peak-calling algorithm that leverages the first derivative of mapped genomic data from next-generation sequencing (NGS) experiments to identify enriched peaks in Chromatin ImmunoPrecipitation followed by sequencing (ChIP-seq). Second, we introduce PeakStream, an extension of WonderPeaks for annotating 3' untranslated regions (UTRs) in transcriptomic data generated using poly(A)-primed library preparation. Together, these tools provide an end-to-end data analysis pipeline, offering a user-friendly solution for researchers studying transcription regulation in fungi. We demonstrate their effectiveness with data from the fungal pathogen Candida albicans, successfully identifying verified peaks in ChIP-seq data and annotating validated UTRs through comparison with total RNA sequencing data under the same conditions. We also discuss the limitations of WonderPeaks for ChIP-seq data compared to current state-of-the-art methods and propose directions for future improvements. Ultimately, this work provides practical guidance and powerful resources for studying transcriptional regulation, with immediate relevance to pathogenic fungi and potential applications in broader genomic studies.