CalCEN

CalCEN (Comprehensive and unbiased coexpression network) was developed explicitly for Candida albicans, a common opportunistic fungal pathogen. This tool was created to address the challenge of functional characterization of open reading frames (ORFs) in nonmodel organisms, which can often be labor-intensive and complex. By leveraging the power of coexpression analysis, CalCEN provides a robust platform for predicting gene function, thereby facilitating deeper biological insights into this pathogen.

The development of CalCEN involved an extensive analysis of 853 RNA sequencing runs sourced from 18 large-scale studies in the NCBI Sequence Read Archive. Notably, the predictive accuracy of CalCEN is further enhanced when combined with sequence similarity and interaction networks from Saccharomyces cerevisiae, utilizing orthology relationships for refined gene function predictions.

An essential demonstration of CalCEN's utility was its ability to predict the function of unannotated ORFs within the C. albicans genome. One significant finding from this application was the identification of CCJ1 as a novel cell cycle regulator in C. albicans. This discovery underscores the potential of CalCEN to uncover new biological insights and contribute to understanding essential genes, virulence factors, and regulators of drug resistance in C. albicans.