DeORFanizing Candida albicans Genes using Coexpression.

Area of Science:

• Mycology and Infectious Diseases
• Systems Biology and Bioinformatics
• Genomics and Molecular Biology

Background:

• The opportunistic fungal pathogen *Candida albicans* has many genes with unknown functions, hindering research into essential genes, virulence, and drug resistance.
• Functional characterization of genes in nonmodel organisms like *C. albicans* is often labor-intensive and requires efficient tools.

Purpose of the Study:

• To construct a comprehensive and unbiased coexpression network for *C. albicans*, named CalCEN, to facilitate gene function prediction.
• To demonstrate the utility of CalCEN for identifying novel gene functions, particularly for underannotated open reading frames (ORFs).
• To provide a computational tool and resource for systems biology analyses in *C. albicans*.

Main Methods:

• Compiled a coexpression network (CalCEN) using data from 853 RNA sequencing runs from 18 large-scale studies of *C. albicans*.
• Validated CalCEN's predictive power retrospectively against known gene annotations and synergistically with orthology-based networks from *Saccharomyces cerevisiae*.
• Prospectively tested CalCEN by predicting functions of underannotated ORFs, leading to the identification of *CCJ1*.

Main Results:

• CalCEN demonstrated high accuracy in predicting known gene functions.
• Integration with *S. cerevisiae* networks via orthology further enhanced gene function prediction accuracy.
• CalCEN successfully identified *CCJ1* (C4_06590W) as a novel regulator of the cell cycle in *C. albicans*.

Conclusions:

• The CalCEN coexpression network is a robust and valuable tool for systems biology and functional genomics in *C. albicans*.
• CalCEN facilitates the characterization of underannotated genes, potentially revealing new targets for antifungal drug development.
• The study provides a computational pipeline and the CalCEN resource for broader research use.