DeORFanizing Candida albicans Genes using Coexpression.

Functional characterization of open reading frames in nonmodel organisms, such as the common opportunistic fungal pathogen , can be labor-intensive. To meet this challenge, we built a comprehensive and unbiased coexpression network for , which we call CalCEN, from data collected from 853 RNA sequencing runs from 18 large-scale studies deposited in the NCBI Sequence Read Archive. Retrospectively, CalCEN is highly predictive of known gene function annotations and can be synergistically combined with sequence similarity and interaction networks in through orthology for additional accuracy in gene function prediction. To prospectively demonstrate the utility of the coexpression network in , we predicted the function of underannotated open reading frames (ORFs) and identified as a novel cell cycle regulator in This study provides a tool for future systems biology analyses of gene function in We provide a computational pipeline for building and analyzing the coexpression network and CalCEN itself at http://github.com/momeara/CalCEN is a common and deadly fungal pathogen of humans, yet the genome of this organism contains many genes of unknown function. By determining gene function, we can help identify essential genes, new virulence factors, or new regulators of drug resistance, and thereby give new targets for antifungal development. Here, we use information from large-scale RNA sequencing (RNAseq) studies and generate a coexpression network (CalCEN) that is robust and able to predict gene function. We demonstrate the utility of this network in both retrospective and prospective testing and use CalCEN to predict a role for C4_06590W/ in cell cycle. This tool will allow for a better characterization of underannotated genes in pathogenic yeasts.