CalCEN

CalCEN constructs a coexpression network from RNA sequencing data to facilitate functional characterization and prediction of open reading frames (ORFs) in Candida albicans.


Key Features:

  • Data Integration: Constructed from 853 RNA sequencing runs across 18 large-scale studies deposited in the NCBI Sequence Read Archive (SRA).
  • Unbiased Network Construction: Builds a coexpression network that captures gene expression patterns without preconceived assumptions about gene function.
  • Retrospective Validation: Demonstrates high predictive accuracy for known gene function annotations in Candida albicans.
  • Cross-species Network Integration: Can be combined with sequence similarity and interaction networks from Saccharomyces cerevisiae via orthologous relationships to improve function prediction.
  • Functional Prediction: Applied prospectively to predict functions for underannotated ORFs, including identifying CCJ1 as a novel cell cycle regulator in Candida albicans.
  • Research Applications: Supports identification of essential genes, potential virulence factors, and regulators of drug resistance in Candida albicans.

Scientific Applications:

  • Gene function annotation: Predicts functions for underannotated ORFs in Candida albicans using coexpression relationships.
  • Comparative functional transfer: Enhances annotation by integrating sequence similarity and interaction data from Saccharomyces cerevisiae through orthology.
  • Pathogenesis and drug-resistance research: Aids identification of genes involved in virulence and regulators of antifungal drug resistance to inform target discovery.
  • Functional genomics: Provides a genome-scale resource for hypothesis generation and validation in Candida albicans functional studies.

Methodology:

Collected 853 RNA-seq runs from 18 studies in the NCBI SRA, constructed an unbiased coexpression network, integrated sequence similarity and interaction networks from Saccharomyces cerevisiae via orthology, and performed retrospective and prospective function prediction analyses (including CCJ1).

Topics

Details

Tool Type:
command-line tool, workflow
Programming Languages:
R
Added:
3/19/2021
Last Updated:
4/21/2021

Operations

Publications

O’Meara TR, O’Meara MJ. DeORFanizing Candida albicans Genes using Coexpression. mSphere. 2021;6(1). doi:10.1128/msphere.01245-20. PMID:33472984. PMCID:PMC7845621.