Annotating non-coding transcription using functional genomics strategies

Experimental annotation of the human pathogen Candida albicans coding and noncoding transcribed regions using high-resolution tiling arrays

BACKGROUND

Compared to other model organisms and despite the clinical relevance of the pathogenic yeast Candida albicans, no comprehensive analysis has been done to provide experimental support of its in silico-based genome annotation.

RESULTS

We have undertaken a genome-wide experimental annotation to accurately uncover the transcriptional landscape of the pathogenic yeast C. albicans using strand-specific high-density tiling arrays. RNAs were purified from cells growing under conditions relevant to C. albicans pathogenicity, including biofilm, lab-grown yeast and serum-induced hyphae, as well as cells isolated from the mouse caecum. This work provides a genome-wide experimental validation for a large number of predicted ORFs for which transcription had not been detected by other approaches. Additionally, we identified more than 2,000 novel transcriptional segments, including new ORFs and exons, non-coding RNAs (ncRNAs) as well as convincing cases of antisense gene transcription. We also characterized the 5' and 3' UTRs of expressed ORFs, and established that genes with long 5' UTRs are significantly enriched in regulatory functions controlling filamentous growth. Furthermore, we found that genomic regions adjacent to telomeres harbor a cluster of expressed ncRNAs. To validate and confirm new ncRNA candidates, we adapted an iterative strategy combining both genome-wide occupancy of the different subunits of RNA polymerases I, II and III and expression data. This comprehensive approach allowed the identification of different families of ncRNAs.

CONCLUSIONS

In summary, we provide a comprehensive expression atlas that covers relevant C. albicans pathogenic developmental stages in addition to the discovery of new ORF and non-coding genetic elements.

An overview of the current status of eukaryote gene prediction strategies

As sequence data continues to be generated at a logarithmic rate our dependence on effective in silico gene prediction methods is also increasing. Herein, I review the current state of eukaryote gene prediction methods; their strengths, weaknesses and future directions.