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Genome Biol. 2010;11(8):R85. doi: 10.1186/gb-2010-11-8-r85. Epub 2010 Aug 25.

Digital expression profiling of novel diatom transcripts provides insight into their biological functions.

Author information

1
Institut de Biologie de l'Ecole Normale Supérieure, CNRS UMR 8197 INSERM U1024, Ecole Normale Supérieure, 46 rue d'Ulm, 75005 Paris, France. uma@ebi.ac.uk

Abstract

BACKGROUND:

Diatoms represent the predominant group of eukaryotic phytoplankton in the oceans and are responsible for around 20% of global photosynthesis. Two whole genome sequences are now available. Notwithstanding, our knowledge of diatom biology remains limited because only around half of their genes can be ascribed a function based onhomology-based methods. High throughput tools are needed, therefore, to associate functions with diatom-specific genes.

RESULTS:

We have performed a systematic analysis of 130,000 ESTs derived from Phaeodactylum tricornutum cells grown in 16 different conditions. These include different sources of nitrogen, different concentrations of carbon dioxide, silicate and iron, and abiotic stresses such as low temperature and low salinity. Based on unbiased statistical methods, we have catalogued transcripts with similar expression profiles and identified transcripts differentially expressed in response to specific treatments. Functional annotation of these transcripts provides insights into expression patterns of genes involved in various metabolic and regulatory pathways and into the roles of novel genes with unknown functions. Specific growth conditions could be associated with enhanced gene diversity, known gene product functions, and over-representation of novel transcripts. Comparative analysis of data from the other sequenced diatom, Thalassiosira pseudonana, helped identify several unique diatom genes that are specifically regulated under particular conditions, thus facilitating studies of gene function, genome annotation and the molecular basis of species diversity.

CONCLUSIONS:

The digital gene expression database represents a new resource for identifying candidate diatom-specific genes involved in processes of major ecological relevance.

PMID:
20738856
PMCID:
PMC2945787
DOI:
10.1186/gb-2010-11-8-r85
[Indexed for MEDLINE]
Free PMC Article
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