This article is part of the supplement: Validation methods for functional genome annotation

Open Access Research

Genome-wide functional annotation and structural verification of metabolic ORFeome of Chlamydomonas reinhardtii

Lila Ghamsari12, Santhanam Balaji12, Yun Shen12, Xinping Yang12, Dawit Balcha12, Changyu Fan12, Tong Hao12, Haiyuan Yu3*, Jason A Papin4* and Kourosh Salehi-Ashtiani125*

Author Affiliations

1 Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA

2 Department of Genetics, Harvard Medical School, Boston, MA 02115, USA

3 Department of Biological Statistics and Computational Biology and Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA

4 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA

5 New York University Abu Dhabi, Abu Dhabi, UAE, and Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA

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BMC Genomics 2011, 12(Suppl 1):S4  doi:10.1186/1471-2164-12-S1-S4

Published: 15 June 2011



Recent advances in the field of metabolic engineering have been expedited by the availability of genome sequences and metabolic modelling approaches. The complete sequencing of the C. reinhardtii genome has made this unicellular alga a good candidate for metabolic engineering studies; however, the annotation of the relevant genes has not been validated and the much-needed metabolic ORFeome is currently unavailable. We describe our efforts on the functional annotation of the ORF models released by the Joint Genome Institute (JGI), prediction of their subcellular localizations, and experimental verification of their structural annotation at the genome scale.


We assigned enzymatic functions to the translated JGI ORF models of C. reinhardtii by reciprocal BLAST searches of the putative proteome against the UniProt and AraCyc enzyme databases. The best match for each translated ORF was identified and the EC numbers were transferred onto the ORF models. Enzymatic functional assignment was extended to the paralogs of the ORFs by clustering ORFs using BLASTCLUST.

In total, we assigned 911 enzymatic functions, including 886 EC numbers, to 1,427 transcripts. We further annotated the enzymatic ORFs by prediction of their subcellular localization. The majority of the ORFs are predicted to be compartmentalized in the cytosol and chloroplast. We verified the structure of the metabolism-related ORF models by reverse transcription-PCR of the functionally annotated ORFs. Following amplification and cloning, we carried out 454FLX and Sanger sequencing of the ORFs. Based on alignment of the 454FLX reads to the ORF predicted sequences, we obtained more than 90% coverage for more than 80% of the ORFs. In total, 1,087 ORF models were verified by 454 and Sanger sequencing methods. We obtained expression evidence for 98% of the metabolic ORFs in the algal cells grown under constant light in the presence of acetate.


We functionally annotated approximately 1,400 JGI predicted metabolic ORFs that can facilitate the reconstruction and refinement of a genome-scale metabolic network. The unveiling of the metabolic potential of this organism, along with structural verification of the relevant ORFs, facilitates the selection of metabolic engineering targets with applications in bioenergy and biopharmaceuticals. The ORF clones are a resource for downstream studies.