The use of Open Reading frame ESTs (ORESTES) for analysis of the honey bee transcriptome

Nunes, Francis M. F.; Valente, Valeria; Sousa, Josane F.; Cunha, Marco A. V.; Pinheiro, Daniel G.; Maia, Rafaela M.; Araujo, Daniela D.; Costa, Maria C. R.; Martins, Waleska K.; Carvalho, Alex F.; Monesi, Nadia; Nascimento, Adriana M.; Peixoto, Pablo M. V.; Silva, Maria F. R.; Ramos, Ricardo G. P.; Reis, Luis F. L.; Dias-Neto, Emmanuel; Souza, Sandro J.; Simpson, Andrew J. G.; Zago, Marco A.
January 2004
BMC Genomics;2004, Vol. 5, p84
Academic Journal
Background: The ongoing efforts to sequence the honey bee genome require additional initiatives to define its transcriptome. Towards this end, we employed the Open Reading frame ESTs (ORESTES) strategy to generate profiles for the life cycle of Apis mellifera workers. Results: Of the 5,021 ORESTES, 35.2% matched with previously deposited Apis ESTs. The analysis of the remaining sequences defined a set of putative orthologs whose majority had their best-match hits with Anopheles and Drosophila genes. CAP3 assembly of the Apis ORESTES with the already existing 15,500 Apis ESTs generated 3,408 contigs. BLASTX comparison of these contigs with protein sets of organisms representing distinct phylogenetic clades revealed a total of 1,629 contigs that Apis mellifera shares with different taxa. Most (41%) represent genes that are in common to all taxa, another 21% are shared between metazoans (Bilateria), and 16% are shared only within the Insecta clade. A set of 23 putative genes presented a best match with human genes, many of which encode factors related to cell signaling/signal transduction. 1,779 contigs (52%) did not match any known sequence. Applying a correction factor deduced from a parallel analysis performed with Drosophila melanogaster ORESTES, we estimate that approximately half of these no-match ESTs contigs (22%) should represent Apis-specific genes. Conclusions: The versatile and cost-efficient ORESTES approach produced minilibraries for honey bee life cycle stages. Such information on central gene regions contributes to genome annotation and also lends itself to cross-transcriptome comparisons to reveal evolutionary trends in insect genomes.


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