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An annotated catalogue of salivary gland transcripts in the adult female mosquito, Aedes aegypti.

José M C Ribeiro | Bruno Arcà | Fabrizio Lombardo | Eric Calvo | Van My Phan | Prafulla K Chandra | Stephen K Wikel
BMC genomics | 2007

Saliva of blood-sucking arthropods contains a cocktail of antihemostatic agents and immunomodulators that help blood feeding. Mosquitoes additionally feed on sugar meals and have specialized regions of their glands containing glycosidases and antimicrobials that might help control bacterial growth in the ingested meals. To expand our knowledge on the salivary cocktail of AEdes aegypti, a vector of dengue and yellow fevers, we analyzed a set of 4,232 expressed sequence tags from cDNA libraries of adult female mosquitoes.

Pubmed ID: 17204158

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Associated grants

  • Agency: Intramural NIH HHS, United States

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Intramural Research Program (tool)

RRID:SCR_012734

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SMART (tool)

RRID:SCR_005026

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Bioinformatic Harvester IV (beta) at Karlsruhe Institute of Technology (tool)

RRID:SCR_008017

Harvester is a Web-based tool that bulk-collects bioinformatic data on human proteins from various databases and prediction servers. It is a meta search engine for gene and protein information. It searches 16 major databases and prediction servers and combines the results on pregenerated HTML pages. In this way Harvester can provide comprehensive gene-protein information from different servers in a convenient and fast manner. As full text meta search engine, similar to Google trade mark, Harvester allows screening of the whole genome proteome for current protein functions and predictions in a few seconds. With Harvester it is now possible to compare and check the quality of different database entries and prediction algorithms on a single page. Sponsors: This work has been supported by the BMBF with grants 01GR0101 and 01KW0013.

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TreeView (tool)

RRID:SCR_013503

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SignalP (tool)

RRID:SCR_015644

Web application for prediction of the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.

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