Literature DB >> 11752336

Olfactory Receptor Database: a metadata-driven automated population from sources of gene and protein sequences.

Chiquito Crasto1, Luis Marenco, Perry Miller, Gordon Shepherd.   

Abstract

The Olfactory Receptor Database (ORDB; http://senselab.med.yale.edu/senselab/ordb) is a central repository of olfactory receptor (OR) and olfactory receptor-like gene and protein sequences. To deal with the very large OR gene family, we have constructed an algorithm that automatically downloads sequences from web sources such as GenBank and SWISS-PROT into the database. The algorithm uses hypertext markup language (HTML) parsing techniques that extract information relevant to ORDB. The information is then correlated with the metadata in the ORDB knowledge base to encode the unstructured text extracted into the structured format compliant with the database architecture, entity attribute value with classes and relationship (EAV/CR), which supports the SenseLab project as a whole. Three population methods: batch, automatic and semi-automatic population are discussed. The data is imported into the database using extensible markup language (XML).

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Substances:

Year:  2002        PMID: 11752336      PMCID: PMC99065          DOI: 10.1093/nar/30.1.354

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  24 in total

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2.  Olfactory receptor database: a sensory chemoreceptor resource.

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3.  The olfactory receptor gene repertoire in primates and mouse: evidence for reduction of the functional fraction in primates.

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4.  Initial sequencing and analysis of the human genome.

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Journal:  Nature       Date:  2001-02-15       Impact factor: 49.962

5.  WebEAV: automatic metadata-driven generation of web interfaces to entity-attribute-value databases.

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Review 6.  Mouse as the measure of man?

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Review 7.  Seven-transmembrane proteins as odorant and chemosensory receptors.

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10.  The sequence of the human genome.

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  33 in total

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2.  Neuroanatomical term generation and comparison between two terminologies.

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3.  Text mining neuroscience journal articles to populate neuroscience databases.

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