Research Article

DBCollHIV: a database system for collaborative HIV analysis in Brazil

Abstract

We developed a database system for collaborative HIV analysis (DBCollHIV) in Brazil. The main purpose of our DBCollHIV project was to develop an HIV-integrated database system with analytical bioinformatics tools that would support the needs of Brazilian research groups for data storage and sequence analysis. Whenever authorized by the principal investigator, this system also allows the integration of data from different studies and/or the release of the data to the general public. The development of a database that combines sequences associated with clinical/epidemiological data is difficult without the active support of interdisciplinary investigators. A functional database that securely stores data and helps the investigator to manipulate their sequences before publication would be an attractive tool for investigators depositing their data and collaborating with other groups. DBCollHIV allows investigators to manipulate their own datasets, as well as integrating molecular and clinical HIV data, in an innovative fashion.

We developed a database system for collaborative HIV analysis (DBCollHIV) in Brazil. The main purpose of our DBCollHIV project was to develop an HIV-integrated database system with analytical bioinformatics tools that would support the needs of Brazilian research groups for data storage and sequence analysis. Whenever authorized by the principal investigator, this system also allows the integration of data from different studies and/or the release of the data to the general public. The development of a database that combines sequences associated with clinical/epidemiological data is difficult without the active support of interdisciplinary investigators. A functional database that securely stores data and helps the investigator to manipulate their sequences before publication would be an attractive tool for investigators depositing their data and collaborating with other groups. DBCollHIV allows investigators to manipulate their own datasets, as well as integrating molecular and clinical HIV data, in an innovative fashion.

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