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Unlocking the potential of survival data for model organisms through a new database and online analysis platform: SurvCurv.

Ziehm M, Thornton JM - Aging Cell (2013)

Bottom Line: Understanding the biology of aging is highly desirable because of the benefits for the wide range of aging-related diseases.The database, available at www.ebi.ac.uk/thornton-srv/databases/SurvCurv/, offers various functions including plotting, Cox proportional hazards analysis, mathematical mortality models and statistical tests.It facilitates reanalysis and allows users to analyse their own data and compare it with the largest repository of model-organism data from published experiments, thus unlocking the potential of survival data and demographics in model organisms.

View Article: PubMed Central - PubMed

Affiliation: EMBL - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.

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(A) Screenshot of the front page of the SurvCurv Website (B) Screenshot of a detailed cohort information display example.
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fig01: (A) Screenshot of the front page of the SurvCurv Website (B) Screenshot of a detailed cohort information display example.

Mentions: The new database and online resource SurvCurv is accessible through the web interface at http://www.ebi.ac.uk/thornton-srv/databases/SurvCurv/ (Fig. 1). The web interface enables the user to browse the database content, analyse it online, download data sets for offline analysis, and to submit data to the database. In addition, the direct analysis interface allows upload of the user's own survival data for analysis and visualisation. These data are transferred to the SurvCurv server via a secure internet connection and only used for the selected analyses. They are not permanently stored, made available to anybody else or included in the database. If the user would like to submit data after having analysed them, this has to be carried out separately via the submission page and the author will be contacted to confirm release before making any submitted data available through the database. Statistical descriptors and tests can be directly computed on user data, and data available through the database can be incorporated in any analysis. The database content can be browsed or searched by a simple text box search as well as an advanced search for specific fields. Identifiers as well as plain-text descriptors can be searched using the simple search feature. Each cohort of animals has been manually curated and annotated with a short name, an optional longer description including remarks, a reference (usually a publication), the species, strain and gender, the experimental conditions and treatments applied as well as its relationship with other cohorts. Furthermore, precalculated descriptive statistics and parameters of six common mathematical mortality models, detailed under Statistical Features below, are provided (Fig. 1B). Besides the annotated related cohorts, the database content can be explored based on various similarity measures through the similarity search available in the ‘details’ section of each cohort. Similarity can be based on any descriptive statistical measure displayed, such as the median or the parameters of the fitted mathematical models. This explorative tool enables users to find other cohorts, similar with respect to the property of interest and can lead to the discovery of unexpected connections. These can prompt a more detailed analysis and even further experiments.


Unlocking the potential of survival data for model organisms through a new database and online analysis platform: SurvCurv.

Ziehm M, Thornton JM - Aging Cell (2013)

(A) Screenshot of the front page of the SurvCurv Website (B) Screenshot of a detailed cohort information display example.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3824079&req=5

fig01: (A) Screenshot of the front page of the SurvCurv Website (B) Screenshot of a detailed cohort information display example.
Mentions: The new database and online resource SurvCurv is accessible through the web interface at http://www.ebi.ac.uk/thornton-srv/databases/SurvCurv/ (Fig. 1). The web interface enables the user to browse the database content, analyse it online, download data sets for offline analysis, and to submit data to the database. In addition, the direct analysis interface allows upload of the user's own survival data for analysis and visualisation. These data are transferred to the SurvCurv server via a secure internet connection and only used for the selected analyses. They are not permanently stored, made available to anybody else or included in the database. If the user would like to submit data after having analysed them, this has to be carried out separately via the submission page and the author will be contacted to confirm release before making any submitted data available through the database. Statistical descriptors and tests can be directly computed on user data, and data available through the database can be incorporated in any analysis. The database content can be browsed or searched by a simple text box search as well as an advanced search for specific fields. Identifiers as well as plain-text descriptors can be searched using the simple search feature. Each cohort of animals has been manually curated and annotated with a short name, an optional longer description including remarks, a reference (usually a publication), the species, strain and gender, the experimental conditions and treatments applied as well as its relationship with other cohorts. Furthermore, precalculated descriptive statistics and parameters of six common mathematical mortality models, detailed under Statistical Features below, are provided (Fig. 1B). Besides the annotated related cohorts, the database content can be explored based on various similarity measures through the similarity search available in the ‘details’ section of each cohort. Similarity can be based on any descriptive statistical measure displayed, such as the median or the parameters of the fitted mathematical models. This explorative tool enables users to find other cohorts, similar with respect to the property of interest and can lead to the discovery of unexpected connections. These can prompt a more detailed analysis and even further experiments.

Bottom Line: Understanding the biology of aging is highly desirable because of the benefits for the wide range of aging-related diseases.The database, available at www.ebi.ac.uk/thornton-srv/databases/SurvCurv/, offers various functions including plotting, Cox proportional hazards analysis, mathematical mortality models and statistical tests.It facilitates reanalysis and allows users to analyse their own data and compare it with the largest repository of model-organism data from published experiments, thus unlocking the potential of survival data and demographics in model organisms.

View Article: PubMed Central - PubMed

Affiliation: EMBL - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.

Show MeSH