Database Commons a catalog of biological databases

Database Commons - PhenoDigm

PhenoDigm

Citations: 44

z-index 7.13

Short name PhenoDigm
Full name PHENOtype comparisons for DIsease and Gene Models
Description Model organisms represent a valuable resource for the characterisation as well as identification of disease-gene associations, especially where the molecular basis is unknown and there is no clue to the candidate gene's function, pathway involvement or expression pattern. To systematically apply this methodology, PhenoDigm uses a semantic approach to map between clinical features observed in humans and mouse and zebrafish phenotype annotations.
URL http://www.sanger.ac.uk/resources/databases/phenodigm/
Year founded 2013
Last update & version 2013-05-09    v1.0
Availability Free to all users
University/Institution hosted Wellcome Trust Sanger Institute
Address Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
City Cambridge
Province/State Cambridgeshire
Country/Region United Kingdom
Contact name Damian Smedley
Contact email ds5@sanger.ac.uk
Data type(s)
Major organism(s)
Keyword(s)
  • gene-disease association
  • high-throughput phenotyping
  • phenotype
Publication(s)
  • PhenoDigm: analyzing curated annotations to associate animal models with human diseases. [PMID: 23660285]

    Damian Smedley, Anika Oellrich, Sebastian Köhler, Barbara Ruef, null null, Monte Westerfield, Peter Robinson, Suzanna Lewis, Christopher Mungall
    Database : the journal of biological databases and curation 2013:2013
    44 Citations (Google Scholar as of 2016-01-29)

    Abstract: The ultimate goal of studying model organisms is to translate what is learned into useful knowledge about normal human biology and disease to facilitate treatment and early screening for diseases. Recent advances in genomic technologies allow for rapid generation of models with a range of targeted genotypes as well as their characterization by high-throughput phenotyping. As an abundance of phenotype data become available, only systematic analysis will facilitate valid conclusions to be drawn from these data and transferred to human diseases. Owing to the volume of data, automated methods are preferable, allowing for a reliable analysis of the data and providing evidence about possible gene-disease associations. Here, we propose Phenotype comparisons for DIsease Genes and Models (PhenoDigm), as an automated method to provide evidence about gene-disease associations by analysing phenotype information. PhenoDigm integrates data from a variety of model organisms and, at the same time, uses several intermediate scoring methods to identify only strongly data-supported gene candidates for human genetic diseases. We show results of an automated evaluation as well as selected manually assessed examples that support the validity of PhenoDigm. Furthermore, we provide guidance on how to browse the data with PhenoDigm's web interface and illustrate its usefulness in supporting research. Database URL: http://www.sanger.ac.uk/resources/databases/phenodigm

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Accessibility

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200 OK2018-02-27
200 OK2018-02-23
200 OK2018-02-20
200 OK2018-02-16
200 OK2018-02-13
200 OK2018-02-09
200 OK2018-02-06
200 OK2018-02-02
200 OK2018-01-30
200 OK2018-01-26
200 OK2018-01-23
200 OK2018-01-19
200 OK2018-01-16
200 OK2018-01-12
200 OK2018-01-09
200 OK2018-01-05
200 OK2018-01-02
200 OK2017-12-29
200 OK2017-12-26
200 OK2017-12-22
200 OK2017-12-19
200 OK2017-12-15
200 OK2017-12-12
200 OK2017-12-08
200 OK2017-12-05
200 OK2017-12-01
200 OK2017-11-28
200 OK2017-11-24
200 OK2017-11-21
200 OK2017-11-17
200 OK2017-11-14
200 OK2017-11-10
200 OK2017-11-07
200 OK2017-11-03
200 OK2017-10-31
200 OK2017-10-27
200 OK2017-10-24
200 OK2017-10-20
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200 OK2017-10-13
200 OK2017-10-10
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200 OK2017-10-03
200 OK2017-09-29
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200 OK2017-09-19
200 OK2017-09-15
200 OK2017-09-12
200 OK2017-09-08
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200 OK2017-02-07
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200 OK2017-01-31
200 OK2017-01-27
200 OK2017-01-24
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200 OK2017-01-17
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200 OK2017-01-10
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200 OK2016-12-02
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200 OK2016-09-27
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200 OK2016-04-29
200 OK2016-04-26
200 OK2016-04-22
200 OK2016-04-19
200 OK2016-04-15
200 OK2016-04-12
200 OK2016-04-08
200 OK2016-04-05
200 OK2016-04-01
200 OK2016-03-29
200 OK2016-03-28
200 OK2016-03-25
200 OK2016-03-23
200 OK2016-03-21
200 OK2016-03-18
200 OK2016-03-16
200 OK2016-03-14
200 OK2016-03-11
200 OK2016-03-09
200 OK2016-03-07
200 OK2016-03-04
200 OK2016-03-02
200 OK2016-02-29
200 OK2016-02-26
200 OK2016-02-24
200 OK2016-02-22
200 OK2016-02-19
200 OK2016-02-17
200 OK2016-02-15
200 OK2016-02-14
200 OK2016-02-12
200 OK2016-02-10
200 OK2016-02-08
200 OK2016-02-07
200 OK2016-02-05
200 OK2016-02-03
200 OK2016-02-01
200 OK2016-01-31
200 OK2016-01-29
200 OK2016-01-27
200 OK2016-01-25
200 OK2016-01-24
200 OK2016-01-22
200 OK2016-01-20
200 OK2016-01-18
200 OK2016-01-17
200 OK2016-01-15
200 OK2016-01-13
200 OK2016-01-11
200 OK2016-01-10
200 OK2016-01-08
200 OK2016-01-06
200 OK2016-01-04

Tags

Disease Expression Phenotype
Danio rerio Homo sapiens Mus musculus
gene-disease association high-throughput phenotyping phenotype

Record metadata

  • Created on: 2015-06-20
  • Curated by:
    • Shixiang Sun [2016-03-25]
    • Mengwei Li [2016-02-20]
    • Shixiang Sun [2015-11-20]
    • Shixiang Sun [2015-06-28]
    • Shixiang Sun [2015-06-26]
Stats