AHCODA-DB: a data repository with web-based mining tools for the analysis of automated high-content mouse phenomics data
Abstract Background Systematic, standardized and in-depth phenotyping and data analyses of rodent behaviour empowers gene-function studies, drug testing and therapy design. However, no data repositories are currently available for standardized quality control, data analysis and mining at the resolut...
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| Format: | Article |
| Language: | English |
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BMC
2017-04-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-017-1612-1 |
| _version_ | 1831561275829649408 |
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| author | Bastijn Koopmans August B. Smit Matthijs Verhage Maarten Loos |
| author_facet | Bastijn Koopmans August B. Smit Matthijs Verhage Maarten Loos |
| author_sort | Bastijn Koopmans |
| collection | DOAJ |
| description | Abstract Background Systematic, standardized and in-depth phenotyping and data analyses of rodent behaviour empowers gene-function studies, drug testing and therapy design. However, no data repositories are currently available for standardized quality control, data analysis and mining at the resolution of individual mice. Description Here, we present AHCODA-DB, a public data repository with standardized quality control and exclusion criteria aimed to enhance robustness of data, enabled with web-based mining tools for the analysis of individually and group-wise collected mouse phenotypic data. AHCODA-DB allows monitoring in vivo effects of compounds collected from conventional behavioural tests and from automated home-cage experiments assessing spontaneous behaviour, anxiety and cognition without human interference. AHCODA-DB includes such data from mutant mice (transgenics, knock-out, knock-in), (recombinant) inbred strains, and compound effects in wildtype mice and disease models. AHCODA-DB provides real time statistical analyses with single mouse resolution and versatile suite of data presentation tools. On March 9th, 2017 AHCODA-DB contained 650 k data points on 2419 parameters from 1563 mice. Conclusion AHCODA-DB provides users with tools to systematically explore mouse behavioural data, both with positive and negative outcome, published and unpublished, across time and experiments with single mouse resolution. The standardized (automated) experimental settings and the large current dataset (1563 mice) in AHCODA-DB provide a unique framework for the interpretation of behavioural data and drug effects. The use of common ontologies allows data export to other databases such as the Mouse Phenome Database. Unbiased presentation of positive and negative data obtained under the highly standardized screening conditions increase cost efficiency of publicly funded mouse screening projects and help to reach consensus conclusions on drug responses and mouse behavioural phenotypes. The website is publicly accessible through https://public.sylics.com and can be viewed in every recent version of all commonly used browsers. |
| first_indexed | 2024-12-17T07:37:05Z |
| format | Article |
| id | doaj.art-9279d3928464420a86d379056653bfbb |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-17T07:37:05Z |
| publishDate | 2017-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-9279d3928464420a86d379056653bfbb2022-12-21T21:58:18ZengBMCBMC Bioinformatics1471-21052017-04-011811510.1186/s12859-017-1612-1AHCODA-DB: a data repository with web-based mining tools for the analysis of automated high-content mouse phenomics dataBastijn Koopmans0August B. Smit1Matthijs Verhage2Maarten Loos3Sylics (Synaptologics BV)Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University AmsterdamDepartment of Functional Genomics, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University AmsterdamSylics (Synaptologics BV)Abstract Background Systematic, standardized and in-depth phenotyping and data analyses of rodent behaviour empowers gene-function studies, drug testing and therapy design. However, no data repositories are currently available for standardized quality control, data analysis and mining at the resolution of individual mice. Description Here, we present AHCODA-DB, a public data repository with standardized quality control and exclusion criteria aimed to enhance robustness of data, enabled with web-based mining tools for the analysis of individually and group-wise collected mouse phenotypic data. AHCODA-DB allows monitoring in vivo effects of compounds collected from conventional behavioural tests and from automated home-cage experiments assessing spontaneous behaviour, anxiety and cognition without human interference. AHCODA-DB includes such data from mutant mice (transgenics, knock-out, knock-in), (recombinant) inbred strains, and compound effects in wildtype mice and disease models. AHCODA-DB provides real time statistical analyses with single mouse resolution and versatile suite of data presentation tools. On March 9th, 2017 AHCODA-DB contained 650 k data points on 2419 parameters from 1563 mice. Conclusion AHCODA-DB provides users with tools to systematically explore mouse behavioural data, both with positive and negative outcome, published and unpublished, across time and experiments with single mouse resolution. The standardized (automated) experimental settings and the large current dataset (1563 mice) in AHCODA-DB provide a unique framework for the interpretation of behavioural data and drug effects. The use of common ontologies allows data export to other databases such as the Mouse Phenome Database. Unbiased presentation of positive and negative data obtained under the highly standardized screening conditions increase cost efficiency of publicly funded mouse screening projects and help to reach consensus conclusions on drug responses and mouse behavioural phenotypes. The website is publicly accessible through https://public.sylics.com and can be viewed in every recent version of all commonly used browsers.http://link.springer.com/article/10.1186/s12859-017-1612-1Data analysisDatabaseNeuroscienceStatisticsVisualizationMouse behaviour |
| spellingShingle | Bastijn Koopmans August B. Smit Matthijs Verhage Maarten Loos AHCODA-DB: a data repository with web-based mining tools for the analysis of automated high-content mouse phenomics data BMC Bioinformatics Data analysis Database Neuroscience Statistics Visualization Mouse behaviour |
| title | AHCODA-DB: a data repository with web-based mining tools for the analysis of automated high-content mouse phenomics data |
| title_full | AHCODA-DB: a data repository with web-based mining tools for the analysis of automated high-content mouse phenomics data |
| title_fullStr | AHCODA-DB: a data repository with web-based mining tools for the analysis of automated high-content mouse phenomics data |
| title_full_unstemmed | AHCODA-DB: a data repository with web-based mining tools for the analysis of automated high-content mouse phenomics data |
| title_short | AHCODA-DB: a data repository with web-based mining tools for the analysis of automated high-content mouse phenomics data |
| title_sort | ahcoda db a data repository with web based mining tools for the analysis of automated high content mouse phenomics data |
| topic | Data analysis Database Neuroscience Statistics Visualization Mouse behaviour |
| url | http://link.springer.com/article/10.1186/s12859-017-1612-1 |
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