Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Apr 4;18(1):200.
doi: 10.1186/s12859-017-1612-1.

AHCODA-DB: A Data Repository With Web-Based Mining Tools for the Analysis of Automated High-Content Mouse Phenomics Data

Affiliations
Free PMC article

AHCODA-DB: A Data Repository With Web-Based Mining Tools for the Analysis of Automated High-Content Mouse Phenomics Data

Bastijn Koopmans et al. BMC Bioinformatics. .
Free PMC article

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with tools to systematically explore mouse behavioural data, both with positive and negative outcome, published and unpublished, across time and experiments with single mouse resolution.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.

Keywords: AHCODA; Data analysis; Database; Mouse behaviour; Neuroscience; Phenotyping; Statistics; Visualization.

Figures

Fig. 1
Fig. 1
Schematic overview of the workflow underlying the AHCODA-DB repository and website. After data of conventional behavioural tests and automated home-cages is acquired (a), the data are transferred to a MySQL database that includes metadata on mice, behavioural tests and analysis parameters (b). Data is processed by R-scripts (c) selected from user instructions in the AHCODA-DB website interface (d). Results of group comparisons are shown in the web browser as publishable ready art and statistics (e-f) that can be downloaded as a PDF or CSV file (g). The heat map function allows large-scale group comparisons (h)

Similar articles

  • Mouse Phenome Database: a data repository and analysis suite for curated primary mouse phenotype data.
    Bogue MA, Philip VM, Walton DO, Grubb SC, Dunn MH, Kolishovski G, Emerson J, Mukherjee G, Stearns T, He H, Sinha V, Kadakkuzha B, Kunde-Ramamoorthy G, Chesler EJ. Bogue MA, et al. Nucleic Acids Res. 2020 Jan 8;48(D1):D716-D723. doi: 10.1093/nar/gkz1032. Nucleic Acids Res. 2020. PMID: 31696236 Free PMC article.
  • MausDB: an open source application for phenotype data and mouse colony management in large-scale mouse phenotyping projects.
    Maier H, Lengger C, Simic B, Fuchs H, Gailus-Durner V, Hrabé de Angelis M. Maier H, et al. BMC Bioinformatics. 2008 Mar 26;9:169. doi: 10.1186/1471-2105-9-169. BMC Bioinformatics. 2008. PMID: 18366799 Free PMC article.
  • Mouse Phenome Database (MPD).
    Bogue MA, Grubb SC, Maddatu TP, Bult CJ. Bogue MA, et al. Nucleic Acids Res. 2007 Jan;35(Database issue):D643-9. doi: 10.1093/nar/gkl1049. Epub 2006 Dec 6. Nucleic Acids Res. 2007. PMID: 17151079 Free PMC article.
  • Mitochondrial Disease Sequence Data Resource (MSeqDR): a global grass-roots consortium to facilitate deposition, curation, annotation, and integrated analysis of genomic data for the mitochondrial disease clinical and research communities.
    Falk MJ, Shen L, Gonzalez M, Leipzig J, Lott MT, Stassen AP, Diroma MA, Navarro-Gomez D, Yeske P, Bai R, Boles RG, Brilhante V, Ralph D, DaRe JT, Shelton R, Terry SF, Zhang Z, Copeland WC, van Oven M, Prokisch H, Wallace DC, Attimonelli M, Krotoski D, Zuchner S, Gai X; MSeqDR Consortium Participants; MSeqDR Consortium participants: Sherri Bale, Jirair Bedoyan, Doron Behar, Penelope Bonnen, Lisa Brooks, Claudia Calabrese, Sarah Calvo, Patrick Chinnery, John Christodoulou, Deanna Church,; Rosanna Clima, Bruce H. Cohen, Richard G. Cotton, IFM de Coo, Olga Derbenevoa, Johan T. den Dunnen, David Dimmock, Gregory Enns, Giuseppe Gasparre,; Amy Goldstein, Iris Gonzalez, Katrina Gwinn, Sihoun Hahn, Richard H. Haas, Hakon Hakonarson, Michio Hirano, Douglas Kerr, Dong Li, Maria Lvova, Finley Macrae, Donna Maglott, Elizabeth McCormick, Grant Mitchell, Vamsi K. Mootha, Yasushi Okazaki,; Aurora Pujol, Melissa Parisi, Juan Carlos Perin, Eric A. Pierce, Vincent Procaccio, Shamima Rahman, Honey Reddi, Heidi Rehm, Erin Riggs, Richard Rodenburg, Yaffa Rubinstein, Russell Saneto, Mariangela Santorsola, Curt Scharfe,; Claire Sheldon, Eric A. Shoubridge, Domenico Simone, Bert Smeets, Jan A. Smeitink, Christine Stanley, Anu Suomalainen, Mark Tarnopolsky, Isabelle Thiffault, David R. Thorburn, Johan Van Hove, Lynne Wolfe, and Lee-Jun Wong. Falk MJ, et al. Mol Genet Metab. 2015 Mar;114(3):388-96. doi: 10.1016/j.ymgme.2014.11.016. Epub 2014 Dec 4. Mol Genet Metab. 2015. PMID: 25542617 Free PMC article. Review.
  • The Pain Genes Database: An interactive web browser of pain-related transgenic knockout studies.
    Lacroix-Fralish ML, Ledoux JB, Mogil JS. Lacroix-Fralish ML, et al. Pain. 2007 Sep;131(1-2):3.e1-4. doi: 10.1016/j.pain.2007.04.041. Epub 2007 Jun 14. Pain. 2007. PMID: 17574758 Review.
See all similar articles

Cited by 1 article

References

    1. Crabbe JC, Morris RGM. Festina lente: Late-night thoughts on high-throughput screening of mouse behavior. Nat Neurosci. 2004;7:1175–9. doi: 10.1038/nn1343. - DOI - PubMed
    1. Loos M, Koopmans B, Aarts E, Maroteaux G, van der Sluis S, Verhage M, et al. Sheltering behavior and locomotor activity in 11 genetically diverse common inbred mouse strains using home-cage monitoring. PLoS One. 2014;9:e108563. doi: 10.1371/journal.pone.0108563. - DOI - PMC - PubMed
    1. Maroteaux G, Loos M, van der Sluis S, Koopmans B, Aarts E, van Gassen K, et al. High-throughput phenotyping of avoidance learning in mice discriminates different genotypes and identifies a novel gene. Genes Brain Behav. 2012;11:772–84. doi: 10.1111/j.1601-183X.2012.00820.x. - DOI - PMC - PubMed
    1. Robinson L, Riedel G. Comparison of automated home-cage monitoring systems: Emphasis on feeding behaviour, activity and spatial learning following pharmacological interventions. J Neurosci Methods. 2014;234:13–25. doi: 10.1016/j.jneumeth.2014.06.013. - DOI - PubMed
    1. Vannoni E, Voikar V, Colacicco G, Sánchez MA, Lipp H-P, Wolfer DP. Spontaneous behavior in the social homecage discriminates strains, lesions and mutations in mice. J Neurosci Methods. 2014;234:26–37. doi: 10.1016/j.jneumeth.2014.04.026. - DOI - PubMed

Substances

LinkOut - more resources

Feedback