PhenStat a tool kit for standardized analysis of high throughput phenotypic data

PLoS ONE

Volumn 10, Issue 7, 2015, Pages

  Kurbatova, Natalja ^a   Mason, Jeremy C ^a   Morgan, Hugh ^b   Meehan, Terrence F ^a   Karp, Natasha A ^c  

^a EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^b MEDICAL RESEARCH COUNCIL   (United Kingdom)

^c WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ANIMAL TISSUE; ARTICLE; AUTOMATION; CONTROLLED STUDY; DATA ANALYSIS; DATA ANALYSIS SOFTWARE; FISHER EXACT TEST; GENOTYPE; INFORMATION PROCESSING; MOUSE; NONHUMAN; PHENOTYPE; STATISTICAL ANALYSIS; ANIMAL; FEMALE; HIGH THROUGHPUT SCREENING; MALE; PROCEDURES; RAT; REPRODUCIBILITY; SOFTWARE; STANDARD; STANDARDS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

ANIMALIA; RATTUS;

ANIMALS; DATASETS AS TOPIC; FEMALE; HIGH-THROUGHPUT SCREENING ASSAYS; LINEAR MODELS; MALE; MICE; PHENOTYPE; RATS; REFERENCE STANDARDS; REPRODUCIBILITY OF RESULTS; SOFTWARE;

EID: 84940062348     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0131274     Document Type: Article

References (31)

1. Prinz F, Schlange T, Asadullah K. Believe it or not: how much can we rely on published data on potential drug targets? Nat Rev Drug Discov. 2011;10:712-712. doi: 10.1038/nrd3439-c1 PMID: 21892149
2. Collins FS, Tabak LA. Policy: NIH plans to enhance reproducibility. Nature. 2014;505:612-613. PMID: 24482835
3. Kilkenny C, Browne WJ, Cuthill IC, Emerson M, Altman DG. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoSBiol. 2010;8:e1000412. doi: 10.1371/journal.pbio.1000412 PMID: 20613859
4. Deans AR, Lewis SE, Huala E, Anzaldo SS, Ashburner M, Balhoff JP, et al. Finding Our Way through Phenotypes. PLoSBiol. 2015;13:e1002033. doi: 10.1371/journal.pbio. 1002033 PMID: 25562316
5. Pettitt SJ, Liang Q, Rairdan XY, Moran JL, Prosser HM, Beier DR, et al. Agouti C57BL/6N embryonic stem cells for mouse genetic resources. Nat Methods. 2009;6:493-495. doi: 10.1038/nmeth.1342 PMID: 19525957
6. Simon MM, Greenaway S, White JK, Fuchs H, Gailus-Durner V, Wells S, et al. A comparative pheno-typic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol. 2013;14:R82. doi: 10.1186/gb-2013-14-7-r82 PMID: 23902802
7. Ringwald M, Iyer V, Mason JC, Stone KR, Tadepally HD, Kadin JA, et al. The IKMCweb portal: a central point of entry to data and resources from the International Knockout Mouse Consortium. Nucleic Acids Res. 2011;39:D849-855. doi: 10.1093/nar/gkq879 PMID: 20929875
8. Kettleborough RNW, Busch-Nentwich EM, Harvey SA, Dooley CM, De Bruijn E, Van Eeden F, et al. A systematic genome-wide analysis of zebrafish protein-coding gene function. Nature. 2013;496:494-497. doi: 10.1038/nature11992 PMID: 23594742
9. Brown SDM, Moore MW. The International Mouse Phenotyping Consortium: past and future perspectives on mouse phenotyping. Mamm Genome Off J IntMamm Genome Soc. 2012;23:632-640. doi: 10.1007/s00335-012-9427-x
10. Karp NA, Melvin D, Sanger Mouse Genetics Project, Mott RF. Robust and sensitive analysis of mouse knockout phenotypes. PLoSONE. 2012;7:e52410. doi: 10.1371/journal.pone.0052410 PMID: 23300663
11. Lees H, Swann J, Poucher SM, Nicholson JK, Holmes E, Wilson ID, et al. Age and Microenvironment Outweigh Genetic Influence on the Zucker Rat Microbiome. PLoSONE. 2014;9:e100916. doi: 10. 1371/journal.pone.0100916 PMID: 25232735
12. R Development Core Team. R: A language and environment for statistical computing [Internet]. R Foundation for Statistical Computing, Vienna, Austria; Available: http://www.R-project.org
13. Hahne F, Huber W, Gentleman R. Bioconductor Case Studies. 2008 edition. New York, NY: Springer; 2008.
14. Flanagan KL. Sexual dimorphism in biomedical research: a call to analyse by sex. Trans R Soc Trop Med Hyg. 2014;108:385-387. doi: 10.1093/trstmh/tru079 PMID: 24934286
15. White JK, Gerdin A-K, Karp NA, Ryder E, Buljan M, Bussell JN, et al. Genome-wide Generation and Systematic Phenotyping of Knockout Mice Reveals New Roles for Many Genes. Cell. 2013;154:452-464. doi: 10.1016/j.cell.2013.06.022 PMID: 23870131
16. Ayadi A, Birling M-C, Bottomley J, Bussell J, Fuchs H, Fray M, et al. Mouse large-scale phenotyping initiatives: overview of the European Mouse Disease Clinic (EUMODIC) and of the Wellcome Trust Sanger Institute Mouse Genetics Project. Mamm Genome Off J Int Mamm Genome Soc. 2012;23:600-610. doi: 10.1007/s00335-012-9418-y
17. Aitman TJ, Critser JK, Cuppen E, Dominiczak A, Fernandez-Suarez XM, Flint J, et al. Progress and prospects in rat genetics: a community view. Nat Genet. 2008;40:516-522. doi: 10.1038/ng.147 PMID: 18443588
18. Koscielny G, Yaikhom G, Iyer V, Meehan TF, Morgan H, Atienza-Herrero J, et al. The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data. Nucleic Acids Res. 2014;42:D802-D809. doi: 10.1093/nar/gkt977 PMID: 24194600
19. Fisher RA. On the Interpretation of %2 from Contingency Tables, and the Calculation of P. JRStat Soc. 1922;85:87. doi: 10.2307/2340521
20. Karp NA, Speak AO, White JK, Adams DJ, Hrabéde Angelis M, Hérault Y, et al. Impact of Temporal Variation on Design and Analysis of Mouse Knockout Phenotyping Studies. PLoS ONE. 2014;9:e111239. doi: 10.1371/journal.pone.0111239 PMID: 25343444
21. West BT, Welch KB, Galecki AT. Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition. 2 edition. Boca Raton: Chapman and Hall/CRC; 2014.
22. Cook MN, Dunning JP, Wiley RG, Chesler EJ, Johnson DK, Miller DR, et al. Neurobehavioral mutants identified in an ENU-mutagenesis project. Mamm Genome Off J Int Mamm Genome Soc. 2007;18:559-572. doi: 10.1007/s00335-007-9035-3
23. Jacob HJ, Kwitek AE. Rat genetics: attachign physiology and pharmacology to the genome. Nat Rev Genet. 2002;3:33-42. doi: 10.1038/nrg702 PMID: 11823789
24. Reed DR, Lawler MP, Tordoff MG. Reduced body weight is a common effect of gene knockout in mice. BMC Genet. 2008;9:4. doi: 10.1186/1471-2156-9-4 PMID: 18182103
25. Valdar W, Solberg LC, Gauguier D, Cookson WO, Rawlins JNP, Mott R, et al. Genetic and environmental effects on complex traits in mice. Genetics. 2006;174:959-984. doi: 10.1534/genetics.106.060004 PMID: 16888333
26. Firth D. Bias reduction of maximum likelihood estimates. Biometrika. 1993;80:27-38. doi: 10.1093/biomet/80.1.27
27. Box GEP, Cox DR. An Analysis of Transformations. JRStat Soc Ser B Methodol. 1964;26:211-252.
28. Begley CG, Ellis LM. Drug development: Raise standards for preclinical cancer research. Nature. 2012;483:531-533. doi: 10.1038/483531a PMID: 22460880
29. Must try harder. Nature. 2012;483:509-509. doi: 10.1038/483509a
30. Baker D, Lidster K, Sottomayor A, Amor S. Two Years Later: Journals Are Not Yet Enforcing the ARRIVE Guidelines on Reporting Standards for Pre-Clinical Animal Studies. Eisen JA, editor. PLoS Biol. 2014;12:e1001756. doi: 10.1371/journal.pbio.1001756 PMID: 24409096
31. Clark RA, Shoaib M, Hewitt KN, Stanford SC, Bate ST. A comparison of InVivoStatwith other statistical software packages for analysis of data generated from animal experiments. J Psychopharmacol Oxf Engl. 2012;26:1136-1142. doi: 10.1177/0269881111420313