Calypso

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Calypso is an easy-to-use online software, allowing non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. The software is free for academic use. Biom and QIIME mapping files can be converted using our Converter. The Calypso server is freely available at: http://cgenome.net/calypso/


Reference:

Zakrzewski M, Proietti C, Ellis J, Hasan S, Brion MJ, Berger B, Krause L (2016) Calypso: A User-Friendly Web-Server for Mining and Visualizing Microbiome-Environment Interactions. Bioinformatics [1].


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Previous Calypso versions:

Calypso version 8.6

Calypso version 8.10

Calypso version 8.13

Calypso version 8.18


Getting help:

Detailed help information can be found on the Calypso Help Wiki. Additionally, the Calypso User Group provides a public forum for asking questions, searching previous questions, and sharing tips regarding Calypso. Post to the forum if you have any questions regarding Calypso, including analysis methods, interpretation of results, parameters, data pre-processing, bug-reports, or suggestions for improvements.



Short description of Calypso:

Calypso has a focus on robust multivariate statistical approaches that can identify complex environment-microbiome associations, whereby differences in microbial composition can be attributed to multiple environmental variables. Powerful visualization techniques are provided, generating high quality figures that can readily be used in scientific publications. Calypso enables, at all taxonomic levels and for large taxonomic datasets, quantitative visualizations and comparisons of composition (e.g. bubbleplots, interactive hierarchical trees, Krona plots and heatmaps), parametric and non-parametric statistical tests, univariate and multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis, and diversity estimates. To render the data suitable for analysis by standard statistical procedures, Calypso can transform and normalize community profiles using various methods, including log transformation and total-sum normalization. Since Calypso does not require scripting or programming skills, it enables all microbiology researchers to routinely apply advanced statistical analysis in their work.


If you find this software useful, please help us by sharing Calypso via social media.

Screenshots

NetworkAnalysis.png TreeCA.png

Tree2.png Kronar.png DiversityCA.png Barchart.png DS1.png DS2.png Stripchart.png DS4.png DS5.png

Disclaimer

This program is provided in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. The software may be used at your own risk. If you decide to use Calypso in published work, it is YOUR responsibility to ensure the correctness and consistency of the data.


Published articles that have used Calypso

  1. Infections by human gastrointestinal helminths are associated with changes in faecal microbiota diversity and composition Timothy P. Jenkins, Yasara Rathnayaka, Piyumali K. Perera, Laura E. Peachey, Matthew J. Nolan, Lutz Krause, Rupika S. Rajakaruna, Cinzia Cantacessi PLoS One. 2017
  2. Prasai, T. P., Walsh, K. B., Bhattarai, S. P., Midmore D. J., Van T. T., Moore R. J. and Stanley, D. Zeolite food supplementation reduces abundance of enterobacteria. Microbiological Research 195: 24-30. (2017).
  3. Mariño, E., Richards, J., McLeod, K., Stanley, D., Yap, Y.E., Knight, J., McKenzie, C., Kranich, J., Oliveira, A., Rossello, F., Krishnamurthy, B., Nefzger, C., Macia, L., Thorburn, A., Baxter, A., Morahan, G., Wong, L., Polo, J.M., Moore, R.J., Lockett, T., Clarke, J., Topping, D., Harrison, L., and Mackay, C.R. (2017) "Gut microbial metabolites limit autoimmune T cell frequencies and protect against type 1 diabetes", Nature Immunology, doi:10.1038/ni.3713, 18 (5), 552-562 (2017).
  4. Abudabos, A.M., Al-Atiyat, R.M., Albatshan, H.A., Al Jassim, R., Aljumaah, M.R., Alkhulaifi, M.M., and Stanley, D. Effects of concentration of corn distillers dried grains with solubles and enzyme supplementation on cecal microbiota and performance in broiler chickens. Applied Microbiology and Biotechnology, DOI:10.1007/s00253-017-8448-5, (2017).
  5. Wilkinson N., Hughes R.J., Aspden W.J., Chapman J., Moore R.J and Stanley D. The gastrointestinal tract microbiota of the Japanese quail, Coturnix japonica. Applied Microbiology and Biotechnology 15:51 1/13 1:9 (2016).
  6. Erin E. Donaldson, Dragana Stanley​, Robert J. Hughes, Robert J. Moore (2017) The time-course of broiler intestinal microbiota development after administration of cecal contents to incubating eggs. PeerJ
  7. R.B. Bataan, A.S. Fanning, V.J. Dalbo, A.T. Scanlan, M.J. Duncan, R.J. Moore, D. Stanley (2017) A gut reaction: the combined influence of exercise and diet on gastrointestinal microbiota in rats. Applied Microbiology
  8. Anitha Isaiah, Joseph Cyrus Parambeth, jörg M. Steiner, Jonathan A. Lidbury, Jan S.Suchodolski (2017) The fecal microbiome of dogs with exocrine pancreatic insufficiency. Anaerobe
  9. Eduardo Cresol-Martínez, Dragana StanleyMark S. GeierRobert J. HughesRobert J. Moore (2017) Understanding the mechanisms of zinc bacitracin and avilamycin on animal production: linking gut microbiota and growth performance in chickens. Applied Microbiology and Biotechnology
  10. Stefan Leuko , Kaisa Koskinen, Laura Sanna, Ilenia M. D’Angeli, Jo De Waele, Paolo Marcia, Christine Moissl-Eichinger, Petra Rettberg (2017) The influence of human exploration on the microbial community structure and ammonia oxidizing potential of the Su Bentu limestone cave in Sardinia, Italy. PLOS one
  11. Crisol-Martínez E, Stanley D, Geier MS, Hughes RJ, Moore RJ. (2017) Sorghum and wheat differentially affect caecal microbiota and associated performance characteristics of meat chickens. PeerJ.
  12. Gomez-Arango LF, Barrett HL, McIntyre HD, Callaway LK, Morrison M, Dekker Nitert M. (2017) Antibiotic treatment at delivery shapes the initial oral microbiome in neonates. Sci Rep
  13. Gomez-Arango LF, Barrett HL, McIntyre HD, Callaway LK, Morrison M, Dekker Nitert M; SPRING Trial Group (2016) Connections Between the Gut Microbiome and Metabolic Hormones in Early Pregnancy in Overweight and Obese Women. Diabetes.
  14. Zakrzewski M, Proietti C, Ellis J, Hasan S, Brion MJ, Berger B, Krause L (2016) Calypso: A User-Friendly Web-Server for Mining and Visualizing Microbiome-Environment Interactions. Bioinformatics.
  15. Giacomin, Zakrzewski, Jenkins, Su, Al-Hallaf, Croese, de Vries, Grant, Mitreva, Loukas, Krause, Cantacessi. Changes in duodenal tissue-associated microbiota following hookworm infection and consecutive gluten challenges in humans with Coeliac Disease. Scientific Reports. Accepted.
  16. Crisol-Martínez E, Moreno-Moyano LT, Wilkinson N, Prasai T, Brown PH, Moore RJ, Stanley D. A low dose of an organophosphate insecticide causes dysbiosis and sex-dependent responses in the intestinal microbiota of the Japanese quail (Coturnix japonica). PeerJ. 2016 May
  17. Stanley D, Hughes RJ, Geier MS, Moore RJ. Bacteria within the Gastrointestinal Tract Microbiota Correlated with Improved Growth and Feed Conversion: Challenges Presented for the Identification of Performance Enhancing Probiotic Bacteria. Front Microbiol. 2016 Feb 19;7:187.
  18. Hu H, Johani K, Almatroudi A, Vickery K, Van Natta B, Kadin ME, Brody G, Clemens M, Cheah CY, Lade S, Joshi PA, Prince HM, Deva AK. Bacterial Biofilm Infection Detected in Breast Implant-Associated Anaplastic Large-Cell Lymphoma. Plast Reconstr Surg. 2016 Jun;137(6):1659-69.
  19. Smith-Brown P, Morrison M, Krause L, Davies P. (2016) Dairy and plant based food intakes are associated with altered faecal microbiota in 2 to 3 year old Australian children. Scientific Reports.
  20. Smith-Brown, P., Morrison, M., Krause, L., Davies, PSW (2016) Mother's Secretor Status Affects Development of Children's Microbiota Composition and Function: A Pilot Study. PLoS ONE.
  21. Li, L., Krause, L., Somerset, S. (2016) Associations between micronutrient intakes and gut microbiota in a group of adults with cystic fibrosis. Clinical Nutrition.
  22. Umu ÖC, Frank JA, Fangel JU, Oostindjer M, da Silva CS, Bolhuis EJ, Bosch G, Willats WG, Pope PB, Diep DB. Resistant starch diet induces change in the swine microbiome and a predominance of beneficial bacterial populations (2015). Microbiome.
  23. Merrifield CA, et al. Neonatal environment exerts a sustained influence on the development of the intestinal microbiota and metabolic phenotype (2016). ISME
  24. Simeoni U, Berger B, Junick J, Blaut M, Pecquet S, Rezzonico E, Grathwohl D, Sprenger N, Brüssow H; Study team, Szajewska H. Gut microbiota analysis reveals a marked shift to bifidobacteria by a starter infant formula containing a symbiotic of bovine milk-derived oligosaccharides and Bifidobacterium animalis subsp. lactis CNCM I-3446. Environ Microbiol. 2015
  25. Ainsworth T, Krause L, …[12 authors]…, Leggat W. The coral core microbiome identifies rare bacterial taxa as ubiquitous endosymbionts (2015). ISME.
  26. Giacomin P, Zakrzewski M, Croese J, Su X, Sotillo J, McCann L, Navarro S, Mitreva M, Krause L, Loukas A, Cantacessi C. Experimental hookworm infection and escalating gluten challenges are associated with increased microbial richness in celiac subjects (2015). Scientific Reports.
  27. Smith DJ, Badrick AC, Zakrzewski M, Krause L, Bell SC, Anderson GJ, Reid DW. Pyrosequencing reveals transient cystic fibrosis lung microbiome changes with intravenous antibiotics (2014). The European respiratory journal.
  28. Dewar ML, Arnould JP, Krause L, Trathan P, Dann P, Smith SC. Influence of fasting during moult on the faecal microbiota of penguins (2014). PLoS One.
  29. Swe PM, Zakrzewski M, Kelly A, Krause L, Fischer K. Scabies mites alter the skin microbiome and promote growth of opportunistic pathogens in a porcine model (2014). PLoS Negl Trop Dis, 8 (5).
  30. Cantacessi C, Giacomin P, Croese J, Zakrzewski M, Sotillo J, McCann L, Nolan MJ, Mitreva M, Krause L**, Loukas A**. Impact of experimental hookworm infection on the human gut microbiota (2014). J Infect Dis.
  31. Dewar ML, Arnould JP, Krause L, Dann P, Smith SC. Interspecific variations in the faecal microbiota of Procellariiform seabirds (2014). FEMS Microbiol Ecol.
  32. Zhang L, Gowardman J, Morrison M, Krause L, Playford EG, Rickard CM. Molecular investigation of bacterial communities on intravascular catheters: no longer just Staphylococcus (2014). Eur J Clin Microbiol Infect Dis, 33 (7)
  33. Plieskatt JL, Deenonpoe R, Mulvenna JP, Krause L, Sripa B, Bethony JM, Brindley PJ. Infection with the carcinogenic liver fluke Opisthorchis viverrini modifies intestinal and biliary microbiome (2013). FASEB J.
  34. Reis, M., Roy, N., Bermingham, E., Ryan, L., Bibiloni, R., Young, W., Krause, L., Berger, B., North, M., Stelwagen, K., Reis, M. Impact of dietary dairy polar lipids on lipid metabolism of young mice fed high fat diet (2013). Journal of Agricultural and Food Chemistry.

Overview of methods

Graphical

Method Description
Heat map Visualize microbial composition, identi-fy sample clusters and explore microbi-ome-environment associations
Bubble plot Visualize microbial community composition
Krona plot, hierarchical tree Explore hierarchical structure of micro-bial communities
Bar charts, box plots and strip charts Illustrate taxa abundance and microbial diversity


Statistical

Method Description
Network analysis Correlation network showing co-occurring and mutual exclusive taxa
WGCNA Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated taxa, for relating modules to external sample traits (using eigengene network methodology), and for calculating module membership measures.
PCoA, PCA, DCA, NMDS Unsupervised ordination methods used for data clustering and the identification of outliers
Anosim, PERMDISP2 Supervised univariate methods for identifying significant associations between community composition and a single explanatory variable
RDA, Adonis, CCA Supervised multivariate method for identifying significant associations between microbial community composition and multiple explanatory variables
Partial least squares regression (PLS) Multivariate method used to identify taxa associated with multiple explanatory variables
LDA Effect Size (LEfSe) Identifies features (e.g. genes, pathways, or proteins) characterizing the differences between two or more biological conditions
(Paired) T-test, (nested) Anova, Bayes T-test, Bayes Anova, logistic regression, (paired) Wilcoxon rank test, Kruskal-Wallis test Identify taxa significantly differentially abundant between sample groups
DESeq2, ANCOM, ALDEx2 Methods specifically developed for counts data. Used for identifying taxa significantly differentially abundant between sample groups.
Multiple linear regression Identify significant associations between individual taxa and multiple explanatory variables
Support Vector Machine (SVM) Examine if microbial community composition is predictive of an outcome of interest
Step-wise regression, LASSO regularized regression, random forest Feature selection methods used for identifying a subset of relevant taxa predictive of an outcome of interest
Shannon index, richness, evenness, Chao 1, ACE, Fisher’s Alpha, Simpson index Quantify microbial alpha diversity
mcpHill Assess microbiome diversity on multiple indices simultaneously
Jaccard, Bray-Curtis, Yue & Clayton, Chao Calculate pairwise distances of microbial community profiles
Rarefaction analysis Estimate coverage of microbial diversity by sequence data
Square root, log, asinh, and centered log ratio transformation; total sum (TSS) and quantile normalization Data transformation and normalization to render data suitable for analysis by standard statistical procedures


Getting Help

Detailed help information can be found on the Calypso Help Wiki.