The results were summarized as the number of times an OTU was found in each sample and the taxonomic prediction for each OTU. For beta diversity analysis we sub-sampled to 3080 sequences per sample to remove sequencing depth bias. A distance matrix was built based on weighted UniFrac check details method [25] and hierarchical cluster tree was built using UPGMA (unweighted pair group method with arithmetic mean). Statistic analyses The Kolmogorov-Smirnov test was used to check the normality of data distribution. Comparisons of parametric normally distributed data were made by the Student’s test, paired tests for intra-group comparisons and unpaired
tests for inter-group comparisons; otherwise the Wilcoxon signed rank test was used for
paired 5-Fluoracil solubility dmso data, and the Mann–Whitney U test for unpaired data. When dataset was small (n<5), we performed a Poisson regression model analysis using the function glm (Generalized {Selleck Anti-diabetic Compound Library|Selleck Antidiabetic Compound Library|Selleck Anti-diabetic Compound Library|Selleck Antidiabetic Compound Library|Selleckchem Anti-diabetic Compound Library|Selleckchem Antidiabetic Compound Library|Selleckchem Anti-diabetic Compound Library|Selleckchem Antidiabetic Compound Library|Anti-diabetic Compound Library|Antidiabetic Compound Library|Anti-diabetic Compound Library|Antidiabetic Compound Library|Anti-diabetic Compound Library|Antidiabetic Compound Library|Anti-diabetic Compound Library|Antidiabetic Compound Library|Anti-diabetic Compound Library|Antidiabetic Compound Library|Anti-diabetic Compound Library|Antidiabetic Compound Library|Anti-diabetic Compound Library|Antidiabetic Compound Library|Anti-diabetic Compound Library|Antidiabetic Compound Library|Anti-diabetic Compound Library|Antidiabetic Compound Library|buy Anti-diabetic Compound Library|Anti-diabetic Compound Library ic50|Anti-diabetic Compound Library price|Anti-diabetic Compound Library cost|Anti-diabetic Compound Library solubility dmso|Anti-diabetic Compound Library purchase|Anti-diabetic Compound Library manufacturer|Anti-diabetic Compound Library research buy|Anti-diabetic Compound Library order|Anti-diabetic Compound Library mouse|Anti-diabetic Compound Library chemical structure|Anti-diabetic Compound Library mw|Anti-diabetic Compound Library molecular weight|Anti-diabetic Compound Library datasheet|Anti-diabetic Compound Library supplier|Anti-diabetic Compound Library in vitro|Anti-diabetic Compound Library cell line|Anti-diabetic Compound Library concentration|Anti-diabetic Compound Library nmr|Anti-diabetic Compound Library in vivo|Anti-diabetic Compound Library clinical trial|Anti-diabetic Compound Library cell assay|Anti-diabetic Compound Library screening|Anti-diabetic Compound Library high throughput|buy Antidiabetic Compound Library|Antidiabetic Compound Library ic50|Antidiabetic Compound Library price|Antidiabetic Compound Library cost|Antidiabetic Compound Library solubility dmso|Antidiabetic Compound Library purchase|Antidiabetic Compound Library manufacturer|Antidiabetic Compound Library research buy|Antidiabetic Compound Library order|Antidiabetic Compound Library chemical structure|Antidiabetic Compound Library datasheet|Antidiabetic Compound Library supplier|Antidiabetic Compound Library in vitro|Antidiabetic Compound Library cell line|Antidiabetic Compound Library concentration|Antidiabetic Compound Library clinical trial|Antidiabetic Compound Library cell assay|Antidiabetic Compound Library screening|Antidiabetic Compound Library high throughput|Anti-diabetic Compound high throughput screening| Linear model) of R with the following formula [glm(formula = z ~ group + pair, family = poisson)]. This model is appropriate for modeling paired count data. P values < 0.05 were referred as significant. Acknowledgments We thank Ricardo Gonzalo, Francisca Gallego, Rosario M Prieto from the Scientific and Technical Support Unit (STSU) for their technical assistance. This work was supported by the FIS PI10/00902 grant (Ministerio de Ciencia e Innovacion, Spain) and the European Community’s Seventh Framework Programme (FP7/2007-2013): International Human Microbiome Standards (IHMS), grant agreement HEALTH.2010.2.1.1-2. Ciberehd is funded by the Instituto de Salud Carlos III (Spain). Electronic Sinomenine supplementary material Additional file 1: Table S1. Detailed taxonomy assignment at the species level of the 24 samples. The taxonomy analysis is based on alignment performed using PyNast against
Silva 108 release database and OTUs assignment using blast and the Silva 108 release taxa mapping file. (XLS 250 KB) Additional file 2: Figure S1. Taxonomy analysis at the phylum level of the 24 samples based on alignment performed using PyNast against Silva 108 release database and OTUs assignment using blast and the Silva 108 release taxa mapping file. (JPEG 1 MB) Additional file 3: Supplementary Methods. Detailed description of extraction of total RNA from fecal samples. (DOC 34 KB) References 1. Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, Knight R: Bacterial community variation in human body habitats across space and time. Science 2009,326(5960):1694–1697.PubMedCrossRef 2. Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, et al.: A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010,464(7285):59–65.PubMedCrossRef 3.