L'Athlète : journal hebdomadaire de tous les sports

07 décembre 19381938/12/07 (N1126)-1938/12/07.Appartient à l’ensemble documentaire : Aquit

L'Avenir de Luchon : journal scientifique, littéraire, annonces et réclames : guide général des baigneurs et des touristes aux eaux thermales, bains de mer de la France et de l'étranger

07 octobre 19231923/10/07 (N164)-1923/10/07.Appartient à l’ensemble documentaire : MidiPyren

Additional file 6: of In-depth resistome analysis by targeted metagenomics

MGC abundance comparison of biocide resistance between swine and human samples. Gene abundance was extracted from original count data after normalization. Some sets of genes make complex MGCs. In this representation, MGC quantification was discarded in order to increase the biological information. Genes were classified by compound susceptibility. Because some biocide resistance genes can confer different phenotypes (resistance to more than one compound), genes are not constricted to one category. Genetic abundance is expressed as reads per kilobase per million reads (RPKM). The right panel shows the results of MSS and the left panel shows the results of ResCap. (PDF 933 kb

Additional file 1: of Quantitative metagenomics reveals unique gut microbiome biomarkers in ankylosing spondylitis

Table S1. Phenotype information of AS patient individuals and health controls in discovery stage (156 samples) and validation stage (55 samples). Table S2. Data production and quality control of 156 samples in discovery stage and 55 samples in validation stage. Table S3. The 8743 reference genomes from NCBI and HMP (downloaded on 15 Dec 2013). Table S4. The differentially abundant genus in AS patients (n = 73) and healthy controls (n = 83). Table S5. Assembly result of 156 samples in discovery stage. Table S6. The improvement with the repeatedly assembly. Table S7. Gene prediction of 156 samples in discovery stage. Table S8. Genes with abundance which belong to proteasome modules. All the differentially abundant genes identified in this study only belong to bacterial proteasome. Table S9. The taxonomic annotation of MGSs. Table S10. The phenotypic correlation analysis (p value) of 12 MGSs according to different clinical groups. Table S11. Comparison of the MGS in different diseases. Table S12. The taxonomic annotation of CAGs (Gene number ≥ 100). Table S13. The details of the best markers selected for five monitoring and classification models based on five kinds of bio-markers. Table S14. The 210 differentially abundant sequenced reference genome markers used for classification training. (XLSX 870 kb

Additional file 2: of Quantitative metagenomics reveals unique gut microbiome biomarkers in ankylosing spondylitis

Figure S1a. Venn diagram of three existing human gut gene catalogs. Figure S1b. Diversity of genera and species between AS patients and healthy controls. Figure S2. The Bacteroidetes/Firmicutes ratio in the AS patient group and in the healthy control group. Figure S3. Phylogenetic abundance under phylum, genus, and species levels between AS patients and healthy controls. Figure S4. Loss of richness of the gut microbiome in AS. Figure S5. The distribution of p values. Figure S6. The distribution of KEGG functional categories (statistics in Level 2) for all genes and differentially abundant genes. Figure S7. The distribution of detail pathways in four KEGG functional categories which were quite different between AS-enriched genes and control-enriched genes in Figure S6. Figure S8. The distribution of eggNOG functional categories for AS related markers. Figure S9. The distribution of KEGG module categories for AS related markers shown by number and percentage. Figure S10. Heatmap of the abundance of a random metagenomic species in both sequencing data and downloaded data. Figure S11. Taxonomic annotation of genes in CAGs by NT database. Figure S12. The NMDS (non-metric multidimensional scaling) analysis based on phylogenetic abundance profiling of all the 156 samples in the discovery cohort. (DOCX 4671 kb