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Additional file 3 of Diversity and distribution of sulfur metabolic genes in the human gut microbiome and their association with colorectal cancer

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NIAID Data Ecosystem2026-03-13 收录
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https://figshare.com/articles/dataset/Additional_file_3_of_Diversity_and_distribution_of_sulfur_metabolic_genes_in_the_human_gut_microbiome_and_their_association_with_colorectal_cancer/19618212
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Additional file 2: Supplemental Table 1. Summary information for genes discussed in this study including abbreviations, full gene name, reaction the gene catalyzes, and HMM information. Supplemental Table 2. Summary table of HMM results where “gene” indicates that gene was found in the MAG and “N/A” indicates the gene was not found in the MAG. Supplemental Table 3. Summary values of participants and MAGs with and without sulfur genes. These data were used to generate dot plots. Supplemental Table 4. Summary of MAG genera and gene hits associated with each genera from the study in addition to any previous associations of that genera with colorectal cancer and production of H2S. Supplemental Table 5. MAGs associated with complete and partial taurine pathways. Supplemental Table 6. Results from chi squared tests for associations of gene presence with participant disease status. Both uncorrected and corrected p-values are reported. Supplemental Table 7. Results from ANOVA tests for associations between presence and absence of sulfur genes and stage of colorectal cancer. Both uncorrected and corrected p-values are reported. Supplemental Table 8. Growth rate values generated by iRep for indicator species of colorectal cancer for 4 of the 5 studies used in our work. Supplemental Table 9. Results from Kruskal Wallis analysis for differences in growth rates for the aggregate data for CRC versus healthy and each individual study including comparisons across CRC, healthy, and adenoma, if present. Supplemental Table 10. Summary of taxonomy of MAGs including classification from original paper constructing MAGs (assigned and closest designators), SILVA classification based on 16S rRNA sequences, and GTDB-tk classifications.
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2022-04-19
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