Additional file 2 of Global patterns of diversity and metabolism of microbial communities in deep-sea hydrothermal vent deposits

Additional file 2: Table S1. Sample metadata including location, year, research vessel, number of metagenome reads and accession numbers. Table S2. MAG genome properties, accession numbers and taxonomic classifications. Taxonomy was assigned using GTDB-Tk, and mis-classified MAGs were taxonomically re-assigned at the phylum level (Bacteria) and class level (Archaea) using curated archaeal and bacterial phylogenetic trees. Genome quality statistics are based on completion and contamination (high quality, >90% completion, <5% contamination; medium quality, ≥50% completion, ≤10% contamination). Average contamination was 4.02%. Table S3. Average amino acid identity (AAI) matrices for the (A) Bacteria and (B) Archaea. Matrices are grouped by GTDB taxonomy and include MAGs that could not be assigned to a known genus by GTDB-Tk. Details are provided which recently identified MAGs were from Brothers volcano hydrothermal deposits. Table S4. Relative abundance of GTDB taxa by site, based on read coverage of MAGs normalized to 100M reads per sample. MAG coverage for each site was summed and expressed as a percent. Table S5. METABOLIC-G results for JALSQH01 (3 MAGs) and JALWCF01 (13 MAGs). In the summary rows for JALSQH01 and JALWCF01, functions and modules are listed as “present” if identified in ≥50% of all MAGs, “partially present” if found in <50% of the MAGs, and “absent” if undetected in the MAGs. Table S6. Selected functional genes found in Patescibacteria MAGs, based on annotation with GhostKOALA. KEGG module numbers are shown in parentheses. Table S7. Functional genes identified in selected > 80%-completeness MAGs from the Chloroflexota. (A) Genes are marked as present (1; green highlight) or not detected (0) in individual MAGs. (B) The proportion of > 80%-completeness MAGs in six GTDB orders that encode functional genes is also shown, with proportions ≥50% highlighted in green. Table S8. Identification and distribution of functional genes in this study. (A) The HMMs, MEROPS peptidases, and CAZymes used to identify functional genes. Gene call numbers were calculated using the component (C) or accumulative (A) methods described in the methods. Genes requiring manual validation (M) are indicated. (B) Functional gene abundance, calculated as described in the methods. Table S9. Percentage of MAGs in phylogenetic clusters that encode core metabolic genes. Unless otherwise indicated, Archaea are shown at the class level, and Bacteria are shown at the phylum level. Genes were detected using METABOLIC, with additional validation steps for oxidative and reductive Dsr, Sdo, PmoA and McrA. Table S10. Comparative (A) relative abundance and (B) functional gene abundance for the Gammaproteobacteria and Campylobacteria, used to generate Fig. 10.