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This guide is designed to help you find fundamental resources for research in the area of theatre. The resources here are by no means comprehensive, but they will help you achieve a sound start to your work. The research process is often nonlinear and you will probably find yourself returning to certain steps and certain resources over and over. Explore, ask questions, find information.

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We found that 3,750 (81%) of the species in the UHGG catalog did not have a representative in any of the human gut culture databases. To extend the search to isolate genomes from other environments or lacking information on the isolation source, we compared the UHGG catalog to all NCBI RefSeq isolate genomes. We identified an additional set of 438 species closely matching cultured genomes (88 from human body sites, 29 from other animals, 3 from plants and the remainder (318) from unknown sources), leaving 3,312 (71%) UHGG species as uncultured (Supplementary Table 2).

By calculating the number of genomes contained within each cultured and uncultured human gut species, we found that species containing isolate genomes represented the largest clusters, while those exclusively encompassing MAGs tended to be the rarest, as discussed previously16,18,20. For example, only 2 of the 25 largest bacterial clusters were exclusively represented by MAGs (Fig. 2c), with 1,212 uncultured species represented by a single genome (80% of which originated from samples only analyzed in one of the MAG studies; Extended Data Fig. 4). The bacterial species most represented in our collection were Agathobacter rectalis (recently reclassified from Eubacterium rectale37), Escherichia coli D and Bacteroides uniformis (Fig. 2c, Extended Data Fig. 5a and Supplementary Table 2), whereas the most frequently recovered archaeal species was Methanobrevibacter A smithii, with 608 genomes found across all six continents (Extended Data Fig. 6). We inferred the level of geographic diversity of each species by calculating the Shannon diversity index on the proportion of samples in which each species was found per continent. The largest species clusters displayed similarly high levels of geographic distribution, indicating that the most highly represented species were not restricted to individual locations (Fig. 2c and Extended Data Fig. 5b).

We used the eggNOG41, InterPro42, COG43 and KEGG44 annotation schemes to capture the full breadth of functions within the UHGP. However, we found that 41.5% of UHGP-100 was poorly characterized, as 27.3% lacked a match to any database and a further 14.2% only had a match to a COG with no known function (Fig. 4c). On the basis of the distribution of COG functions, the most highly represented categories were related to amino acid transport and metabolism, cell wall/membrane/envelope biogenesis and transcription.

We next investigated intraspecies single-nucleotide variants (SNVs) within the UHGG species. We generated a catalog consisting of 249,435,699 SNVs from 2,489 species with three or more conspecific genomes (Fig. 6a). For context, a previously published catalog contained 10.3 million single-nucleotide polymorphisms from 101 gut microbiome species45. Of note, more than 85% of these SNVs were exclusively detected in MAGs, whereas only 2.2% were exclusive to isolate genomes (Fig. 6b). We found the overall pairwise SNV density between MAGs to be higher than that observed between isolate genomes (Fig. 6c). This was irrespective of the level of strain heterogeneity of the MAGs, as there was no correlation between SNV density and the degree of strain heterogeneity estimated with CMseq (Extended Data Fig. 10). Next, we assigned the detected SNVs to the continent of origin of each genome and observed that 36% of the SNVs were continent specific. Notably, genomes with a European origin contributed to the most exclusive SNVs (Fig. 6d). However, genomes from Africa contributed over three times more variation on average than European or North American genomes. Pairwise SNV analysis also supported a higher cross-continent SNV density, especially between genomes from Africa and Europe (Fig. 6e). Our results suggest that there is high strain variability between continents and that a considerable level of diversity remains to be discovered, especially from under-represented regions such as Africa, South America and Oceania.

A geographic diversity index was estimated to assess how widely distributed each species was. We calculated the Shannon diversity index on the proportion of samples in which each species was found per continent. This metric combines both richness and evenness, such that the level of estimated diversity is highest in species found across all continents at a similar proportion.

We would like to thank P. Bradley and Z. Iqbal for their help in the BIGSI implementation; D. Wu for assistance in the identification of uncultured monophyletic groups; M. Zolfo for guidance in running CMseq; and J. Lu for building the UHGG Bracken databases. Funding: European Molecular Biology Laboratory (EMBL); Biotechnology and Biological Sciences Research Council (BB/N018354/1 and BB/R015228/1); and European Research Council (project ERC-STG MetaPG-716575) to N.S. S.N. and N.C.K. were supported by the Chan Zuckerberg Biohub and by the US Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, under contract DE-AC02-05CH11231 and used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US Department of Energy under contract DE-AC02-05CH11231.

a, Cumulative distribution curve of the number and size of the gene clusters of the UHGP-95 (n = 20,239,340), UHGP-90 (n = 13,907,849) and UHGP-50 (n = 4,735,546). Dashed vertical lines indicate the cluster size below which 90% of the gene clusters can be found. b, Proportion of metagenomic reads from 1,005 independent datasets aligned with DIAMOND against the combined clusters of UHGP-90 and IGC-90 (left). The degree of classification improvement provided over the IGC-90 alone is represented in the right panel. The following represents the number of datasets analysed per country: Cameroon, n = 54; Ethiopia, n = 25; Germany, n = 56; Ghana, n = 40; India, n = 105; Italy, n = 50; Luxembourg, n = 26; Russia, n = 4; Tanzania, n = 61; United Kingdom, n = 210; United States, n = 374. Box lengths represent the interquartile range (IQR) of the data, and the whiskers the lowest and highest values within 1.5 times the IQR from the first and third quartiles, respectively. 041b061a72