dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019b). Here we sampled each subject on at least 4 separate days to improve the accuracy of automatic binning through differential coverage  (Quince et al. , 2017). To further minimize potential errors in automatic binning results, we used anvi'o to manually inspect, and when necessary, further refine key genomes in our study by (1) visualizing the change in GC-content and gene taxonomy of each contig, (2) performing ad hoc searches of sequences in public databases, and (3) ensuring the agreement across all contigs with respect to sequence composition signal and differential coverage, the coverage of contigs by reads recruited from our metagenomes as well as metagenomes from other studies. Our data report includes each genome bin for interactive inspection (see Data Availability). After removal of human host DNA contamination, which accounted for 5%-45% of the reads per sample, competitive read recruitment revealed that the final list of genomes recruited nearly half of the reads from our metagenomes (mean 47%, with a range of 10%-74% per sample).
† Refined versions of genomes, which we previously published  (Shaiber & Eren, 2019). ‡ Genomes from IMG that we refined in this study, but for which accession numbers for refined versions are available in  Cross et al. 2019. We then sought to investigate whether the ancestral relationships among TM7 genomes could explain their intriguing site-specificity. For this, we combined our 43 MAGs with 9 human oral TM7 genomes from the literature. In addition to 3 single amplified genomes that we downloaded from the Integrated Microbial Genomes and Microbiomes database (IMG/M)  (Chen et al. , 2019c)  and refined (see Methods) and a MAG from Marcy et al. (Marcy et al. , 2007) , we included 4 MAGs from Espinoza et al. (Espinoza et al. , 2018)  after manually refining composite TM7 genomes  (Shaiber & Eren, 2019) , and the first cultivated strain of TM7, TM7x  (He et al.
, 2018b). Taking advantage of the large number of genomes we have reconstructed, we revisited this observation by performing a phylogenomic analysis using the 156 publicly available TM7 genomes in the NCBI's GenBank database as of 1/16/2019 (Figure  3 ). We identified six monophyletic human oral clades that were associated either with tongue (T1, T2) or plaque (P1, P2, P3, P4) (Figure  3 ). Using a pair-wise comparison of the ANI of oral TM7 genomes, we further identified subclades corresponding to genus and species level groups within the six monophyletic clades, including 12 species of TM7 represented each by at least 2 genomes in our collection (Figure  3 , Supplementary Tables 7f-h, Supplementary Information). MAGs reconstructed from metagenomic short-reads often lack ribosomal RNA operons and therefore cannot be identified with known 16S rRNA gene sequences.
Additionally, our analysis reveals 12 species of TM7 that are represented by multiple genomes in our collection and lays the groundwork for definition of taxonomic groups within this candidate phylum. The phylogenomic organization of genomes corresponds to their niche (tongue/plaque) in our dataset, suggesting a link between environmental distribution of these genomes and their evolutionary history in the context of ribosomal proteins. in grey  (Parks et al. , 2017). The majority of the rest of the genomes originate from various publications from the Banfield Lab at UC Berkeley. The insert at the top right of the figure shows boxplots for ANI results for genomes in each subclade against all other genomes. Data points represent the ANI score for comparisons in which the alignment coverage was at least 25%.
The most prevalent tongue-associated genome and plaque-associated genome were detected in samples from 45% and 50% of the HMP individuals, respectively (Figure  4 ). In contrast, TM7x, the first cultured strain of TM7, was detected in only 5% of the HMP individuals. While the majority of the samples in the HMP dataset were taken from the tongue dorsum and supragingival plaque, there are additional oral sample types. Our analysis of these additional sample types suggested that certain TM7 populations have a preferential association with oral sites other than the tongue and supragingival plaque (Supplementary Table  7o , Supplementary Information). Of particular notice, the single MAG of clade P4 (group G5), which was previously suggested to associate with periodontitis  (Abusleme et al. , 2013)  appeared to associate with subgingival plaque, but occurred similarly in subgingival plaque metagenomes of patients with periodontitis and healthy individuals (Supplementary Table 7p-s).
The GCs can themselves be clustered into groups that show similar distribution across genomes. By computing the hierarchical clustering of GCs based on their distribution across genomes we identified a collection of 205 core GCs that are found in nearly all genomes, as well as clusters of accessory GCs, many of which were exclusively associated with oral habitats or phylogenomic clades (Figure  5a ), confirming that the agreement between phylogenomics and ecology of these genomes was also represented by differentially occurring GCs. The proportion of genes with functional hits varied dramatically between the core and accessory TM7 genes. While more than 90% of core GCs had functional annotations, NCBI's Clusters of Orthologous Groups (COGs) database  (Tatusov et al. , 2003)  annotated only 29% of singletons, and 22% to 88% of other clusters of GCs (Supplementary Table  8c ), revealing a vast number of unknown genes.
2014 , Eren et al. 2014). The oral microbiome is highly represented in genomic databases  (Vartoukian et al. , 2016; Nayfach et al. , 2016) , hence we next sought to check if the lack of genomic representation for these novel MAGs is due to low prevalence. We mapped short reads from the HMP metagenomes to these MAGs to estimate their prevalence and abundance across oral sites. Overall, the organisms represented by these novel genomes presented strong tropism for either tongue or plaque, with the exception of three populations that appear to consistently recruit reads from both plaque and tongue samples, represented by the.
Flavobacteriaceae MAGs, T_A_M_MAG_00009 (Clostridiales), and three Capnocytophaga MAGs (Figure  S7a ). While we found some populations to be rare, which could explain their lack of genomic representation in databases, other populations were extremely prevalent (Figures  S7a-c , Supplementary Table  10e -h). In addition to their high prevalence, some of these novel MAGs were highly abundant. P_B_M_MAG_00008 (Capnocytophaga) recruited on average 1% of the reads of plaque samples and two of the Propionibacterium MAGs recruited up to 18% of the reads of a single plaque metagenome, and on average 0. 7% for plaque metagenomes (Supplementary Table  10h ). S8 ). While all five Flavobacteriaceae MAGs had high sequence similarity, both ANI results and the phylogenetic analysis clustered these genomes according to the site of recovery, suggesting the existence of a plaque and tongue-specific sub-population.
Eating, drinking, and oral hygiene were otherwise permitted as was customary for the subject. Shotgun metagenomic library preparation and sequencing. For short-read library preparation, we sheared DNA using a Covaris acoustic platform. We visualized amplified libraries on an Agilent Bioanalyzer DNA1000 chip, pooled at equimolar concentrations based on these results, and size-selected to an insert size of 600 bp using a PippinPrep 2% cassette (Sage Biosciences). To quantify library pools we used a Kapa Biosystems qPCR library quantification protocol and then performed sequencing on the Illumina NextSeq in a 2x150 paired-end sequencing run using dedicated read indexing. Metagenomic assembly and processing of contigs. We used illumina-utils  (Eren et al. , 2013)  for quality filtering of short reads from the 71 metagenomes with the 'iu-filterquality-minoche' program using default parameters, which removes noisy reads using the method described in  (Minoche, Dohm & Himmelbauer, 2011).
We then used MEGAHIT  (Li et al. , 2015)  v1. 0. 6 to co-assemble the set of all quality filtered metagenomes originating from one oral site (either plaque or tongue) of one donor, for a total of 14 coassemblies. We used anvi-display-contigs-stats to get a summary of contigs statistics for each co-assembly. To process FASTA files for each of the 14 assemblies we used the contigs workflow implemented in anvi'o  (Eren et al. , 2015) , v5. 5. 1, which (1) generated an anvi'o contigs database, (  2 ) identified open reading frames using Prodigal  (Hyatt et al. , 2010)  v2. 60, (3) predicted gene-level taxonomy using Centrifuge  (Kim et al. , 2016) , (4) annotated genes with functions using the NCBI's Clusters of Orthologous Groups (COG)  (Tatusov et al. , 2003) , and (5) identified single-copy core genes using HMMER  (Eddy, 2011)  v3.
, 2002)  to calculate the average nucleotide identity (ANI) between each pair of MAGs that were estimated by CheckM to belong to the same phylum. To assign phylum affiliations to MAGs that had no phylum designation from CheckM we used phylogenomics (see below) and complimented phylogenomics with BLAST of protein sequences against the NCBI's nonredundant database. We determined that a pair of MAGs are redundant if their ANI was 99. 8% with the alignment length covering at least 50% of the shorter of the two genomes. For each group of redundant genomes, we chose the genome with the highest 'completion minus redundancy' as the representative of the group, where completion and redundancy were calculated by anvi'o based on single-copy core genes. If multiple redundant genomes had the same 'completion minus redundancy' then we selected the longest genome as the representative genome.
We merged the sequences of the collection of non-redundant bins into one FASTA file, and processed this FASTA file using the anvi'o contigs workflow as mentioned above. We then also used this FASTA file to recruit reads from all 71 metagenomes, and used the anvi'o metagenomics workflow as mentioned above to generate a merged profile database. To rapidly inspect contamination that may be missed by SCGs, we generated images that visually describe the coverage of each contig in each MAG across metagenomes. For this, we used (1) anvi-split to split each MAG into their stand-alone database files, (2) anvi-interactive with the flag --export-svg, which stores the display as an SVG without user interaction, and (3) Inkscape from terminal for SVG to PNG conversion for quick screening. We used these images to identify MAGs that required additional refinement.
For the novel Flavobacteriaceae population genome ORAL_T-B-M_MAG_00001, we followed the previously explained scaffold extension and gap closing strategies  (Chen et al. , 2019b)  to reduce the number of contigs from 48 to 8. Sequence searches in public databases. We used the NCBI nucleotide collection to search for nucleotide sequences, and the NCBI non-redundant protein sequences database to search for protein sequences. For 16S rRNA sequences, we used the 16S rRNA RefSeq Version 15. 2 (starts at position 28) through the online search tool of HOMD (http://www. homd. org) with default settings.
Read recruitment from public metagenomes. To recruit reads from Human Microbiome Project (HMP) (Human Microbiome Project Consortium, 2012) oral metagenomes we used the program 'anvi-run-workflow' with '--workflow metagenomics', which uses Snakemake  (Köster & Rahmann, 2012)  to execute the steps described above for our metagenomic read recruitment analysis. We used the same approach to also recruit reads from previously published metagenomes from periodontitis patients  (Califf et al. , 2017)  to the TM7 pangenome. Quantifying human contamination in metagenomes. We ran the aforementioned metagenomics workflow using anvi-run-workflow and used the human genome build 38 (GRCh38) from NCBI to quantify the number of reads matching the human genome in each sample. We estimated the number of reads that originate from microbes (or "nonhuman" reads) in each sample as the total number of reads minus the number of reads that mapped to the human genome.
Phylogenomic analyses. For phylogenomic analyses we used our collection of 37 ribosomal proteins that occurred both in bacterial  (Campbell et al. , 2013)  and archaeal  (Rinke et al. , 2013)  single-copy core gene collections: Ribosom_S12_S23, Ribosomal_L1, Ribosomal_L10, Ribosomal_L11,  Ribosomal_L11_N, Ribosomal_L13, Ribosomal_L14, Ribosomal_L16, Ribosomal_L18e, Ribosomal_L18p, Ribosomal_L19, Ribosomal_L2, Ribosomal_L21p, Ribosomal_L22, Ribosomal_L23, Ribosomal_L29, Ribosomal_L2_C, Ribosomal_L3, Ribosomal_L32p, Ribosomal_L4, Ribosomal_L5, Ribosomal_L5_C, Ribosomal_L6, Ribosomal_S11, Ribosomal_S13, Ribosomal_S15, Ribosomal_S17, Ribosomal_S19, Ribosomal_S2, Ribosomal_S3_C, Ribosomal_S4, Ribosomal_S5, Ribosomal_S5_C, Ribosomal_S6, Ribosomal_S7, Ribosomal_S8, Ribosomal_S9. To compute phylogenetic trees we used the program 'anvi-run-workflow' with '--workflow phylogenomics' parameter, which runs 'anvi-get-sequences-for-hmmhits' using parameters (1) '--align-with famsa' to perform alignment of protein sequences using FAMSA  (Deorowicz, Debudaj-Grabysz & Gudyś, 2016) , (2) '--concatenate-genes' to concatenate separately aligned and concatenated ribosomal proteins, (3) '--returnbest-hit' to return only the most significant hit when a single HMM profile had multiple hits in one genome, (4) `--get-aa-sequences' to output amino-acid sequence, and (4) '--hmmsources Campbell_et_al' to use the Campbell_et_al HMM source  (Campbell et al.
To root phylogenetic trees we used an outlier genome in each analysis: for Figure  1   Processing publicly available genomes. To process FASTA files, we used the program 'anvi-run-workflow' with '--workflow contigs' parameter, which includes the steps of the anvi'o contigs workflow as described above. To generate the data in Supplementary Table  8a , our workflow also included running 'anvi-run-pfams' to annotate functions with Pfams  (El-Gebali et al. 2019). We used 'anvi-get-sequences-for-gene-calls' to get all protein sequences and used GhostKoala (https://www. kegg. jp/ghostkoala/) to annotate genes with KEGG functions  (Kanehisa, Sato, and Morishima 2016). Assessing the occurrence of populations in metagenomes. We used anvi-mcgclassifier with the settings '--get-samples-stats-only', '--alpha 0.
Pangenomic analyses. To compute pangenomes in our study we used the program 'anvi-run-workflow' with '--workflow pangenomics'. Anvi'o pangenomics workflow is detailed elsewhere  (Delmont & Eren, 2018) , but briefly the pangenomic analysis used the NCBI's BLAST  (Altschul et al. , 1990)  to quantify similarity between each pair of genes, and the Markov Cluster algorithm (MCL)  (Enright, Van Dongen & Ouzounis, 2002)  (with inflation parameter of 2) to resolve clusters homologous genes. The program 'anvisummarize' created summary tables for pangenomes and 'anvi-display-pan' provided interactive visualizations of pangenomes. To simplify visualizations of complex pangenomes we removed singleton gene clusters using the parameter '--min-occurrence 2'. Average nucleotide identity (ANI). We used 'anvi-compute-ani' with parameters '-method ANIm' to align genomes using MUMmer  (Marçais et al.
, 2018)  and '--minalignment-fraction 0. 25' to only keep scores if the alignment fraction covers at least 25% percent of both genomes. For the ANI data presented in Figures  5  and 6 , we first computed ANI without the flag '--min-alignment-fraction' to get all alignment statistics, and then we imported ANI values only for pairs of genomes with alignment coverage of at least 25%. Long-read sequencing, analysis, and extraction of 16S rRNA sequences. We collected additional samples for long-read sequencing from two of the initial seven individuals (C-M and C-F) as well as a new female participant (L). To increase microbial biomass and the likelihood of getting reads from low abundance members, we pooled four daily tongue dorsum scrapings from individuals C_F and C_M. From the individual L, we collected 13 daily tongue dorsum scrapings (Supplementary Table  1d ).
To obtain high-molecular weight (HMW) DNA from these low-biomass samples, we extracted total genomic DNA using the Qiagen Genomic Tip 20/G gravity flow columns (Qiagen, Germantown, MD) using the manufacturer's protocol. We modified the lytic enzyme contamination we mapped final long-read sequences to the human genome using minimap2  (Li, 2018). We used the remaining contigs to generate anvi'o contigs databases as described above, extracted 16S rRNA gene sequences using 'anvi-get-sequences-forhmm-hits' program with '--hmm-sources Ribosomal_RNAs' parameter, and used their HOMD matches to assign group affiliation to TM7 genomes. Group affiliation of TM7 based on 16S rRNA gene sequences. We exported ribosomal RNA sequences from all TM7 genomes, including ones downloaded from NCBI. We then searched 16S rRNA sequences against the HOMD as explained above.
As this test is performed independently for each function, we computed q-values from p-values to account for multiple testing using the R package "qvalue"  (Storey & Tibshirani, 2003). To apply functional enrichment analysis to our pangemome we used the program 'anvi-get-enriched-functions-per-pan-group' using COG functions across genomes and using clade affiliation as the explanatory variable. We considered a function to be enriched if the q-value was below 0. 05; this controls the expected proportion of false positives at 0. 05. The URL http://merenlab. org/p provides details on how to use this method. Identifying prophages in TM7 genomes. We used Virsorter  (Roux et al. , 2015)  and the "Inovirus detector"  (Roux et al. , 2019)  to identify contigs that include putative phage sequences.
Statistics and visualization. We used ggplot2 version 3. 2. 1 to generate boxplots and barplots of abundances, as well as barplots of occurrences across metagenomes. To compare the number of reads recruited by our MAGs from our plaque and tongue metagenomes, we ran a two-sided Z-test, using the Python package statsmodels  (Seabold & Perktold, 2010). We finalized figures for publication using the open-source vector graphics editor Inkscape (https://inkscape. org/). Access to previously published sequences. We downloaded all oral genomes from the HOMD FTP site (ftp://ftp. homd. org/HOMD_annotated_genomes/, and ftp://ftp. homd. org/NCBI_annotated_genomes/); accession numbers are available in Supplementary Table  6b , which also includes the archeon Methanobrevibacter oralis that was used to root the phylogeny in Figure  1.
While the TM7 genomes we downloaded from IMG had no accession numbers, refined versions of these genomes have recently been published  (Cross et al. , 2019). To download genomes from GenBank we used 'ncbigenome-download' (https://github. com/kblin/ncbi-genome-download) and processed them with 'anvi-script-process-genbank-metadata' to generate input files for the anvi'o contigs workflow. We downloaded TM7 genomes from GenBank on 1/16/2019 (accession numbers provided in Supplementary.
Acknowledgements We thank  Gary Borisy  (ORCiD: 0000-0002-0266-8018 ),  Emily Fogarty  ( 0000-0002-8957-9922 ),  Evan Kiefl  ( 0000-0002-6473-0921 ) for helpful discussions, and  Elaina Graham  ( 0000-0001-5036-1929 ) for her help with GhostKOALA. ADW was funded by  NIH   R35GM133420 ; SR was supported by the  Office of Science of the U. S. Department of Energy  under contract no. DE-AC02-05CH11231 ; FED was funded by  NIH   NIDCR DE016937  and  DE024468 ; JMW was funded by  NIH NIDCR   DE022586 ; AME and JMW were supported by a  G. Unger Vetlesen Foundation  grant to the  Marine Biological Laboratory. This research was funded by the  Frank R. Lillie Research Innovation  Award.
Data availability We deposited the short-read sequencing data for amplicons and shotgun metagenomes under the NCBI BioProject PRJNA625082 (Supplementary Table lists accession numbers for each sample individually). We deposited the long-read sequencing data in the NCBI's SRA database for individuals C-F (SRR11547007), C-M (SRR11547005, SRR11547006), and L (SRR11547004). For reproducibility, reusability, and transparency, we also have made available for each individual the FASTA files for coassembled metagenomes (doi:10. 6084/m9. figshare. 12217799) and anvi'o merged profile databases (doi:10. 6084/m9. figshare. 12217802) in addition to anvi'o split profiles for each 790 MAG (doi:10. 6084/m9. figshare. 12217805) and the TM7 pangenome (doi:10. 6084/m9. figshare. 12217811).
2020). The investigation of cQTL datasets revealed SIGLEC15 as a susceptibility factor for RVVC (M Jaeger et al. 2019) as well as susceptibility pathways, such as lipid homeostasis and inflammation, that affect the response of monocytes to fungal bloodstream infections  (Martin Jaeger et al. 2019). In addition to genetic variability, external factors such as broad-spectrum antibiotics or immunosuppression regimens negatively influence the microbial community. This, in turn, affects metabolic homeostasis  (Zarrinpar et al. 2018 ) and host resistance to both antibiotic-resistant microbes and fungal pathogens  (Ubeda and Pamer 2012). For instance, preexposure to antibiotics not only increases Candida colonisation levels in the GI tract, but also facilitates disruption of the mucosal barrier and leads to C.
2014; Almeida et al. 2019). For example, a recent metagenomic analysis of the human gut microbiota from over eleven thousand individuals identified 1952 candidate bacterial species that have not yet been cultured and increased the known phylogenetic diversity of the gut microbiota almost three-fold  (Almeida et al. 2019). Furthermore, these new genomes were estimated to encode hundreds of new biosynthetic gene clusters, revealing valuable clues about the potential functionalities of these novel candidate species. In principle, full shotgun metagenomics can also be applied to mycobiota studies, but further advances are essential before fungal metagenomes can be analyzed more accurately. In particular, the lack of non-redundant and comprehensive fungal databases presents one of the most significant limitations.
The accuracy of sequence classification depends fundamentally on the quality of the reference database, and, due to the large number of microbial species that have not yet been identified or genome sequenced, the existing databases are incomplete. This problem is gradually being lessened however, by the continual addition of genomes from newly isolated species (see Culturomics) and metagenome-assembled genomes (MAGs), which can provide reasonably accurate draft genomes for uncultured organisms. Metagenomics can also be complemented by other -omic approaches to increase the power of meta-analyses. For example, metagenomics is being combined with meta-transcriptomic [i. e. the combined transcriptomes of the microbial community as a whole  (Martinez et al. 2016; Franzosa et al. 2018) ], proteomic  (Van Belkum et al.
Despite these challenges, our increased understanding of antifungal immunity and responses is offering potential immunotherapeutic opportunities  (De Luca et al. 2013; Davidson, Netea and Kullberg 2018). Effective anti-Candida vaccines, which have proven elusive for so long, are now in sight  (Cassone 2015; De Bernardis et al. 2018; Edwards et al. 2018). Our deeper comprehension of fungal immune evasion strategies affords the potential to block these phenotypes and thereby enhance the efficacy of natural antifungal immunity mechanisms  (Childers et al. 2020). Significantly, the dramatic expansion in genomic and phenotypic datasets for clinical isolates of C. albicans is providing a much clearer picture of the nature of fungal variability and in-patient evolution  (Selmecki, Forche and Berman 2006; Ford et al.