Studies of microbial community biology continue to be enriched by the growth of culture-independent sequencing and high-throughput isolate genomics (Almeida et al., 2021; Almeida et al., 2019; Forster et al., 2019; Parks et al., 2017; Pasolli et al., 2019; Poyet et al., 2019; Zou et al., 2019). Shotgun metagenomic and metatranscriptomic (i.e. ‘meta-omic’) measurements can be used to address an increasing range of questions as diverse as the transmission and evolution of strains in situ (Asnicar et al., 2017; Ferretti et al., 2018; Truong et al., 2017; Yassour et al., 2018), the mechanisms of multi-organism biochemical responses in the environment (Unified Microbiome Initiative Consortium et al., 2015; Blaser et al., 2016), or the epidemiology of the human microbiome for biomarkers and therapy (Gopalakrishnan et al., 2018; Le Chatelier et al., 2013; Thomas et al., 2019; Zeller et al., 2014). Using such analyses for accurate discovery, however, requires efficient ways to integrate hundreds of thousands of (potentially fragmentary) isolate genomes with community profiles to detect novel species and strains, non-bacterial community members, microbial phylogeny and evolution, and biochemical and molecular signaling mechanisms. Correspondingly, this computational challenge has necessitated the continued development of platforms for the detailed functional interpretation of microbial communities.

The past decade of metagenomics has seen remarkable growth both in terms of biology accessible via high-throughput sequencing and in terms of methods for doing so. Beginning with the now-classic questions of ‘who’s there?’ and ‘what are they doing?’ in microbial ecology (Human Microbiome Project Consortium, 2012), shotgun metagenomics provide a complementary means of taxonomic profiling to amplicon-based (e.g. 16S rRNA gene) sequencing, as well as functional profiling of genes or biochemical pathways (Morgan et al., 2013; Quince et al., 2017; Segata et al., 2013). More recently, metagenomic functional profiles have been joined by metatranscriptomics to also capture community regulation of gene expression (IBDMDB Investigators et al., 2019). Methods have been developed to focus on all variants of particular taxa of interest within a set of communities (Pasolli et al., 2019; Truong et al., 2017), to discover new variants of gene families or biochemical activities (Franzosa et al., 2018; Kaminski et al., 2015), or to link the presence and evolution of closely related strains within or between communities over time, space, and around the globe (Beghini et al., 2017; Karcher et al., 2020; Tett et al., 2019). Critically, all these analyses (and the use of the word ‘microbiome’ throughout this manuscript) are equally applicable to both bacterial and non-bacterial community members (e.g. viruses and eukaryotes) (Beghini et al., 2017; Olm et al., 2019; Yutin et al., 2018). Finally, although not addressed in depth by this study, shotgun meta-omics have increasingly also been combined with other community profiling techniques such as metabolomics (Heinken et al., 2019; Lloyd-Price et al., 2017; Sun et al., 2018) and proteomics (Xiong et al., 2015) to provide richer pictures of microbial community membership, function, and ecology.

Methods enabling such analyses of meta-omic sequencing have developed in roughly two complementary types, either relying on metagenomic assembly or using largely assembly-independent, reference-based approaches (Quince et al., 2017). The latter is especially supported by the corresponding growth of fragmentary, draft, and finished microbial isolate genomes, and their consistent annotation and clustering into genome groups and pan-genomes (Almeida et al., 2021; Almeida et al., 2019; Pasolli et al., 2019). Most such methods focus on addressing a single profiling task within (most often) metagenomes, such as taxonomic profiling (Lu et al., 2017; Milanese et al., 2019; Truong et al., 2015; Wood et al., 2019), strain identification (Luo et al., 2015; Nayfach et al., 2016; Scholz et al., 2016; Truong et al., 2017), or functional profiling (Franzosa et al., 2018; Kaminski et al., 2015; Nayfach et al., 2015; Nazeen et al., 2020). In a few cases, platforms such as bioBakery (McIver et al., 2018), QIIME 2 (Bolyen et al., 2019), or MEGAN (Mitra et al., 2011) integrate several such methods within an overarching environment. While not a primary focus of this study, metagenomic assembly methods enabling the former types of analyses, including novel organism discovery or gene cataloging (Lesker et al., 2020; Stewart et al., 2019), have also advanced tremendously (Li et al., 2015; Nurk et al., 2017). Assembly-based analyses are now reaching a point of integrating microbial communities and isolate genomics as well, particularly for phylogeny (Asnicar et al., 2020; Zhu et al., 2019). These efforts have also led to increased consistency in microbial systematics and phylogeny, facilitating the types of automated, high-throughput analyses necessary when manual curation cannot keep up with such rapid growth (Asnicar et al., 2020; Chaumeil et al., 2019).

Here, to further increase the scope of feasible microbial community studies, we introduce a suite of updated and expanded computational methods in a new version of the bioBakery platform. bioBakery 3 includes updated sequence-level quality control and contaminant depletion guidelines (KneadData), MetaPhlAn 3 for taxonomic profiling, HUMAnN 3 for functional profiling, StrainPhlAn 3 and PanPhlAn 3 for nucleotide- and gene-variant-based strain profiling, and PhyloPhlAn 3 for phylogenetic placement and putative taxonomic assignment of new assemblies (metagenomic or isolate). Most of these tools leverage an updated ChocoPhlAn 3 database of systematically organized and annotated microbial genomes and gene family clusters, newly derived from UniProt/UniRef (Suzek et al., 2007) and NCBI (NCBI Resource Coordinators and Coordinators, 2014). Our quantitative evaluations show each individual tool to be more accurate and, typically, more efficient than its previous version and other comparable methods, increasing sensitivity and specificity by sometimes more than twofold (e.g. in non-human-associated microbial communities). Biomarker identifications in 1262 colorectal cancer (CRC) metagenomes, 1635 inflammatory bowel disease (IBD) metagenomes, and 817 metatranscriptomes show both the platform’s efficiency and its ability to detect hundreds of species and thousands of gene families not previously profiled. Finally, in 4077 human gut metagenomes containing Ruminococcus bromii, the bioBakery 3 platform permits an initial integration of assembly- and reference-based metagenomics, discovering a novel biogeographical and functional structure within the clade’s evolution and global distribution. All components are available as open-source implementations with documentation, source code, and workflows enabling provenance, reproducibility, and local or cloud deployment at http://segatalab.cibio.unitn.it/tools/biobakery and http://huttenhower.sph.harvard.edu/biobakery.

bioBakery provides a complete meta-omic tool suite and analysis environment, including methods for individual meta-omic (and other microbial community) processing steps, downstream statistics, integrated reproducible workflows, standardized packaging and documentation via open-source repositories (GitHub, Conda, PyPI, and R/Bioconductor), grid- and cloud-deployable images (AWS, GCP, and Docker), online training material and demonstration data, and a public community support forum. For any sample set, quality control, taxonomic profiling, functional profiling, strain profiling, and resulting data products and reports can all be generated with a single workflow, while maintaining version control and provenance logging. All of the methods themselves, the associated training material, quality control using KneadData, and packaging for distribution and use have been updated in this version. For example, Docker images have been scaled down in size to optimize use in cloud environments, and workflows have been ported to AWS (Amazon Web Services) Batch and Terra/Cromwell (Google Compute Engine) to reduce costs through the use of spot and pre-emptive instances, respectively. All base images and dependencies have been updated as well, including the most recent Python (v3.7+) and R (v4.0+, see Materials and methods). New and updated documentation of all tools, including detailed instructions on installation in different environments and package managers, is available at http://huttenhower.sph.harvard.edu/biobakery.

High-quality reference sequences for improved meta-omic profiling

The majority of methods within the bioBakery 3 suite leverage a newly updated reference genome and gene cataloging procedure, the results of which are packaged as ChocoPhlAn 3 (Figure 1A). ChocoPhlAn uses publicly available genomes and standardized gene calls and gene families to generate markers for taxonomic and strain-level profiling of metagenomes with MetaPhlAn 3, StrainPhlAn 3, and PanPhlAn 3, phylogenetic profiling of genomes and MAGs with PhyloPhlAn 3, and functional profiling of metagenomes with HUMAnN 3.

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ChocoPhlAn 3 is based on a genomic repository of 99.2 k high-quality, fully annotated reference microbial genomes from 16.8 k species available in the UniProt Proteomes portal as of January 2019 (The UniProt Consortium, 2019) and the corresponding functionally-annotated 87.3M UniRef90 gene families (Suzek et al., 2015). From this resource, ChocoPhlAn initially generates annotated species-level pangenomes associating each microbial species with its sequenced genomes and repertoire of UniRef-based gene (nucleotide) and protein (amino acid sequence) families. These pangenomes provide a uniform shared resource for subsequent profiling across bioBakery 3. HUMAnN 3 and PanPhlAn 3 are directly based on complete pangenomes for overall functional and strain profiling, whereas other tools use additional information annotated onto the catalog. PhyloPhlAn 3 focuses on the subset of conserved core gene families (i.e. present in almost all strains of a species) for inferring accurate phylogenies, and both MetaPhlAn 3 and StrainPhlAn 3 further refine core gene families into species-specific unique gene families to generate unambiguous markers for metagenomic species identification and strain-level genetic characterization.

MetaPhlAn 3 and HUMAnN 3 expand the link between the microbiome and colorectal cancer with a meta-analysis of 1262 metagenomes

To illustrate the potential of bioBakery 3’s updated profiling tools and to extend our understanding of the microbial signatures in colorectal cancer (CRC), we expanded our previous work to meta-analyze both existing and newly available CRC metagenomic cohorts for a total of 1262 samples (600 control and 662 CRC samples) from nine different datasets spanning eight different countries (Feng et al., 2015; Gupta et al., 2019; Thomas et al., 2019; Vogtmann et al., 2016; Wirbel et al., 2019; Yachida et al., 2019; Yu et al., 2017; Zeller et al., 2014). The resulting integrated profiles are available for download (Supplementary file 5) and included in the new release of curatedMetagenomicData (Pasolli et al., 2017).

MetaPhlAn 3 identified a total of 1083 species detected at least once (172 considered ‘prevalent’ when defined as present in >5% of samples at >0.1% relative abundance), of which 505 species (52 prevalent) were previously not reported by MetaPhlAn 2 due to the expansion of the genome database (or in some cases because of changes in the NCBI taxonomy). In addition, 82 species present in the MetaPhlAn 2 database were not detected by MetaPhlAn 2 but are now identified in the samples by MetaPhlAn 3, due to the expanded sequence catalog, improved marker discovery procedure, and increased sensitivity to low-abundance species (Thomas et al., 2019).

We found 121 species significantly associated with CRC (FDR q < 0.05 and Q-test for heterogeneity >0.05; Supplementary file 6) by a meta-analysis of standardized mean differences using a random-effects model on arcsine-square-root-transformed relative abundances (see Materials and methods). Compared to MetaPhlAn 2 when run on the same data, this includes 60 additional species that reached significance in the meta-analysis, confirming that the updated methods lead to improved biomarker discovery. Among them, three additional species not identified in previous MetaPhlAn-2-based analysis (Thomas et al., 2019) were among those most strongly associated with CRC (effect size >0.35): Dialister pneumosintes, Ruthenibacterium lactatiformans, and Eisenbergiella tayi (Figure 2B, Figure 2—figure supplement 1, Figure 2—figure supplement 2A). Dialister pneumosintes is typically oral, further reinforcing the role of oral taxa in CRC, and R. lactatiformans was reported as part of a consortium of bacteria able to increase colonic IFNγ+T cells (Tanoue et al., 2019). The increased number of species detectable by MetaPhlAn 3 also strengthened the previously observed pattern of greater richness in CRC-associated microbiomes. This pattern has been found previously both with MetaPhlAn 2 (Thomas et al., 2019) and with mOTUs2 (Wirbel et al., 2019), in large part due to low-level detection of typically oral microbes in addition to the baseline gut microbiome during CRC. Improved MetaPhlAn 3 profiling in this study allowed the difference to achieve even stronger statistical significance (Figure 2C).

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Functional profiling of this expanded CRC meta-analysis with HUMAnN 3 identified 4.3M UniRef90 gene families, corresponding to 549 MetaCyc pathways and 2895 ECs. Out of these 4.3M gene families, the meta-analysis identified 206,296 significantly associated with CRC and controls (FDR q < 0.05), a substantial increase from the 64,315 gene families previously identified with the meta-analysis based on HUMAnN 2 profiles (FDR q < 0.05). In addition, 120 MetaCyc pathways were significantly associated with CRC (Wilcoxon rank-sum test FDR q < 0.05 and Q-test for heterogeneity >0.05) (Figure 2—figure supplement 2B), of which 59 (49.1%) overlapped previous results, including for example the increased abundance of starch degradation V (Supplementary file 6) in healthy individuals. This pathway encodes functions for extracellular breakdown of starch by an amylopullulanase enzyme, which has both pullulanase and α-amylase activity (Flint et al., 2012). Bifidobacterium breve and other Bifidobacterium spp have been shown to encode amylopullulanases and attach to starch particles, and they have also been reported for their potential protective role against carcinogenesis here and previously (Sivan et al., 2015). Among the 20 disease-associated pathways with the highest significance, only three were present in the previous meta-analysis, with the majority exhibiting significant heterogeneity in the random effects model (possibly due to the inclusion of additional geographically distinct cohorts here). Large and diverse cohorts combined with improved taxonomic and functional profiling available via bioBakery 3 thus have the possibility to extend and refine microbiome biomarkers in CRC and other conditions.

Improvements in HUMAnN 3 also allowed us to directly test specific functional hypotheses in the context of the CRC microbiome. Specifically, we previously showed that the abundance of the microbial gene encoding for the choline trimethylamine-lyase (cutC) responsible for production of the disease-associated trimethylamine and trimethylamine N-oxide (Kalnins et al., 2015; Kummen et al., 2017; Oellgaard et al., 2017; Rath et al., 2017) (TMA/TMAO) is significantly higher in CRC patients (Thomas et al., 2019). This association was previously detected using a customized ShortBRED database (Kaminski et al., 2015) due to the lack of appropriate reference sequences previously available to HUMAnN 2. HUMAnN 3 was instead able to directly profile relative abundances of 113 UniRef90 gene families annotated as cutC orthologs and identified 909 metagenomes in this data collection carrying at least one gene family annotated as cutC. These confirmed an increase of cutC relative abundance in CRC samples compared to controls (Wilcoxon rank-sum test p<0.05 in six of the nine datasets, meta-analysis p<0.0001). Interestingly, a meta-analysis performed on the relative abundances of the L-carnitine dioxygenase gene (yeaW), a gene also involved in trimethylamine synthesis, revealed only weak associations with disease status (Wilcoxon rank-sum test p<0.05 in three of the nine datasets, meta-analysis p=0.095, Figure 2—figure supplement 3, Figure 2—figure supplement 4), possibly reflecting a stronger effect of dietary choline on CRC risk compared to carnitine.

MetaPhlAn 3 and HUMAnN 3 also proved accurate when combining CRC microbiomes using more purely discriminative models such as random forests (RFs), reaching 0.85 average AUC for CRC (vs. control) sample classification in leave-one-dataset-out evaluations using taxonomic features (LODO, minimum 0.76 for the YachidaS_2019 and ThomasAM_2019_a datasets, maximum 0.97 for the GuptaA_2019 dataset; Figure 2E, Figure 2—figure supplement 5). As in previous studies (Pasolli et al., 2016; Thomas et al., 2019), RFs using functional features performed similarly (0.69 cross validation and 0.71 LODO ROC AUC on pathways relative abundance), indicating a tight link between strain-specific taxonomy and gene carriage in this setting. When the classification model was used for assessing features’ importance, several new taxa were identified compared to MetaPhlAn 2 and metabolic pathways or EC-numbers relative to HUMAnN 2 (Figure 2—figure supplement 5), further confirming the relevance of the new reference sequences and annotations available to be profiled in bioBakery 3.

Longitudinal taxonomic and functional meta-omics of IBD

To further demonstrate the utility of MetaPhlAn 3 and HUMAnN 3 on combined meta-omic sequencing datasets, including identification of expression-level biomarkers, we applied the updated methods to 1635 shotgun metagenomes (MGX) and 817 shotgun metatranscriptomes (MTX) derived from the stool samples of the HMP2 Inflammatory Bowel Disease Multi-omics Database (IBDMDB) cohort (http://ibdmdb.org; see Materials and methods). Compared with previously published profiles of the samples generated with MetaPhlAn 2 and HUMAnN 2 (IBDMDB Investigators et al., 2019; Figure 3A), the v3 methods’ profiles (i) identified more species pangenomes (MGX medians 40 vs. 48, MTX medians 40 vs. 47); (ii) explained larger fractions of sample reads by mapping to pangenomes (MGX medians 54 vs. 63%, MTX medians 12 vs. 22%); and (iii) explained larger total fractions of sample reads after falling back to translated search (MGX medians 69 vs. 75%, MTX medians 20 vs. 31%). Note that reduced MTX mapping rates (relative to MGX rates) result from enrichment for high-quality but non-coding RNA reads, which are unmapped by design in both HUMAnN 2 and 3. The v3 profiles thus promise increased understanding even of an already well-characterized dataset.

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To that end, we applied a mixed-effects model to identify microbial biomarkers of disease activity within the Crohn’s disease (CD) and ulcerative colitis (UC) subpopulations of the HMP2 cohort (see Materials and methods). More specifically, we examined HUMAnN 3-based abundance profiles of EC families from 817 paired HMP2 metagenomes and metatranscriptomes in search of differences in functional activity between active (dysbiotic) and inactive (non-dysbiotic) time points from longitudinally sampled CD and UC patients. We identified 558 ECs whose residual expression was significantly different (FDR q < 0.05) in active CD compared with inactive CD (Figure 3B): a 66% increase compared to an identical analysis incorporating EC abundance profiles generated by HUMAnN 2 (Figure 3—figure supplement 1). We identified only one EC that was differentially expressed in active UC (protein O-GlcNAcase, EC 3.2.1.169). This relative absence of biomarkers for active UC may result both from its generally more benign phenotype (IBDMDB Investigators et al., 2019) and from the smaller number of active UC samples (n = 23) compared with active CD samples (n = 76); as a result, we focused our subsequent analyses on expression differences within the CD subcohort.

Of the >500 significantly differentially expressed ECs in active CD, all but one were ‘over-expressed’ (i.e. their residual expression after controlling for DNA copy number was higher than expected in active CD; see Figure 3B). Hence, while many species (and their encoded functions) are known to be lost entirely during active IBD (IBDMDB Investigators et al., 2019), it seems to be rare for functions to be maintained by the community but not utilized. The one notable example of an ‘under-expressed’ function was galactonate dehydratase (EC 4.2.1.6; Figure 3C). This enzyme was encoded and highly expressed by Faecalibacterium prausnitzii in both control and inactive CD samples. While galactonate dehydratase was still metagenomically abundant in active CD (where it was contributed primarily by Escherichia coli), it was not highly expressed under those conditions. Related observations were made previously using a mouse model of colitis monocolonized with commensal E. coli (Patwa et al., 2011). There, microarray-based measurements found a number of enzymes in the galactonate utilization pathway, including galactonate dehydratase, to be among the most strongly down-regulated in comparison with wild-type mice. These results suggest that galactonate metabolism is either infeasible (e.g. due to low bioavailability) or otherwise suboptimal (e.g. due to the presence of preferred energy sources) in the inflamed gut, thus leading to its down-regulation by ‘generalist’ pathobionts like E. coli.

From the many over-expressed functions in active CD, we focused for illustrative purposes on examples that were encoded non-trivially by species either new or newly classified in MetaPhlAn 3 (‘3.0-new species’; Figure 3C). To aid in this process, we defined an h-index-inspired ‘novelty’ score (s) for each EC equal to the largest percentile p of samples with the EC in which p percent of its copies were contributed by 3.0-new species. For example, an EC with s = 0.25 indicates that at least 25% of the EC’s copies were from 3.0-new species in at least 25% of samples with the EC. The previously mentioned galactonate dehydratase thus had a low novelty score (s = 0.11) resulting from dominant contributions of F. prausnitzii and E. coli (which are not new to MetaPhlAn 3).

Conversely, the highest novelty score was observed for glutamyl-tRNA reductase (EC 1.2.1.70, s = 0.46), a highly-transcribed housekeeping gene that received large contributions from the 3.0-new species Roseburia faecis, Phascolarctobacterium faecium, and Ruminococcus bicirculans. Betaine reductase (EC 1.21.4.4, s = 0.43), instead, is much more specific and was contributed in part by 3.0-new species Hungatella hathewayi; this is notable as a rare example of a function that was often detectable from community RNA but not DNA (indicating high expression from a small pool of gene copies). Pyruvate, water dikinase (EC: 2.7.9.2) and Ribonuclease E (EC 3.1.26.12) were among the strongest signals of over-expression in active CD by both effect size and statistical significance; these functions were also characterized by large contributions of 3.0-new species (s = 0.36 and 0.38, respectively). Ribonuclease E and a final example, hydrogen dehydrogenase NADP(+) (EC 1.12.1.3), are also representative of the degree to which metagenomic copy number (DNA abundance) tends to be a strong driver of transcription (RNA abundance) in the gut microbiome, and thus the need to account for the former when estimating functional activity. The 3.0-new H. hathewayi expresses this enzyme highly in a subset of active CD samples, thus contributing to the enzyme’s overall association with active CD.

Population-scale subspecies genetics (StrainPhlAn) and pangenomics (PanPhlAn) of Ruminococcus bromii

Strain-level characterization of taxa directly from metagenomes is an effective cultivation-free means to profile the population structure of a microbial species across geography or other conditions (Scholz et al., 2016; Truong et al., 2017) and to track strain transmission (Ferretti et al., 2018). These functionalities are incorporated into (i) StrainPhlAn 3, which infers strain-level genotypes by reconstructing sample-specific consensus sequences from MetaPhlAn 3 markers (Zolfo et al., 2019) (ii) PanPhlAn 3, which identifies strain-specific combination of genes from species’ pangenomes; and (iii) PhyloPhlAn 3, which performs precise phylogenetic placement of isolate and metagenome-assembled genomes (MAGs) using global and species-specific core genes (Asnicar et al., 2020) (see Materials and methods). ChocoPhlAn 3 automatically quantifies and annotates the distinct types of conservation metrics necessary to identify these markers, all updated in bioBakery 3 (Supplementary file 2).

Ruminococcus bromii is a common gut microbe that is surprisingly understudied due to its fastidious anaerobicity and general non-pathogenicity (Ze et al., 2012). R. bromii is prevalent in over half of typical gut microbiomes, but large-scale comparative genomic analyses of this species are not available with only one previous investigation (Mukhopadhya et al., 2018) limited to the five reference genomes available at the time. This made R. bromii population genetics, geographic association, and genomic variability of particular interest to assess via StrainPhlAn and PanPhlAn. From the meta-analysis of 7783 gut metagenomes integrated for a previous study (Pasolli et al., 2019), we considered the 4077 metagenomes in which R. bromii was found present with a relative abundance above 0.05% according to MetaPhlAn 3. StrainPhlAn analysis based on single-nucleotide variants (SNVs) of the 124 R. bromii-specific marker genes across the 3375 samples with sufficient markers’ coverage (see Materials and methods) revealed a complex population structure not previously recapitulated by the only 15 genomes available from isolate sequencing (Figure 4A). Sub-clade prediction (see Materials and methods) highlighted two sub-species clades that are particularly divergent within the phylogeny (Figure 4—figure supplement 1C–D); interestingly, the first one (Cluster 1) is mainly composed of strains retrieved from Chinese subjects and from cohorts with a rural and a more pre-industrial traditional lifestyle and diet (Pasolli et al., 2019; Figure 4A; Cluster 1; Fisher’s exact test p<2.2e-16). StrainPhlAn 3 can thus rapidly reconstruct complex strain-level phylogenies from metagenomes (5,700 s using 20 CPUs), and with the integration of PhyloPhlAn 3’s improvements specifically for strain-level manipulation of alignments and phylogenies (Asnicar et al., 2020), surpasses the previous version of the software in accuracy and sensitivity (67.4% more strain profiled, Figure 4—figure supplement 1A–B).

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StrainPhlAn 3 also extends the ability of reference-based approaches to infer the genetic identity of strains across samples as previously explored (Ferretti et al., 2018; Truong et al., 2017). Specifically for R. bromii, different individuals tend to carry different strains diverged with a roughly normal distribution of genetic identities (mean 3.54e-3 normalized phylogenetic distance, Figure 4B). However, the genetic differences between Cluster 1 and Cluster 2 were generally greater, with a lower peak and higher distances (mean 6.1e-3, Figure 4B). For carriers of either clade, within-subject strain retention tended to be high as expected (i.e. low divergence); at distinct time points (average 261.35 s.d. 239.86 days, first quartile 72 days, third quartile 386 days, 3537 comparisons in total), most of the strain distances (76.4%) approached zero (compared to 1% of comparisons for inter-individual differences, Figure 4B). In addition to detecting these two genetically distinct clades and quantifying within-individual strain retention, a final distribution of higher intra-individual distances clearly captured (rare) strain replacement by R. bromii strains (i) in the same or (ii) in a different main cluster in the species’ phylogeny. Mother-infant pairs showed a similar dynamic (Figure 4B), with sporadic vertical transmission (~33.3%) (Ferretti et al., 2018; Korpela et al., 2018; Yassour et al., 2018) mixed with strain loss, replacement, and acquisition from other environmental or human sources (Korpela et al., 2018). This analysis highlighted the high precision of StrainPhlAn 3 in detecting strain identity across samples and thus the potential of using it for tracking the transmission network of specific individual strains within and between subjects.

PanPhlAn 3 provides a complementary form of strain analysis by constructing pangenome presence-absence (rather than individual nucleotide variant) genotypes (see Materials and methods). Using eight R. bromii reference genomes, PanPhlAn 3 revealed the presence of 6151 UniRef90 pangenes across 2679 samples with sufficient depth to permit confident strain-specific gene repertoire reconstruction (Figure 4C). This mirrored the genetic divergence of R. bromii Clusters 1 and 2, while also highlighting a range of functional differences annotatable to genes unique to the two clusters: Cluster 1 and Cluster 2 showed a total of 797 and 601 UniRef90 families specific to them (Fisher's exact test, FDR q < 0.05). Although most of these gene families do not have precise functional annotations, these sets of genes should be prioritized in experimental characterization efforts to unravel the sub-species diversity of R. bromii, and UnireRef90-to-GO ID mapping also highlighted an enrichment of membrane proteins in Cluster 2. Interestingly, other clusters of co-occurring genes were independent of phylogenetic structure and also verified to be on the same locus on at least two reference genomes in the PanPhlAn 3 database (Figure 4C) providing a new approach at identifying and annotating potential laterally-mobile elements.

StrainPhlan 3 and PanPhlAn 3 can thus be combined with PhyloPhlAn 3 (Asnicar et al., 2020) and HUMAnN 3 to provide multiple, complementary, culture-independent means to investigate the strain-level diversity of taxa in the microbiome, from new data or by re-using thousands of publicly available metagenomes. It is notable that these approaches tend to be consistent with each other (e.g. for R. bromii, Pearson’s r = 0.632, p<2.2e-16, Figure 4D), while providing different benefits and drawbacks: PanPhlAn used with HUMAnN input is computationally efficient, used from whole pangenomes has higher sensitivity, and StrainPhlAn tends to have higher specificity. As these methods unravelled the population genomic structure of R. bromii that was not previously known, they can be similarly applied to hundreds of other host-associated or environmental microbial species to uncover their phylogenetic, functional, and transmission characteristics. Together, the bioBakery 3 components provide an integrated platform for applying strain-level comparative genomics, taxonomic, and functional profiling to meta-omic microbial community studies.

Here, we introduce and validate the set of expanded microbial community profiling methods making up the bioBakery 3 platform, including quality control (KneadData), taxonomic profiling (MetaPhlAn), strain profiling (StrainPhlAn and PanPhlAn), functional profiling (HUMAnN), and phylogenetics (PhyloPhlAn), largely relying on the underlying data resource of ChocoPhlAn 3 genomes and pangenomes. These modules are each more accurate and, often, more efficient than their previous versions and current alternatives, particularly for challenging (e.g. non-human-associated) metagenomes and for multi-omics (e.g. metatranscriptomes). While the improvements were in large part a consequence of the much larger database of reference genomes that the system can now handle, additional algorithmic changes (Supplementary file 2) were instrumental to provide more complete reporting and higher accuracy for references already available in previous releases. In the process of these evaluations, we detected three species newly associated with CRC (Dialister pneumosintes, Ruthenibacterium lactatiformans, and Eisenbergiella tayi), over 500 enzyme families metatranscriptomically upregulated by diverse microbes in IBD, and two new phylogenetically, genomically, and biogeographically distinct subclades of Ruminococcus bromii.

These results highlight the degree to which meta-omic approaches can now realize the potential of culture-independent sequencing for characterizing microbial community dynamics, interactions, and evolution that are only active in situ and not in vitro. Since early studies of environmental and host-associated microbial communities (Gill et al., 2006; Tyson et al., 2004; Venter et al., 2004), it has been clear that many aspects of intercellular and inter-species signaling, short- and long-term evolution, and regulatory programs are exercised by microbes in their natural settings and extremely difficult to recapitulate in a controlled setting. This is supported by the extent to which ‘dark matter’ not previously characterized in the laboratory pervades host-associated and (especially) environmental metagenomes (Parks et al., 2017), with most communities containing a plurality, majority, or sometimes supermajority of novel and/or uncharacterized sequences (Almeida et al., 2019; Pasolli et al., 2019). bioBakery 3 begins to overcome this challenge by combining a greatly expanded set of reference sequences with ways of ‘falling back’ gracefully when encountering new sequences, while also paving the way for further integration of assembly-based discovery in the future (discussed below). Critically, this now permits large collections of meta-omes to be used in ways only previously possible with large isolate genome or transcriptome collections, for example strain-level integrative comparative genomics, near-real-time epidemiology and evolution, and detailed gene content prediction and metabolic modeling. Results such as the heterogeneity of maternal-infant strain transmission and retention, or the globally stratified distribution of subspecies clades, would be extremely challenging to discover by other means.

Methodologically, it is notable that these new meta-omic analysis types have been enabled by several years of improved experimental fidelity, denoising, and quality control approaches. These effectively retain only the ‘best’ subset of reads from large, noisy meta-omes for each analysis of interest, for example only the most unique sequences for taxonomic identification, or only the most evolutionarily informative loci for phylogeny. Meta-omes are uniquely positioned for broad reuse and discovery since different ‘best’ subsets of each dataset can be used to answer different questions. The development of meta-omic analysis methods thus parallels that of genome-wide association studies or transcriptomics, in as much as early methods were later refined to provide much greater accuracy and scalability through removal of low-quality measurements, within- and between-study normalization approaches, statistical methods to reliably separate signal from noise, and biological annotation of previously uncharacterized loci. Similarly, methods for amplicon-based community profiling have progressed from noise- and chimera-prone stitching and clustering to near-exact sequence variant tracking (Callahan et al., 2016). Fortunately, continued decreases in sequencing prices and increases in protocol efficiency have now made shotgun meta-omics nearly as affordable as amplicon sequencing in many settings. The challenge, of course, is that each metagenome combines many different noise sources: there is no single, whole genome to finish; host, microbial, and contaminant sequences are not always easily differentiated; there is no one set of ‘true’ underlying variants (since each organism might be represented by multiple strains); and millions of microbial gene products remain functionally uncharacterized (Thomas and Segata, 2019).

Notably, bioBakery provides one of very few environments currently capable of integrating both metagenomes and metatranscriptomes to begin overcoming these uncertainties (Franzosa et al., 2018). As introduced above, microbial community transcriptomes can be highly unintuitive to interpret, as transcript abundance is always influenced both by expression level and by underlying DNA copy number, that is abundance of the expressing taxon. Since both sequence-based DNA and RNA profiles are typically compositional (relative, not absolute, abundances), there is not always a simple way to account for these effects. HUMAnN 3 provides initial within- and between-species normalization options that can be combined with the statistical models of differential expression described here, making for example the >500 transcripts overexpressed in Crohn’s disease particularly noteworthy. Hungatella hathewayi was uniquely responsible for many of these, an organism not previously associated with IBD in humans (Schaubeck et al., 2016). While many of its overexpressed transcripts are core or housekeeping processes, indicative of general bioactivity in the inflamed gut (comparable to that of for example Escherichia coli IBDMDB Investigators et al., 2019), others such as betaine reductase are much more specific. This enzyme contributes directly to trimethylamine (TMA) formation (Rath et al., 2019), one of the more noteworthy microbial metabolites implicated in human disease via its transformation to proatherogenic trimethylamine-oxide (TMAO) (Tang et al., 2013). Conversely, the only transcript differentially regulated in ulcerative colitis, underexpressed F. prausnitzii galactonate dehydratase, contrasts its utility in polysaccharide degradation under non-inflamed conditions with the upregulation of alternative, more host-antagonistic energy sources in E. coli during inflammation (IBDMDB Investigators et al., 2019). Both of these examples are only analyzable due to the highly specific assignment of meta-omic reads to individual community members’ gene families, in combination with appropriate downstream statistical methods for multi-omics.

Finally, it is striking that metagenomically-derived comparative genomics has only recently been able to reach the scale and scope previously possible with microbial isolates. The genomic epidemiology of pathogens has driven the latter – recently in viral outbreaks such as COVID-19 (Lu et al., 2020) and Ebola (Gire et al., 2014), and in many bacterial conditions such as cholera (Weill et al., 2017) or pneumonia (Croucher et al., 2011). Since metagenomes can simultaneously access all community members with relatively little bias, such studies are now possible with organisms previously overlooked due to the absence of obviously associated phenotypes or convenient culture techniques (Manara et al., 2019; Pasolli et al., 2019). Ruminococcus bromii is one such example; despite being over 50% prevalent among typical human gut communities, only 15 isolates were previously sequenced, precluding epidemiology or phylogenetics. In addition to making a novel sub-species phylogenetic and biogeographic structure apparent, the combination of MetaPhlAn, HUMAnN, PanPhlAn, StrainPhlAn, and PhyloPhlAn together confirmed that most R. bromii strains are ‘personal’ (i.e. specific to and retained within individuals, like most microbiome members), rarely transmissible across hosts, and that genomic differences characterize each subspecies (suggesting a degree of functional adaptation and specialization). Such results are in principle possible with any combination of metagenomic and isolate taxa and genes of interest, richly integrating culture-independent data with hundreds or thousands of isolate genomes.

Of course, many challenges remain both for improvement of the bioBakery platform and for the field as a whole. Both experimental and computational accessibility of non-bacterial microbial community members remains limited. While accurate, bioBakery 3’s capacity for non-bacterial profiling is only slightly improved from the previous version by the expansion of available eukaryotic microbial reference sequences. These components of metagenomes – and, for RNA viruses, metatranscriptomes – are often measured with surprising heterogeneity during the initial generation of sequencing data themselves (Zolfo et al., 2019), suggesting necessary improvements in analytical quality control and normalization as well. The visibility of species with particularly high genetic diversity within individual communities also remains limited; in most cases, only the most dominant strain of each taxon per community is currently analyzable, again for both experimental (e.g. sequencing depth) and analytical reasons (Quince et al., 2017). This is true both for reference-based and for assembly-based approaches, the latter of which are often also stymied by highly diverse taxa (Pasolli et al., 2019). A final area of improvement for bioBakery, relatedly, is the increased integration between reference-based and assembly-based approaches – begun here via PhyloPhlAn 3 – in order to better leverage MAGs (Almeida et al., 2021), SGBs (Pasolli et al., 2019), and novel gene families.

We thus anticipate improved integration of reference- and assembly-based meta-omic analyses to be one of the main areas of future development for bioBakery, along with expanded methods for other types of multi-omics in addition to transcription. There will also be a continued focus on quality control and precision, enabling new types of functional analysis within microbial communities (e.g. bioactivity and gene function prediction) without sacrificing sensitivity to rare or novel community members. We also expect to better-integrate long-read sequencing data, both via long-read mapping to reference sequences and through their contributions to better assembly-based approaches. Finally, we are also committed to the platform’s availability with well-documented, open-source implementations, training material, and pre-built locally-executable and cloud-deployable packaging. Feedback on any aspect of the methods or their applications in diverse host-associated or environmental microbiome settings can be submitted at https://forum.biobakery.org, and we hope bioBakery will continue to provide a flexible, convenient, reproducible, and accurate discovery platform for microbial community biology.

bioBakery 3 is a set of computational methods for the analysis of microbial communities from meta-omic data that produce taxonomic, functional, phylogenetic, and strain-level profiles to be interpreted directly or included in downstream statistical analyses (Figure 1A). After read-level quality control by KneadData, MetaPhlAn 3 estimates the set of microbial species (and corresponding higher taxonomic clades) present in a sample and their relative abundances. StrainPhlAn 3 deepens genetic characterization by refining strain-level genotypes of species identified by MetaPhlAn 3. HUMAnN 3 focuses instead on the identification and quantification of the molecular functions encoded in the metagenome or expressed in the metatranscriptome, which can be resolved by PanPhlAn 3 into gene presence-absence strain-level genotypes. PhyloPhlAn 3, as previously reported (Asnicar et al., 2020), provides a comprehensive means to interpret the draft genomes produced by assembly-based metagenomic tools. These bioBakery 3 modules are generally based on an underlying dataset of functionally-annotated isolate microbial genes and genomes produced by ChocoPhlAn 3 to quality-control and annotated UniProt derivatives. This currently includes 99,227 genomes and 87.3M gene families, almost 100-fold greater than the data types included in the first bioBakery release (Segata and Huttenhower, 2011).

Human and murine synthetic metagenomes and gold standards provided by the CAMI Challenge are available at https://data.cami-challenge.org/participate. Non-human synthetic metagenomes and gold standards are available at http://segatalab.cibio.unitn.it/tools/biobakery/. CRC metagenomic datasets analyzed in the meta-analysis are available in BioProject under accession numbers PRJEB7774, PRJNA531273, PRJNA447983, PRJDB4176, PRJEB12449, PRJEB27928, PRJDB4176, PRJEB10878, and PRJEB6070. Sequences and data for the Integrative Human Microbiome Project are available at the IBDMDB website (https://ibdmdb.org/) and deposited in SRA under accession number PRJNA398089. Taxonomic profiles, functional profiles, and sample metadata of the CRC datasets are available as Table S5 and Table S10. Taxonomic profiles and functional profiles of the HMP IBDMDB dataset are newly available at https://ibdmdb.org/. Profiles are also available through the curatedMetagenomicData R package (Pasolli et al., 2017). The full list of metagenomic datasets and samples used for the strain-level analysis of Ruminococcus bromii is reported in Table S1 from (Pasolli et al., 2019). Ruminococcus bromii reference genomes are deposited in GenBank under accession GCA_002834165, GCA_002834225, GCA_002834235, GCA_003466165, GCA_003466205, GCA_003466225, GCA_900101355 and GCA_900291485.

1. 1. Andrew Maltez Thomas
   2. Paolo Manghi
   3. Francesco Asnicar
   4. Edoardo Pasolli
   5. Federica Armanini
   6. Moreno Zolfo
   7. Francesco Beghini
   8. Serena Manara
   9. Nicolai Karcher
   10. Chiara Pozzi
   11. Sara Gandini
   12. Davide Serrano
   13. Sonia Tarallo
   14. Antonio Francavilla
   15. Gaetano Gallo
   16. Mario Trompetto
   17. Giulio Ferrero
   18. Sayaka Mizutani
   19. Hirotsugu Shiroma
   20. Satoshi Shiba
   21. Tatsuhiro Shibata
   22. Shinichi Yachida
   23. Takuji Yamada
   24. Jakob Wirbel
   25. Petra Schrotz-King
   26. Cornelia M. Ulrich
   27. Hermann Brenner
   28. Manimozhiyan Arumugam
   29. Peer Bork
   30. Georg Zeller
   31. Francesca Cordero
   32. Emmanuel Dias-Neto
   33. João Carlos Setubal
   34. Adrian Tett
   35. Barbara Pardini
   36. Maria Rescigno
   37. Levi Waldron
   38. Alessio Naccarati
   39. Nicola Segata

   (2018)

   BioProject

   Whole-metagenome shotgun sequencing of two Italian fecal CRC cohorts.
2. 1. Fritz A
   2. Hofmann P
   3. Majda S
   4. Dahms E
   5. Dröge J
   6. Fiedler J
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   (2019) CAMI Challenge

   ID CAMISIM. CAMISIM: simulating metagenomes and microbial communities.

   https://data.cami-challenge.org/participate
3. 1. Qiang Feng
   2. Suisha Liang
   3. Huijue Jia
   4. Andreas Stadlmayr
   5. Longqing Tang
   6. Zhou Lan
   7. Dongya Zhang
   8. Huihua Xia
   9. Xiaoying Xu
   10. Zhuye Jie
   11. Lili Su
   12. Xiaoping Li
   13. Xin Li
   14. Junhua Li
   15. Liang Xiao
   16. Ursula Huber-Schönauer
   17. David Niederseer
   18. Xun Xu
   19. Jumana Yousuf Al-Aama
   20. Huanming Yang
   21. Jian Wang
   22. Karsten Kristiansen
   23. Manimozhiyan Arumugam
   24. Herbert Tilg
   25. Christian Datz
   26. Jun Wang

   (2015) Bioproject

   ID PRJEB7774. Gut microbiome development along the colorectal adenoma–carcinoma sequence.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJEB7774
4. 1. Ankit Gupta
   2. Darshan B. Dhakan
   3. Abhijit Maji
   4. Rituja Saxena
   5. Vishnu Prasoodanan P.K.
   6. Shruti Mahajan
   7. Joby Pulikkan
   8. Jacob Kurian
   9. Andres M. Gomez
   10. Joy Scaria
   11. Katherine R. Amato
   12. Ashok K. Sharma
   13. Vineet K. Sharma

   (2019) BioProject

   ID PRJNA531273. Association of Flavonifractor plautii, a Flavonoid-Degrading Bacterium, with the Gut Microbiome of Colorectal Cancer Patients in India.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA531273
5. 1. Emily Vogtmann
   2. Xing Hua
   3. Georg Zeller
   4. Shinichi Sunagawa
   5. Anita Y. Voigt
   6. Rajna Hercog
   7. James J. Goedert
   8. Jianxin Shi
   9. Peer Bork
   10. Rashmi Sinha

   (2016) BioProject

   ID PRJEB12449. Colorectal cancer and the human gut microbiome.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJEB12449
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   2. Paul Theodor Pyl
   3. Ece Kartal
   4. Konrad Zych
   5. Alireza Kashani
   6. Alessio Milanese
   7. Jonas S. Fleck
   8. Anita Y. Voigt
   9. Albert Palleja
   10. Ruby Ponnudurai
   11. Shinichi Sunagawa
   12. Luis Pedro Coelho
   13. Petra Schrotz-King
   14. Emily Vogtmann
   15. Nina Habermann
   16. Emma Niméus
   17. Andrew M. Thomas
   18. Paolo Manghi
   19. Sara Gandini
   20. Davide Serrano
   21. Sayaka Mizutani
   22. Hirotsugu Shiroma
   23. Satoshi Shiba
   24. Tatsuhiro Shibata
   25. Shinichi Yachida
   26. Takuji Yamada
   27. Levi Waldron
   28. Alessio Naccarati
   29. Nicola Segata
   30. Rashmi Sinha
   31. Cornelia M. Ulrich
   32. Hermann Brenner
   33. Manimozhiyan Arumugam
   34. Peer Bork
   35. Georg Zeller

   (2019) BioProject

   ID PRJEB27928. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJEB27928
7. 1. Shinichi Yachida
   2. Sayaka Mizutani
   3. Hirotsugu Shiroma
   4. Satoshi Shiba
   5. Takeshi Nakajima
   6. Taku Sakamoto
   7. Hikaru Watanabe
   8. Keigo Masuda
   9. Yuichiro Nishimoto
   10. Masaru Kubo
   11. Fumie Hosoda
   12. Hirofumi Rokutan
   13. Minori Matsumoto
   14. Hiroyuki Takamaru
   15. Masayoshi Yamada
   16. Takahisa Matsuda
   17. Motoki Iwasaki
   18. Taiki Yamaji
   19. Tatsuo Yachida
   20. Tomoyoshi Soga
   21. Ken Kurokawa
   22. Atsushi Toyoda
   23. Yoshitoshi Ogura
   24. Tetsuya Hayashi
   25. Masanori Hatakeyama
   26. Hitoshi Nakagama
   27. Yutaka Saito
   28. Shinji Fukuda
   29. Tatsuhiro Shibata
   30. Takuji Yamada

   (2019) BioProject

   ID PRJDB4176. Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJDB4176
8. 1. Jun Yu
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   23. Huanming Yang
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   25. Christian Datz
   26. Herbert Tilg
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   30. Manimozhiyan Arumugam
   31. Joseph Jao-Yiu Sung Jun Wang

   (2015) BioProject

   ID PRJEB10878. Metagenomic analysis of fecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJEB10878
9. 1. Georg Zeller
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   6. Paul I Costea
   7. Aurélien Amiot
   8. Jürgen Böhm
   9. Francesco Brunetti
   10. Nina Habermann
   11. Rajna Hercog
   12. Moritz Koch
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   23. Cornelia M Ulrich
   24. Magnus von Knebel Doeberitz
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   26. Peer Bork

   (2014)

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   Colorectal cancer detection from fecal microbiota.
10. 1. Indrani Mukhopadhya
    2. Sarah Moraïs
    3. Jenny Laverde‐Gomez
    4. Paul O. Sheridan
    5. Alan W. Walker
    6. William Kelly
    7. Athol V. Klieve
    8. Diane Ouwerkerk
    9. Sylvia H. Duncan
    10. Petra Louis
    11. Nicole Koropatkin
    12. Darrell Cockburn
    13. Ryan Kibler
    14. Philip J. Cooper
    15. Carlos Sandoval
    16. Emmanuelle Crost
    17. Nathalie Juge
    18. Edward A. Bayer
    19. Harry J. Flint

    (2017) GenBank

    ID GCA_002834165. ASM283416v1.

    https://www.ncbi.nlm.nih.gov/assembly/GCF_002834165.1/
11. 1. Indrani Mukhopadhya
    2. Sarah Moraïs
    3. Jenny Laverde‐Gomez
    4. Paul O. Sheridan
    5. Alan W. Walker
    6. William Kelly
    7. Athol V. Klieve
    8. Diane Ouwerkerk
    9. Sylvia H. Duncan
    10. Petra Louis
    11. Nicole Koropatkin
    12. Darrell Cockburn
    13. Ryan Kibler
    14. Philip J. Cooper
    15. Carlos Sandoval
    16. Emmanuelle Crost
    17. Nathalie Juge
    18. Edward A. Bayer
    19. Harry J. Flint

    (2017)

    GenBank

    ASM283422v1.
12. 1. Indrani Mukhopadhya
    2. Sarah Moraïs
    3. Jenny Laverde‐Gomez
    4. Paul O. Sheridan
    5. Alan W. Walker
    6. William Kelly
    7. Athol V. Klieve
    8. Diane Ouwerkerk
    9. Sylvia H. Duncan
    10. Petra Louis
    11. Nicole Koropatkin
    12. Darrell Cockburn
    13. Ryan Kibler
    14. Philip J. Cooper
    15. Carlos Sandoval
    16. Emmanuelle Crost
    17. Nathalie Juge
    18. Edward A. Bayer
    19. Harry J. Flint

    (2017) GenBank

    ID GCA_002834235. ASM283423v1.

    https://www.ncbi.nlm.nih.gov/assembly/GCA_002834235/
13. 1. Yuanqiang Zou
    2. Wenbin Xue
    3. Guangwen Luo
    4. Ziqing Deng
    5. Panpan Qin
    6. Ruijin Guo
    7. Haipeng Sun
    8. Yan Xia
    9. Suisha Liang
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    16. Tongkun Guo
    17. Zhongkui Xia
    18. Chuan Liu
    19. Jinghong Yu
    20. Yuxiang Lin
    21. Shanmei Tang
    22. Guicheng Huo
    23. Xun Xu
    24. Yong Hou
    25. Xin Liu
    26. Jian Wang
    27. Huanming Yang
    28. Karsten Kristiansen
    29. Junhua Li
    30. Huijue Jia
    31. Liang Xiao

    (2018)

    GenBank

    ASM346616v1.
14. 1. Yuanqiang Zou
    2. Wenbin Xue
    3. Guangwen Luo
    4. Ziqing Deng
    5. Panpan Qin
    6. Ruijin Guo
    7. Haipeng Sun
    8. Yan Xia
    9. Suisha Liang
    10. Ying Dai
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    12. Rongrong Jiang
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    14. Qiang Feng
    15. Zhuye Jie
    16. Tongkun Guo
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    22. Guicheng Huo
    23. Xun Xu
    24. Yong Hou
    25. Xin Liu
    26. Jian Wang
    27. Huanming Yang
    28. Karsten Kristiansen
    29. Junhua Li
    30. Huijue Jia
    31. Liang Xiao

    (2018) GenBank

    ID GCA_003466205. ASM346620v1.

    https://www.ncbi.nlm.nih.gov/assembly/GCA_003466205/
15. 1. Yuanqiang Zou
    2. Wenbin Xue
    3. Guangwen Luo
    4. Ziqing Deng
    5. Panpan Qin
    6. Ruijin Guo
    7. Haipeng Sun
    8. Yan Xia
    9. Suisha Liang
    10. Ying Dai
    11. Daiwei Wan
    12. Rongrong Jiang
    13. Lili Su
    14. Qiang Feng
    15. Zhuye Jie
    16. Tongkun Guo
    17. Zhongkui Xia
    18. Chuan Liu
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    25. Xin Liu
    26. Jian Wang
    27. Huanming Yang
    28. Karsten Kristiansen
    29. Junhua Li
    30. Huijue Jia
    31. Liang Xiao

    (2018) GenBank

    ID GCA_003466225. ASM346622v1.

    https://www.ncbi.nlm.nih.gov/assembly/GCA_003466225/
16. 1. William Kelly

    (2017) GenBank

    ID GCA_900101355. IMG-taxon 2593339225 annotated assembly.

    https://www.ncbi.nlm.nih.gov/assembly/GCA_900101355/
17. 1. Indrani Mukhopadhya
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    5. Alan W. Walker
    6. William Kelly
    7. Athol V. Klieve
    8. Diane Ouwerkerk
    9. Sylvia H. Duncan
    10. Petra Louis
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    19. Harry J. Flint

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    ID GCA_900291485. R_bromii_L2_63_validated_080218.embl.gz.

    https://www.ncbi.nlm.nih.gov/assembly/GCA_900291485/

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