Taxonomic Binning Research Articles

Integrating taxonomic signals from MAGs and contigs improves read annotation and taxonomic profiling of metagenomes.

Metagenomic analysis typically includes read-based taxonomic profiling, assembly, and binning of metagenome-assembled genomes (MAGs). Here we integrate these steps in Read Annotation Tool (RAT), which uses robust taxonomic signals from MAGs and contigs to enhance read annotation. RAT reconstructs taxonomic profiles with high precision and sensitivity, outperforming other state-of-the-art tools. In high-diversity groundwater samples, RAT annotates a large fraction of the metagenomic reads, calling novel taxa at the appropriate, sometimes high taxonomic ranks. Thus, RAT integrative profiling provides an accurate and comprehensive view of the microbiome from shotgun metagenomics data. The package of Contig Annotation Tool (CAT), Bin Annotation Tool (BAT), and RAT is available at https://github.com/MGXlab/CAT_pack (from CATpack v6.0). The CAT pack now also supports Genome Taxonomy Database (GTDB) annotations.

Qmatey: an automated pipeline for fast exact matching-based alignment and strain-level taxonomic binning and profiling of metagenomes.

Metagenomics is a powerful tool for understanding organismal interactions; however, classification, profiling and detection of interactions at the strain level remain challenging. We present an automated pipeline, quantitative metagenomic alignment and taxonomic exact matching (Qmatey), that performs a fast exact matching-based alignment and integration of taxonomic binning and profiling. It interrogates large databases without using metagenome-assembled genomes, curated pan-genes or k-mer spectra that limit resolution. Qmatey minimizes misclassification and maintains strain level resolution by using only diagnostic reads as shown in the analysis of amplicon, quantitative reduced representation and shotgun sequencing datasets. Using Qmatey to analyze shotgun data from a synthetic community with 35% of the 26 strains at low abundance (0.01-0.06%), we revealed a remarkable 85-96% strain recall and 92-100% species recall while maintaining 100% precision. Benchmarking revealed that the highly ranked Kraken2 and KrakenUniq tools identified 2-4 more taxa (92-100% recall) than Qmatey but produced 315-1752 false positive taxa and high penalty on precision (1-8%). The speed, accuracy and precision of the Qmatey pipeline positions it as a valuable tool for broad-spectrum profiling and for uncovering biologically relevant interactions.

Baseline metagenome-assembled genome (MAG) data of Sikkim hot springs from Indian Himalayan geothermal belt (IHGB) showcasing its potential CAZymes, and sulfur-nitrogen metabolic activity.

Here we present the construction and characterization of metagenome assembled genomes (MAGs) from two hot springs residing in the vicinity of Indian Himalayan Geothermal Belt (IHGB). A total of 78 and 7 taxonomic bins were obtained for Old Yume Samdong (OYS) and New Yume Samdong (NYS) hot springs respectively. After passing all the criteria only 21 and 4 MAGs were further studied based on the successful prediction of their 16S rRNA. Various databases were used such as GTDB, Kaiju, EzTaxon, BLAST XY Plot and NCBI BLAST to get the taxonomic classification of various 16S rRNA predicted MAGs. The bacterial genomes found were from both thermophilic and mesophilic bacteria among which Proteobacteria, Chloroflexi, Bacteroidetes and Firmicutes were the abundant phyla. However, in case of OYS, two genomes belonged to archaeal Methanobacterium and Methanocaldococcus. Functional characterization revealed the richness of CAZymes such as Glycosyl Transferase (GT) (56.7%), Glycoside Hydrolase (GH) (37.4%), Carbohydrate Esterase family (CE) (8.2%), and Polysaccharide Lyase (PL) (1.9%). There were negligible antibiotic resistance genes in the MAGs however, a significant heavy metal tolerance gene was found in the MAGs. Thus, it may be assumed that there is no coexistence of antibiotic and heavy metal resistance genes in these hot spring microbiomes. Since the selected hot springs possess good sulfur content thus, we also checked the presence of genes for sulfur and nitrogen metabolism. It was found that MAGs from both the hot springs possess significant number of genes related to sulfur and nitrogen metabolism.

Seed-endophytes empower Anthemis cotula to expand in invaded range

The seeds serve as a biological vehicle for alien species to colonize new habitats and seed endophytes may enhance their chances of survival and spread in the introduced range. Thus, the endophytic diversity associated with the seeds of alien species and their plant growth-promoting activities need to be investigated in detail. Here we explored the composition and diversity of culturable endophytic microbes inhabiting achenes of Anthemis cotula L. an invasive alien species in Kashmir Himalaya. A total of 812 culturable endophytic isolates were recovered from seeds of A. cotula, comprising 716 bacterial isolates, and 96 fungal isolates. Taxonomic binning resolved the isolates into 39 bacterial OTUs and 22 fungal OTUs belonging to 4 bacterial phyla and 2 fungal phyla. The relative abundance of these culturable seed endophytic taxa differed significantly across the sites (p < 0.001). Moreover, similar endophytic communities with a shared core endophytic assemblage were observed across different sites. This core microbiome was represented by 17 bacterial and 8 fungal OTUs. We also screened the isolated endophytes for various plant growth-promoting activities, such as IAA production, phosphate solubilization, and ammonia production and more than 75% of the isolates showed one or the other plant growth-promoting activity, Interestingly, some isolates showed strong antagonistic activity against Botrytis cinerea which is a common pathogen of A. cotula in its native range. Given the functional attributes of achene borne endophytes, their role in the establishment and spread of A. cotula in Kashmir Himalaya is quite likely and plausible.

Metagenomes from Coastal Sediments of Kuwait: Insights into the Microbiome, Metabolic Functions and Resistome.

Coastal sediments in the proximity of wastewater and emergency outfalls are often sinks of pharmaceutical compounds and other organic and inorganic contaminants that are likely to affect the microbial community. The metabolites of these contaminants affect microbial diversity and their metabolic processes, resulting in undesirable effects on ecosystem functioning, thus necessitating the need to understand their composition and functions. In the present investigation, we studied the metagenomes of 12 coastal surface sediments through whole genome shot-gun sequencing. Taxonomic binning of the genes predicted about 86% as bacteria, 1% as archaea, >0.001% as viruses and Eukaryota, and 12% as other communities. The dominant bacterial, archaeal, and fungal genera were Woeseia, Nitrosopumilus, and Rhizophagus, respectively. The most prevalent viral families were Myoviridae and Siphoviridae, and the T4 virus was the most dominant bacteriophage. The unigenes further aligned to 26 clusters of orthologous genes (COGs) and five carbohydrate-active enzymes (CAZy) classes. Glycoside hydrolases (GH) and glycoside transferase (GT) were the highest-recorded CAzymes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) level 3 functions were subjugated by purine metabolism > ABC transporters > oxidative phosphorylation > two-component system > pyrimidine metabolism > pyruvate metabolism > quorum sensing > carbon fixation pathways > ribosomes > and glyoxalate and dicarboxylate metabolism. Sequences allying with plasmids, integrons, insertion sequences and antibiotic-resistance genes were also observed. Both the taxonomies and functional abundances exhibited variation in relative abundances, with limited spatial variability (ANOVA p > 0.05; ANOSIM-0.05, p > 0.05). This study underlines the dominant microbial communities and functional genes in the marine sediments of Kuwait as a baseline for future biomonitoring programs.

DIAMOND + MEGAN Microbiome Analysis.

Metagenomics is the study of microbiomes using DNA sequencing technologies. Basic computational tasks are to determine the taxonomic composition (who is out there?), the functional composition (what can they do?), and also to correlate changes of composition to changes in external parameters (how do they compare?). One approach to address these issues is to first align all sequences against a protein reference database such as NCBI-nr and to then perform taxonomic and functional binning of all sequences based on their alignments. The resulting classifications can then be interactively analyzed and compared. Here we illustrate how to pursue this approach using the DIAMOND+MEGAN pipeline, on two different publicly available datasets, one containing short-read samples and other containing long-read samples.

High-Resolution Metagenomics of Human Gut Microbiota Generated by Nanopore and Illumina Hybrid Metagenome Assembly.

Metagenome assembly is a core yet methodologically challenging step for taxonomic classification and functional annotation of a microbiome. This study aims to generate the high-resolution human gut metagenome using both Illumina and Nanopore platforms. Assembly was achieved using four assemblers, including Flye (Nanopore), metaSPAdes (Illumina), hybridSPAdes (Illumina and Nanopore), and OPERA-MS (Illumina and Nanopore). Hybrid metagenome assembly was shown to generate contigs with almost same sizes comparable to those produced using Illumina reads alone, but was more contiguous, informative, and longer compared with those assembled with Illumina reads only. In addition, hybrid metagenome assembly enables us to obtain complete plasmid sequences and much more AMR gene-encoding contigs than the Illumina method. Most importantly, using our workflow, 58 novel high-quality metagenome bins were obtained from four assembly algorithms, particularly hybrid assembly (47/58), although metaSPAdes could provide 11 high-quality bins independently. Among them, 29 bins were currently uncultured bacterial metagenome-assembled genomes. These findings were highly consistent and supported by mock community data tested. In the analysis of biosynthetic gene clusters (BGCs), the number of BGCs in the contigs from hybridSPAdes (241) is higher than that of contigs from metaSPAdes (233). In conclusion, hybrid metagenome assembly could significantly enhance the efficiency of contig assembly, taxonomic binning, and genome construction compared with procedures using Illumina short-read data alone, indicating that nanopore long reads are highly useful in metagenomic applications. This technique could be used to create high-resolution references for future human metagenome studies.

A theoretical analysis of taxonomic binning accuracy.

Many metagenomic and environmental DNA studies require the taxonomic assignment of individual reads or sequences by aligning reads to a reference database, known as taxonomic binning. When a read aligns to more than one reference sequence, it is often classified based on sequence similarity. This step can assign reads to incorrect taxa, at a rate which depends both on the assignment algorithm and on underlying population genetic and database parameters. In particular, as we move towards using environmental DNA to study eukaryotic taxa subject to regular recombination, we must take into account issues concerning gene tree discordance. Though accuracy is often compared across algorithms using a fixed data set, the relative impact of these population genetic and database parameters on accuracy has not yet been quantified. Here, we develop both a theoretical and simulation framework in the simplified case of two reference species, and compute binning accuracy over a wide range of parameters, including sequence length, species-query divergence time, divergence times of the reference species, reference database completeness, sample age and effective population size. We consider two assignment methods and contextualize our results using parameters from a recent ancient environmental DNA study, comparing them to the commonly used discriminative k-mer-based method Clark (Current Biology, 31, 2021, 2728; BMC Genomics, 16, 2015, 1). Our results quantify the degradation in assignment accuracy as the samples diverge from their closest reference sequence, and with incompleteness of reference sequences. We also provide a framework in which others can compute expected accuracy for their particular method or parameter set. Code is available at https://github.com/bdesanctis/binning-accuracy.

SprayNPray: user-friendly taxonomic profiling of genome and metagenome contigs

BackgroundShotgun sequencing of cultured microbial isolates/individual eukaryotes (whole-genome sequencing) and microbial communities (metagenomics) has become commonplace in biology. Very often, sequenced samples encompass organisms spanning multiple domains of life, necessitating increasingly elaborate software for accurate taxonomic classification of assembled sequences.ResultsWhile many software tools for taxonomic classification exist, SprayNPray offers a quick and user-friendly, semi-automated approach, allowing users to separate contigs by taxonomy (and other metrics) of interest. Easy installation, usage, and intuitive output, which is amenable to visual inspection and/or further computational parsing, will reduce barriers for biologists beginning to analyze genomes and metagenomes. This approach can be used for broad-level overviews, preliminary analyses, or as a supplement to other taxonomic classification or binning software. SprayNPray profiles contigs using multiple metrics, including closest homologs from a user-specified reference database, gene density, read coverage, GC content, tetranucleotide frequency, and codon-usage bias.ConclusionsThe output from this software is designed to allow users to spot-check metagenome-assembled genomes, identify, and remove contigs from putative contaminants in isolate assemblies, identify bacteria in eukaryotic assemblies (and vice-versa), and identify possible horizontal gene transfer events.

BugSplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies

A large gap remains between sequencing a microbial community and characterizing all of the organisms inside of it. Here we develop a novel method to taxonomically bin metagenomic assemblies through alignment of contigs against a reference database. We show that this workflow, BugSplit, bins metagenome-assembled contigs to species with a 33% absolute improvement in F1-score when compared to alternative tools. We perform nanopore mNGS on patients with COVID-19, and using a reference database predating COVID-19, demonstrate that BugSplit’s taxonomic binning enables sensitive and specific detection of a novel coronavirus not possible with other approaches. When applied to nanopore mNGS data from cases of Klebsiella pneumoniae and Neisseria gonorrhoeae infection, BugSplit’s taxonomic binning accurately separates pathogen sequences from those of the host and microbiota, and unlocks the possibility of sequence typing, in silico serotyping, and antimicrobial resistance prediction of each organism within a sample. BugSplit is available at https://bugseq.com/academic.

MetaConClust - Unsupervised Binning of Metagenomics Data using Consensus Clustering.

Background: Binning of metagenomic reads is an active area of research, and many unsupervised machine learning-based techniques have been used for taxonomic independent binning of metagenomic reads. Objective: It is important to find the optimum number of the cluster as well as develop an efficient pipeline for deciphering the complexity of the microbial genome. Methods: Applying unsupervised clustering techniques for binning requires finding the optimal number of clusters beforehand and is observed to be a difficult task. This paper describes a novel method, MetaConClust, using coverage information for grouping of contigs and automatically finding the optimal number of clusters for binning of metagenomics data using a consensus-based clustering approach. The coverage of contigs in a metagenomics sample has been observed to be directly proportional to the abundance of species in the sample and is used for grouping of data in the first phase by MetaConClust. The Partitioning Around Medoid (PAM) method is used for clustering in the second phase for generating bins with the initial number of clusters determined automatically through a consensus-based method. Results: Finally, the quality of the obtained bins is tested using silhouette index, rand Index, recall, precision, and accuracy. Performance of MetaConClust is compared with recent methods and tools using benchmarked low complexity simulated and real metagenomic datasets and is found better for unsupervised and comparable for hybrid methods. Conclusion: This is suggestive of the proposition that the consensus-based clustering approach is a promising method for automatically finding the number of bins for metagenomics data.

Diel-Regulated Transcriptional Cascades of Microbial Eukaryotes in the North Pacific Subtropical Gyre.

Open-ocean surface waters host a diverse community of single-celled eukaryotic plankton (protists) consisting of phototrophs, heterotrophs, and mixotrophs. The productivity and biomass of these organisms oscillate over diel cycles, and yet the underlying transcriptional processes are known for few members of the community. Here, we examined a 4-day diel time series of transcriptional abundance profiles for the protist community (0.2–100 μm in cell size) in the North Pacific Subtropical Gyre near Station ALOHA. De novo assembly of poly-A+ selected metatranscriptomes yielded over 30 million contigs with taxonomic and functional annotations assigned to 54 and 25% of translated contigs, respectively. The completeness of the resulting environmental eukaryotic taxonomic bins was assessed, and 48 genera were further evaluated for diel patterns in transcript abundances. These environmental transcriptome bins maintained reproducible temporal partitioning of total gene family abundances, with haptophyte and ochrophyte genera generally showing the greatest diel partitioning of their transcriptomes. The haptophyte Phaeocystis demonstrated the highest proportion of transcript diel periodicity, while most other protists had intermediate levels of periodicity regardless of their trophic status. Dinoflagellates, except for the parasitoid genus Amoebophrya, exhibit the fewest diel oscillations of transcript abundances. Diel-regulated gene families were enriched in key metabolic pathways; photosynthesis, carbon fixation, and fatty acid biosynthesis gene families had peak times concentrated around dawn, while gene families involved in protein turnover (proteasome and protein processing) are most active during the high intensity daylight hours. TCA cycle, oxidative phosphorylation and fatty acid degradation predominantly peaked near dusk. We identified temporal pathway enrichments unique to certain taxa, including assimilatory sulfate reduction at dawn in dictyophytes and signaling pathways at early evening in haptophytes, pointing to possible taxon-specific channels of carbon and nutrients through the microbial community. These results illustrate the synchrony of transcriptional regulation to the diel cycle and how the protist community of the North Pacific Subtropical Gyre structures their transcriptomes to guide the daily flux of matter and energy through the gyre ecosystem.

Effect of Red Orange and Lemon Extract-Enriched Diet in Suckling Lambs’ Fecal Microbiota

Red orange and lemon extract (RLE) is an anthocyanins-rich dietary supplement that may influence gastrointestinal bacterial community in ruminants. The aim of the present study was to investigate the RLE effects on gut microbiota composition in lambs. Twenty-eight lambs were randomly divided into a control group (CON; n = 14) and an anthocyanin group (ANT; n = 14) and fed the same diet; additionally, only the ANT received 90 mg/kg live weight of RLE at day. After lamb slaughter (40 ± 1 days), fecal samples were collected from the rectum and stored at −20 °C until analysis. Analysis of fecal microbiome was carried out by metabarcoding analysis of 16S rRNA. After reads denoising, sequences were aligned against SILVA rRNA sequence database using MALT, and taxonomic binning was performed with MEGAN. A significant increase in Firmicutes and Bacteroidetes and a decrease in Proteobacteria and Actinobacteria was observed in ANT compared to CON. Moreover, an interesting increase of Lactobacillus and Bifidobacterium genera and a decrease in Escherichia coli and Salmonella species were detected in ANT compared to CON. Results recommend that anthocyanin supplementation in lamb diet is able to modulate positively gut microbiota and may inhibit the growth of some potential pathogenic microorganisms.

DIAMOND+MEGAN: Fast and Easy Taxonomic and Functional Analysis of Short and Long Microbiome Sequences

One main approach to computational analysis of microbiome sequences is to first align against a reference database of annotated protein sequences (NCBI-nr) and then perform taxonomic and functional binning of the sequences based on the resulting alignments. For both short and long reads (or assembled contigs), alignment is performed using DIAMOND, whereas taxonomic and functional binning, followed by inter- active exploration and analysis, is performed using MEGAN. We provide two step-by-step descriptions of this approach: © 2021 The Authors. Basic Protocol 1: Taxonomic and functional analysis of short read microbiome sequences Support Protocol 1: Preprocessing Basic Protocol 2: taxonomic and functional analysis of assembled long read microbiome sequences Support Protocol 2: Taxonomic binning and CheckM.

Taxonomic Binning Approaches and Functional Characteristics of the Microbial Community during the Anaerobic Digestion of Hydrolyzed Corncob

Maize forms the basis of Mexican food. As a result, approximately six million tons of corncob are produced each year, which represents an environmental issue, as well as a potential feedstock for biogas production. This research aimed to analyze the taxonomic and functional shift in the microbiome of the fermenters using a whole metagenome shotgun approach. Two strategies were used to understand the microbial community at the beginning and the end of anaerobic digestion: (i) phylogenetic analysis to infer the presence and coverage of clade-specific markers to assign taxonomy and (ii) the recovery of the individual genomes from the samples using the binning of the assembled scaffolds. The results showed that anaerobic digestion brought some noticeable changes and the main microbial community was composed of Corynebacterium variable, Desulfovibrio desulfuricans, Vibrio furnissii, Shewanella spp., Actinoplanes spp., Pseudoxanthomonas spp., Saccharomonospora azurea, Agromyces spp., Serinicoccus spp., Cellulomonas spp., Pseudonocardia spp., Rhodococcus rhodochrous, Sphingobacterium spp. Methanosarcina mazei, Methanoculleus hydrogenitrophicus, Methanosphaerula spp., Methanoregula spp., Methanosaeta spp. and Methanospirillum spp. This study provides evidence of the drastic change in the microbial community structure in a short time and the functional strategy that the most representative microorganisms of the consortia used to carry out the process.

Alpha-, beta-, and gamma-diversity of bacteria varies across habitats.

Bacteria are essential parts of ecosystems and are the most diverse organisms on the planet. Yet, we still do not know which habitats support the highest diversity of bacteria across multiple scales. We analyzed alpha-, beta-, and gamma-diversity of bacterial assemblages using 11,680 samples compiled by the Earth Microbiome Project. We found that soils contained the highest bacterial richness within a single sample (alpha-diversity), but sediment assemblages displayed the highest gamma-diversity. Sediment, biofilms/mats, and inland water exhibited the most variation in community composition among geographic locations (beta-diversity). Within soils, agricultural lands, hot deserts, grasslands, and shrublands contained the highest richness, while forests, cold deserts, and tundra biomes consistently harbored fewer bacterial species. Surprisingly, agricultural soils encompassed similar levels of beta-diversity as other soil biomes. These patterns were robust to the alpha- and beta- diversity metrics used and the taxonomic binning approach. Overall, the results support the idea that spatial environmental heterogeneity is an important driver of bacterial diversity.

Phylogenetic characterization of bacterial endophytes from four Pinus species and their nematicidal activity against the pine wood nematode

Recently, bacterial endophytes (BEs) have gained importance in the agricultural sector for their use as biocontrol agents to manage plant pathogens. Outbreak of the pine wilt disease (PWD) in Korea has led researchers to test the feasibility of BEs in controlling the pine wood nematode (PWN) Bursaphelenchus xylophilus. In this study, we have reported the diversity and biocontrol activity of BEs against the PWN. By employing a culture-dependent approach, 1,622 BEs were isolated from the needle, stem, and root tissues of P. densiflora, P. rigida, P. thunbergii, and P. koraiensis across 18 sampling sites in Korea. We classified 389 members based on 16S rDNA analysis and taxonomic binning, of which, 215 operational taxonomic units (OTUs) were determined. Using Shannon’s indices, diversity across the Pinus species and tissues was estimated to reveal the composition of BEs and their tissue-specific preferences. When their ethyl acetate crude extracts were analysed for biocontrol activity, 44 candidates with nematicidal activity were obtained. Among these, Stenotrophomonas and Bacillus sp. exhibited significant inhibitory activity against PWN during their developmental stages. Altogether, our study furnishes a basic comprehension of bacterial communities found in the Pinus species and highlights the potential of BEs as biocontrol agents to combat PWD.

A taxonomy of impacts on clinical and translational research from community stakeholder engagement.

BackgroundCommunity engagement is increasingly recognized as a valuable tool in clinical and translational research; however, the impact of engagement is not fully understood. No standard nomenclature yet exists to clearly define how research changes when community stakeholders are engaged across the research spectrum. This severely limits our ability to assess the value of community engagement in research. To address this gap, we developed a taxonomy for characterizing and classifying changes in research due to community engagement.MethodsUsing an iterative process, we (a) identified areas of potential impact associated with community engagement from author experience, (b) categorized these in taxonomic bins based on research stages, (c) conducted semi‐structured interviews with researchers and community stakeholders, (d) validated the codebook in a sample dataset and (e) refined the taxonomy based on the validation. Community stakeholders were involved in every step of the process including as members of the primary study team.ResultsThe final taxonomy catalogues changes into eleven domains corresponding to research phases. Each domain includes 2‐4 dimensions depicting concepts within the domain's scope and, within each dimension, 2‐10 elements labelling activities through which community engagement could change research.ConclusionsCommunity engagement has great potential to enhance clinical and translational research. This taxonomy provides a common vocabulary and framework for understanding the impact of community engagement and suggests metrics for assessing the value of community engagement in research.

CAMISIM: simulating metagenomes and microbial communities

BackgroundShotgun metagenome data sets of microbial communities are highly diverse, not only due to the natural variation of the underlying biological systems, but also due to differences in laboratory protocols, replicate numbers, and sequencing technologies. Accordingly, to effectively assess the performance of metagenomic analysis software, a wide range of benchmark data sets are required.ResultsWe describe the CAMISIM microbial community and metagenome simulator. The software can model different microbial abundance profiles, multi-sample time series, and differential abundance studies, includes real and simulated strain-level diversity, and generates second- and third-generation sequencing data from taxonomic profiles or de novo. Gold standards are created for sequence assembly, genome binning, taxonomic binning, and taxonomic profiling. CAMSIM generated the benchmark data sets of the first CAMI challenge. For two simulated multi-sample data sets of the human and mouse gut microbiomes, we observed high functional congruence to the real data. As further applications, we investigated the effect of varying evolutionary genome divergence, sequencing depth, and read error profiles on two popular metagenome assemblers, MEGAHIT, and metaSPAdes, on several thousand small data sets generated with CAMISIM.ConclusionsCAMISIM can simulate a wide variety of microbial communities and metagenome data sets together with standards of truth for method evaluation. All data sets and the software are freely available at https://github.com/CAMI-challenge/CAMISIM

The Genomic Potentials of NOB and Comammox Nitrospira in River Sediment Are Impacted by Native Freshwater Mussels.

Freshwater mussel assemblages of the Upper Mississippi River (UMR) sequester tons of ammonia- and urea-based biodeposits each day and aerate sediment through burrowing activities, thus creating a unique niche for nitrogen (N) cycling microorganisms. This study explored how mussels impact the abundance of N-cycling species with an emphasis on Candidatus Nitrospira inopinata, the first microorganism known to completely oxidize ammonia (comammox) to nitrate. This study used metagenomic shotgun sequencing of genomic DNA to compare nitrogen cycling species in sediment under a well-established mussel assemblage and in nearby sediment without mussels. Metagenomic reads were aligned to the prokaryotic RefSeq non-redundant protein database using BLASTx, taxonomic binning was performed using the weighted lowest common ancestor algorithm, and protein-coding genes were categorized by metabolic function using the SEED subsystem. Linear discriminant analysis (LDA) effect sizes were used to determine which metagenomes and metabolic features explained the most differences between the mussel habitat sediment and sediment without mussels. Of the N-cycling species deemed differentially abundant, Nitrospira moscoviensis and “Candidatus Nitrospira inopinata” were responsible for creating a distinctive N-cycling microbiome in the mussel habitat sediment. Further investigation revealed that comammox Nitrospira had a large metabolic potential to degrade mussel biodeposits, as evidenced the top ten percent of protein-coding genes including the cytochrome c-type biogenesis protein required for hydroxylamine oxidation, ammonia monooxygenase, and urea decomposition SEED subsystems. Genetic marker analysis of these two Nitrospira taxons suggested that N. moscoviensis was most impacted by diverse carbon metabolic processes while “Candidatus Nitrospira inopinata” was most distinguished by multidrug efflux proteins (AcrB), NiFe hydrogenase (HypF) used in hydrogen oxidation and sulfur reduction coupled reactions, and a heme chaperone (CcmE). Furthermore, our research suggests that comammox and NOB Nitrospira likely coexisted by utilizing mixotrophic metabolisms. For example, “Candidatus Nitrospira inopinata” had the largest potentials for ammonia oxidation, nitrite reduction with NirK, and hydrogen oxidation, while NOB Nitrospira had the greatest potential for nitrite oxidation, and nitrate reduction possibly coupled with formate oxidation. Overall, our results suggest that this mussel habitat sediment harbors a niche for NOB and comammox Nitrospira, and ultimately impacts N-cycling in backwaters of the UMR.