Regulatory sequence-based discovery of anti-defense genes in archaeal viruses.

In silico identification of viral anti-CRISPR proteins (Acrs) has relied largely on the guilt-by-association method using known Acrs or anti-CRISPR associated proteins (Acas) as the bait. However, the low number and limited spread of the characterized archaeal Acrs and Aca hinders our ability to identify Acrs using guilt-by-association. Here, based on the observation that the few characterized archaeal Acrs and Aca are transcribed immediately post viral infection, we hypothesize that these genes, and many other unidentified anti-defense genes (ADG), are under the control of conserved regulatory sequences including a strong promoter, which can be used to predict anti-defense genes in archaeal viruses. Using this consensus sequence based method, we identify 354 potential ADGs in 57 archaeal viruses and 6 metagenome-assembled genomes. Experimental validation identified a CRISPR subtype I-A inhibitor and the first virally encoded inhibitor of an archaeal toxin-antitoxin based immune system. We also identify regulatory proteins potentially akin to Acas that can facilitate further identification of ADGs combined with the guilt-by-association approach. These results demonstrate the potential of regulatory sequence analysis for extensive identification of ADGs in viruses of archaea and bacteria.

Long-read powered viral metagenomics in the oligotrophic Sargasso Sea.

Dominant microorganisms of the Sargasso Sea are key drivers of the global carbon cycle. However, associated viruses that shape microbial community structure and function are not well characterised. Here, we combined short and long read sequencing to survey Sargasso Sea phage communities in virus- and cellular fractions at viral maximum (80 m) and mesopelagic (200 m) depths. We identified 2,301 Sargasso Sea phage populations from 186 genera. Over half of the phage populations identified here lacked representation in global ocean viral metagenomes, whilst 177 of the 186 identified genera lacked representation in genomic databases of phage isolates. Viral fraction and cell-associated viral communities were decoupled, indicating viral turnover occurred across periods longer than the sampling period of three days. Inclusion of long-read data was critical for capturing the breadth of viral diversity. Phage isolates that infect the dominant bacterial taxa Prochlorococcus and Pelagibacter, usually regarded as cosmopolitan and abundant, were poorly represented.

Readsynth: short-read simulation for consideration of composition-biases in reduced metagenome sequencing approaches.

BACKGROUND: The application of reduced metagenomic sequencing approaches holds promise as a middle ground between targeted amplicon sequencing and whole metagenome sequencing approaches but has not been widely adopted as a technique. A major barrier to adoption is the lack of read simulation software built to handle characteristic features of these novel approaches. Reduced metagenomic sequencing (RMS) produces unique patterns of fragmentation per genome that are sensitive to restriction enzyme choice, and the non-uniform size selection of these fragments may introduce novel challenges to taxonomic assignment as well as relative abundance estimates. RESULTS: Through the development and application of simulation software, readsynth, we compare simulated metagenomic sequencing libraries with existing RMS data to assess the influence of multiple library preparation and sequencing steps on downstream analytical results. Based on read depth per position, readsynth achieved 0.79 Pearson's correlation and 0.94 Spearman's correlation to these benchmarks. Application of a novel estimation approach, fixed length taxonomic ratios, improved quantification accuracy of simulated human gut microbial communities when compared to estimates of mean or median coverage. CONCLUSIONS: We investigate the possible strengths and weaknesses of applying the RMS technique to profiling microbial communities via simulations with readsynth. The choice of restriction enzymes and size selection steps in library prep are non-trivial decisions that bias downstream profiling and quantification. The simulations investigated in this study illustrate the possible limits of preparing metagenomic libraries with a reduced representation sequencing approach, but also allow for the development of strategies for producing and handling the sequence data produced by this promising application.

Genetic and functional diversity of ß-N-acetylgalactosamine-targeting glycosidases expanded by deep-sea metagenome analysis.

ß-N-Acetylgalactosamine-containing glycans play essential roles in several biological processes, including cell adhesion, signal transduction, and immune responses. ß-N-Acetylgalactosaminidases hydrolyze ß-N-acetylgalactosamine linkages of various glycoconjugates. However, their biological significance remains ambiguous, primarily because only one type of enzyme, exo-ß-N-acetylgalactosaminidases that specifically act on ß-N-acetylgalactosamine residues, has been documented to date. In this study, we identify four groups distributed among all three domains of life and characterize eight ß-N-acetylgalactosaminidases and ß-N-acetylhexosaminidase through sequence-based screening of deep-sea metagenomes and subsequent searching of public protein databases. Despite low sequence similarity, the crystal structures of these enzymes demonstrate that all enzymes share a prototype structure and have diversified their substrate specificities (oligosaccharide-releasing, oligosaccharide/monosaccharide-releasing, and monosaccharide-releasing) through the accumulation of mutations and insertional amino acid sequences. The diverse ß-N-acetylgalactosaminidases reported in this study could facilitate the comprehension of their structures and functions and present evolutionary pathways for expanding their substrate specificity.

Interpretable metric learning in comparative metagenomics: The adaptive Haar-like distance.

Random forests have emerged as a promising tool in comparative metagenomics because they can predict environmental characteristics based on microbial composition in datasets where ß-diversity metrics fall short of revealing meaningful relationships between samples. Nevertheless, despite this efficacy, they lack biological insight in tandem with their predictions, potentially hindering scientific advancement. To overcome this limitation, we leverage a geometric characterization of random forests to introduce a data-driven phylogenetic ß-diversity metric, the adaptive Haar-like distance. This new metric assigns a weight to each internal node (i.e., split or bifurcation) of a reference phylogeny, indicating the relative importance of that node in discerning environmental samples based on their microbial composition. Alongside this, a weighted nearest-neighbors classifier, constructed using the adaptive metric, can be used as a proxy for the random forest while maintaining accuracy on par with that of the original forest and another state-of-the-art classifier, CoDaCoRe. As shown in datasets from diverse microbial environments, however, the new metric and classifier significantly enhance the biological interpretability and visualization of high-dimensional metagenomic samples.

MicroPredict: predicting species-level taxonomic abundance of whole-shotgun metagenomic data using only 16S amplicon sequencing data.

BACKGROUND: The importance of the human microbiome in the analysis of various diseases is emerging. The two main methods used to profile the human microbiome are 16S rRNA gene sequencing (16S sequencing) and whole-genome shotgun sequencing (WGS). Owing to the full coverage of the genome in sequencing, WGS has multiple advantages over 16S sequencing, including higher taxonomic profiling resolution at the species-level and functional profiling analysis. However, 16S sequencing remains widely used because of its relatively low cost. Although WGS is the standard method for obtaining accurate species-level data, we found that 16S sequencing data contained rich information to predict high-resolution species-level abundances with reasonable accuracy. OBJECTIVE: In this study, we proposed MicroPredict, a method for accurately predicting WGS-comparable species-level abundance data using 16S taxonomic profile data. METHODS: We employed a mixed model using two key strategies: (1) modeling both sample- and species-specific information for predicting WGS abundances, and (2) accounting for the possible correlations among different species. RESULTS: We found that MicroPredict outperformed the other machine learning methods. CONCLUSION: We expect that our approach will help researchers accurately approximate the species-level abundances of microbiome profiles in datasets for which only cost-effective 16S sequencing has been applied.

How to Obtain and Compare Metagenome-Assembled Genomes.

Metagenome-assembled genomes, or MAGs, are genomes retrieved from metagenome datasets. In the vast majority of cases, MAGs are genomes from prokaryotic species that have not been isolated or cultivated in the lab. They, therefore, provide us with information on these species that are impossible to obtain otherwise, at least until new cultivation methods are devised. Thanks to improvements and cost reductions of DNA sequencing technologies and growing interest in microbial ecology, the rise in number of MAGs in genome repositories has been exponential. This chapter covers the basics of MAG retrieval and processing and provides a practical step-by-step guide using a real dataset and state-of-the-art tools for MAG analysis and comparison.

A Practical Approach to Using the Genomic Standards Consortium MIxS Reporting Standard for Comparative Genomics and Metagenomics.

Comparative analysis of (meta)genomes necessitates aggregation, integration, and synthesis of well-annotated data using standards. The Genomic Standards Consortium (GSC) collaborates with the research community to develop and maintain the Minimum Information about any (x) Sequence (MIxS) reporting standard for genomic data. To facilitate the use of the GSC's MIxS reporting standard, we provide a description of the structure and terminology, how to navigate ontologies for required terms in MIxS, and demonstrate practical usage through a soil metagenome example.

Nonribosomal Peptide Synthetase Specific Genome Amplification Using Rolling Circle Amplification for Targeted Gene Sequencing.

Next-generation sequencing has transformed the acquisition of vast amounts of genomic information, including the rapid identification of target gene sequences in metagenomic databases. However, dominant species can sometimes hinder the detection of rare bacterial species. Therefore, a highly sensitive amplification technique that can selectively amplify bacterial genomes containing target genes of interest was developed in this study. The rolling circle amplification (RCA) method can initiate amplification from a single locus using a specific single primer to amplify a specific whole genome. A mixed cell suspension was prepared using Pseudomonas fluorescens ATCC17400 (targeting nonribosomal peptide synthetase [NRPS]) and Escherichia coli (non-target), and a specific primer designed for the NRPS was used for the RCA reaction. The resulting RCA product (RCP) amplified only the Pseudomonas genome. The NRPS was successfully amplified using RCP as a template from even five cells, indicating that the single-priming RCA technique can specifically enrich the target genome using gene-specific primers. Ultimately, this specific genome RCA technique was applied to metagenomes extracted from sponge-associated bacteria, and NRPS sequences were successfully obtained from an unknown sponge-associated bacterium. Therefore, this method could be effective for accessing species-specific sequences of NRPS in unknown bacteria, including viable but non-culturable bacteria.

Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity.

Although long-read sequencing enables the generation of complete genomes for unculturable microbes, its high cost limits the widespread adoption of long-read sequencing in large-scale metagenomic studies. An alternative method is to assemble short-reads with long-range connectivity, which can be a cost-effective way to generate high-quality microbial genomes. Here, we develop Pangaea, a bioinformatic approach designed to enhance metagenome assembly using short-reads with long-range connectivity. Pangaea leverages connectivity derived from physical barcodes of linked-reads or virtual barcodes by aligning short-reads to long-reads. Pangaea utilizes a deep learning-based read binning algorithm to assemble co-barcoded reads exhibiting similar sequence contexts and abundances, thereby improving the assembly of high- and medium-abundance microbial genomes. Pangaea also leverages a multi-thresholding algorithm strategy to refine assembly for low-abundance microbes. We benchmark Pangaea on linked-reads and a combination of short- and long-reads from simulation data, mock communities and human gut metagenomes. Pangaea achieves significantly higher contig continuity as well as more near-complete metagenome-assembled genomes (NCMAGs) than the existing assemblers. Pangaea also generates three complete and circular NCMAGs on the human gut microbiomes.

Hidden diversity and potential ecological function of phosphorus acquisition genes in widespread terrestrial bacteriophages.

Phosphorus (P) limitation of ecosystem processes is widespread in terrestrial habitats. While a few auxiliary metabolic genes (AMGs) in bacteriophages from aquatic habitats are reported to have the potential to enhance P-acquisition ability of their hosts, little is known about the diversity and potential ecological function of P-acquisition genes encoded by terrestrial bacteriophages. Here, we analyze 333 soil metagenomes from five terrestrial habitat types across China and identify 75 viral operational taxonomic units (vOTUs) that encode 105 P-acquisition AMGs. These AMGs span 17 distinct functional genes involved in four primary processes of microbial P-acquisition. Among them, over 60% (11/17) have not been reported previously. We experimentally verify in-vitro enzymatic activities of two pyrophosphatases and one alkaline phosphatase encoded by P-acquisition vOTUs. Thirty-six percent of the 75 P-acquisition vOTUs are detectable in a published global topsoil metagenome dataset. Further analyses reveal that, under certain circumstances, the identified P-acquisition AMGs have a greater influence on soil P availability and are more dominant in soil metatranscriptomes than their corresponding bacterial genes. Overall, our results reinforce the necessity of incorporating viral contributions into biogeochemical P cycling.

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 CAT pack v6.0). The CAT pack now also supports Genome Taxonomy Database (GTDB) annotations.

Mora: abundance aware metagenomic read re-assignment for disentangling similar strains.

BACKGROUND: Taxonomic classification of reads obtained by metagenomic sequencing is often a first step for understanding a microbial community, but correctly assigning sequencing reads to the strain or sub-species level has remained a challenging computational problem. RESULTS: We introduce Mora, a MetagenOmic read Re-Assignment algorithm capable of assigning short and long metagenomic reads with high precision, even at the strain level. Mora is able to accurately re-assign reads by first estimating abundances through an expectation-maximization algorithm and then utilizing abundance information to re-assign query reads. The key idea behind Mora is to maximize read re-assignment qualities while simultaneously minimizing the difference from estimated abundance levels, allowing Mora to avoid over assigning reads to the same genomes. On simulated diverse reads, this allows Mora to achieve F1 scores comparable to other algorithms while having less runtime. However, Mora significantly outshines other algorithms on very similar reads. We show that the high penalty of over assigning reads to a common reference genome allows Mora to accurately infer correct strains for real data in the form of E. coli reads. CONCLUSIONS: Mora is a fast and accurate read re-assignment algorithm that is modularized, allowing it to be incorporated into general metagenomics and genomics workflows. It is freely available at https://github.com/AfZheng126/MORA .

A generic approach to infer community-level fitness of microbial genes.

The gene content in a metagenomic pool defines the function potential of a microbial community. Natural selection, operating on the level of genomes or genes, shapes the evolution of community functions by enriching some genes while depriving the others. Despite the importance of microbiomes in the environment and health, a general metric to evaluate the community-wide fitness of microbial genes remains lacking. In this work, we adapt the classic neutral model of species and use it to predict how the abundances of different genes will be shaped by selection, regardless of at which level the selection acts. We establish a simple metric that quantitatively infers the average survival capability of each gene in a microbiome. We then experimentally validate the predictions using synthetic communities of barcoded Escherichia coli strains undergoing neutral assembly and competition. We further show that this approach can be applied to publicly available metagenomic datasets to gain insights into the environment-function interplay of natural microbiomes.

The detailed analysis of the microbiome and resistome of artisanal blue-veined cheeses provides evidence on sources and patterns of succession linked with quality and safety traits.

BACKGROUND: Artisanal cheeses usually contain a highly diverse microbial community which can significantly impact their quality and safety. Here, we describe a detailed longitudinal study assessing the impact of ripening in three natural caves on the microbiome and resistome succession across three different producers of Cabrales blue-veined cheese. RESULTS: Both the producer and cave in which cheeses were ripened significantly influenced the cheese microbiome. Lactococcus and the former Lactobacillus genus, among other taxa, showed high abundance in cheeses at initial stages of ripening, either coming from the raw material, starter culture used, and/or the environment of processing plants. Along cheese ripening in caves, these taxa were displaced by other bacteria, such as Tetragenococcus, Corynebacterium, Brevibacterium, Yaniella, and Staphylococcus, predominantly originating from cave environments (mainly food contact surfaces), as demonstrated by source-tracking analysis, strain analysis at read level, and the characterization of 613 metagenome-assembled genomes. The high abundance of Tetragenococcus koreensis and Tetragenococcus halophilus detected in cheese has not been found previously in cheese metagenomes. Furthermore, Tetragenococcus showed a high level of horizontal gene transfer with other members of the cheese microbiome, mainly with Lactococcus and Staphylococcus, involving genes related to carbohydrate metabolism functions. The resistome analysis revealed that raw milk and the associated processing environments are a rich reservoir of antimicrobial resistance determinants, mainly associated with resistance to aminoglycosides, tetracyclines, and ß-lactam antibiotics and harbored by aerobic gram-negative bacteria of high relevance from a safety point of view, such as Escherichia coli, Salmonella enterica, Acinetobacter, and Klebsiella pneumoniae, and that the displacement of most raw milk-associated taxa by cave-associated taxa during ripening gave rise to a significant decrease in the load of ARGs and, therefore, to a safer end product. CONCLUSION: Overall, the cave environments represented an important source of non-starter microorganisms which may play a relevant role in the quality and safety of the end products. Among them, we have identified novel taxa and taxa not previously regarded as being dominant components of the cheese microbiome (Tetragenococcus spp.), providing very valuable information for the authentication of this protected designation of origin artisanal cheese. Video Abstract.

Discovery and description of novel phage genomes from urban microbiomes sampled by the MetaSUB consortium.

Bacteriophages are recognized as the most abundant members of microbiomes and have therefore a profound impact on microbial communities through the interactions with their bacterial hosts. The International Metagenomics and Metadesign of Subways and Urban Biomes Consortium (MetaSUB) has sampled mass-transit systems in 60 cities over 3 years using metagenomics, throwing light into these hitherto largely unexplored urban environments. MetaSUB focused primarily on the bacterial community. In this work, we explored MetaSUB metagenomic data in order to recover and analyze bacteriophage genomes. We recovered and analyzed 1714 phage genomes with size at least 40 kbp, from the class Caudoviricetes, the vast majority of which (80%) are novel. The recovered genomes were predicted to belong to temperate (69%) and lytic (31%) phages. Thirty-three of these genomes have more than 200 kbp, and one of them reaches 572 kbp, placing it among the largest phage genomes ever found. In general, the phages tended to be site-specific or nearly so, but 194 genomes could be identified in every city from which phage genomes were retrieved. We predicted hosts for 48% of the phages and observed general agreement between phage abundance and the respective bacterial host abundance, which include the most common nosocomial multidrug-resistant pathogens. A small fraction of the phage genomes are carriers of antibiotic resistance genes, and such genomes tended to be particularly abundant in the sites where they were found. We also detected CRISPR-Cas systems in five phage genomes. This study expands the previously reported MetaSUB results and is a contribution to the knowledge about phage diversity, global distribution, and phage genome content.

A genome-centric view of the role of the Acropora kenti microbiome in coral health and resilience.

Microbial diversity has been extensively explored in reef-building corals. However, the functional roles of coral-associated microorganisms remain poorly elucidated. Here, we recover 191 bacterial and 10 archaeal metagenome-assembled genomes (MAGs) from the coral Acropora kenti (formerly A. tenuis) and adjacent seawater, to identify microbial functions and metabolic interactions within the holobiont. We show that 82 MAGs were specific to the A. kenti holobiont, including members of the Pseudomonadota, Bacteroidota, and Desulfobacterota. A. kenti-specific MAGs displayed significant differences in their genomic features and functional potential relative to seawater-specific MAGs, with a higher prevalence of genes involved in host immune system evasion, nitrogen and carbon fixation, and synthesis of five essential B-vitamins. We find a diversity of A. kenti-specific MAGs encode the biosynthesis of essential amino acids, such as tryptophan, histidine, and lysine, which cannot be de novo synthesised by the host or Symbiodiniaceae. Across a water quality gradient spanning 2° of latitude, A. kenti microbial community composition is correlated to increased temperature and dissolved inorganic nitrogen, with corresponding enrichment in molecular chaperones, nitrate reductases, and a heat-shock protein. We reveal mechanisms of A. kenti-microbiome-symbiosis on the Great Barrier Reef, highlighting the interactions underpinning the health of this keystone holobiont.

Taxonomic and metabolic development of the human gut microbiome across life stages: a worldwide metagenomic investigation.

The human gut microbiota is a dynamic community of microorganisms that undergo variable changes over the entire life span. To thoroughly investigate the possible fluctuations of the microbiota throughout human life, we performed a pooled analysis of healthy fecal samples across different age groups covering the entire human life span. Our study integrated data from 79 publicly available studies and new stool samples from an Italian cohort, i.e., the Parma Microbiota project, resulting in 6,653 samples processed through the shotgun metagenomic approach. This approach has allowed species-level taxonomic reconstruction of the gut microbiota and investigation of its metabolic potential across the human life span. From a taxonomic point of view, our findings confirmed and detailed at species-level accuracy that the microbial richness of the gut microbiota gradually increases in the first stage of life, becoming relatively stable during adolescence. Moreover, the analysis identified the potential core microbiota representative of distinct age groups, revealing age-related bacterial patterns and the continuous rearrangement of the microbiota in terms of relative abundances across the life span rather than the acquisition and loss of taxa. Furthermore, the shotgun approach provided insights into the functional contribution of the human gut microbiome. The metagenomic analysis revealed functional age-related differences, particularly in carbohydrate and fiber metabolism, suggesting a co-evolution of the microbiome assembly with diet. Additionally, we identified correlations between vitamin synthesis, such as thiamine and niacin, and early life, suggesting a potential role of the microbiome in human physiology, in particular in the functions of the host's nervous and immune systems. IMPORTANCE: In this study, we provided comprehensive insights into the dynamic nature of the human gut microbiota across the human life span. In detail, we analyzed a large data set based on a shotgun metagenomic approach, combining public data sets and new samples from the Parma Microbiota project and obtaining a detailed overview of the possible relationship between gut microbiota development and aging. Our findings confirmed the main stages in microbial richness development and revealed specific core microbiota associated with different age stages. Moreover, the shotgun metagenomic approach allowed the disentangling of the functional changes in the microbiome across the human life span, particularly in diet-related metabolism, which is probably correlated to bacterial co-evolution with dietary habits. Notably, our study also uncovered positive correlations with vitamin synthesis in early life, suggesting a possible impact of the microbiota on human physiology.

Discovery and characterization of novel DNA viruses in Apis mellifera: expanding the honey bee virome through metagenomic analysis.

To date, many viruses have been discovered to infect honey bees. In this study, we used high-throughput sequencing to expand the known virome of the honey bee, Apis mellifera, by identifying several novel DNA viruses. While the majority of previously identified bee viruses are RNA, our study reveals nine new genomes from the Parvoviridae family, tentatively named Bee densoviruses 1 to 9. In addition, we characterized a large DNA virus, Apis mellifera filamentous-like virus (AmFLV), which shares limited protein identities with the known Apis mellifera filamentous virus. The complete sequence of AmFLV, obtained by a combination of laboratory techniques and bioinformatics, spans 152,678 bp. Linear dsDNA genome encodes for 112 proteins, of which 49 are annotated. Another large virus we discovered is Apis mellifera nudivirus, which belongs to a group of Alphanudivirus. The virus has a length of 129,467 bp and a circular dsDNA genome, and has 106 protein encoding genes. The virus contains most of the core genes of the family Nudiviridae. This research demonstrates the effectiveness of viral binning in identifying viruses in honey bee virology, showcasing its initial application in this field.IMPORTANCEHoney bees contribute significantly to food security by providing pollination services. Understanding the virome of honey bees is crucial for the health and conservation of bee populations and also for the stability of the ecosystems and economies for which they are indispensable. This study unveils previously unknown DNA viruses in the honey bee virome, expanding our knowledge of potential threats to bee health. The use of the viral binning approach we employed in this study offers a promising method to uncovering and understanding the vast viral diversity in these essential pollinators.

Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics.

BACKGROUND: Microbiota are closely associated with human health and disease. Metaproteomics can provide a direct means to identify microbial proteins in microbiota for compositional and functional characterization. However, in-depth and accurate metaproteomics is still limited due to the extreme complexity and high diversity of microbiota samples. It is generally recommended to use metagenomic data from the same samples to construct the protein sequence database for metaproteomic data analysis. Although different metagenomics-based database construction strategies have been developed, an optimization of gene taxonomic annotation has not been reported, which, however, is extremely important for accurate metaproteomic analysis. RESULTS: Herein, we proposed an accurate taxonomic annotation pipeline for genes from metagenomic data, namely contigs directed gene annotation (ConDiGA), and used the method to build a protein sequence database for metaproteomic analysis. We compared our pipeline (ConDiGA or MD3) with two other popular annotation pipelines (MD1 and MD2). In MD1, genes were directly annotated against the whole bacterial genome database; in MD2, contigs were annotated against the whole bacterial genome database and the taxonomic information of contigs was assigned to the genes; in MD3, the most confident species from the contigs annotation results were taken as reference to annotate genes. Annotation tools, including BLAST, Kaiju, and Kraken2, were compared. Based on a synthetic microbial community of 12 species, it was found that Kaiju with the MD3 pipeline outperformed the others in the construction of protein sequence database from metagenomic data. Similar performance was also observed with a fecal sample, as well as in silico mixed datasets of the simulated microbial community and the fecal sample. CONCLUSIONS: Overall, we developed an optimized pipeline for gene taxonomic annotation to construct protein sequence databases. Our study can tackle the current taxonomic annotation reliability problem in metagenomics-derived protein sequence database and can promote the in-depth metaproteomic analysis of microbiome. The unique metagenomic and metaproteomic datasets of the 12 bacterial species are publicly available as a standard benchmarking sample for evaluating various analysis pipelines. The code of ConDiGA is open access at GitHub for the analysis of microbiota samples. Video Abstract.