Metagenome Assembled Genome Analysis: From Bins to Biological Insights

Metagenome-assembled genomes (MAGs) are microbial genomes reconstructed directly from shotgun sequencing data of mixed microbial communities, bypassing the need for laboratory cultivation. For students, researchers, analysts, and life-science professionals working with environmental, agricultural, or clinical samples, the path from raw sequencing reads to biologically meaningful MAGs involves binning, quality assessment, taxonomic assignment, and functional analysis. This article describes the practical steps for evaluating MAG quality, assigning taxonomy, and extracting functional insights, with emphasis on reproducible workflows, common pitfalls, and interpretation limits.

What Are Metagenome Assembled Genomes

MAGs are produced when shotgun sequencing reads from a mixed microbial community are assembled into longer contiguous sequences (contigs), and those contigs are grouped into bins that represent individual microbial genomes. This approach captures the genomic content of uncultivated Bacteria and Archaea, which account for the vast majority of microbial species on Earth. The recovery of genomes directly from environmental samples using shotgun sequencing has only become practical in recent years, driven by advances in sequencing throughput and computational methods.

The process begins with DNA extraction from a sample such as soil, sediment, rumen fluid, urine, or coastal marine sediment. After sequencing, reads are quality trimmed and assembled. Binning algorithms group contigs by sequence composition and coverage depth, producing draft genomes. These drafts require rigorous quality control before they can support biological conclusions. The EMBL-EBI Training portal provides foundational instruction on sequence analysis concepts, and the NCBI Data Resources host the public sequence repositories where MAGs are deposited.

MAGs differ from isolate genomes in several important ways. Isolate genomes come from a single cultured organism and are usually complete or near-complete. MAGs are typically incomplete, may contain contamination from other community members, and represent consensus sequences of closely related strains present in the sample. These characteristics shape every downstream analysis decision.

Quality Assessment of Metagenome Assembled Genomes

Quality assessment is the first mandatory step after binning. A MAG with high contamination or low completeness will produce misleading taxonomic and functional conclusions. The Minimum Information about a Metagenome-Assembled Genome (MIMAG) criteria provide the community standard for reporting MAG quality, and most published workflows reference these standards.

Completeness and Contamination Estimation

Completeness measures what fraction of the expected single-copy core genes are present in the MAG. Contamination measures how many duplicated or unexpected single-copy genes appear, indicating that sequences from multiple organisms were merged into one bin. Tools such as CheckM and BUSCO estimate these metrics using lineage-specific marker gene sets. The KBase platform integrates these quality checks into an end-to-end workflow that takes users from raw reads to MAGs, with quality assessment as a built-in step.

A high-quality MAG typically shows completeness above 90% and contamination below 5%. Near-complete genomes reach higher completeness thresholds with minimal contamination. However, these thresholds are not absolute rules. The acceptable quality depends on the research question. A survey of community composition may tolerate lower completeness, while metabolic reconstruction or comparative genomics demands the highest quality bins.

Refinement and Reassembly

Initial bins often contain contamination or miss sequences that belong to the genome. Refinement tools compare bin boundaries against coverage and composition signals to split or merge bins. Reassembly uses the reads assigned to a refined bin to produce a better assembly, often improving completeness and reducing fragmentation. The MetaflowX workflow includes a dedicated reassembly module that increased completeness by 5.6% and reduced contamination by 53% on average in benchmarking tests. This improvement can be decisive for downstream functional analysis.

Quality Reporting

Every MAG should be reported with its completeness, contamination, number of contigs, N50, and total length. Strain heterogeneity, which indicates the presence of multiple closely related strains within one bin, is also informative. These metrics belong in the methods section of any publication and in the metadata of any database submission. The Microbiome Datahub aggregates MAGs from public repositories and re-assesses their quality, reporting an average completeness of 80.5% and contamination of 1.8% across 214,427 MAGs, with the most frequent values exceeding 95% completeness and falling below 1% contamination.

Taxonomic Assignment of Metagenome Assembled Genomes

Taxonomic assignment places a MAG within the microbial tree of life. The Genome Taxonomy Database (GTDB) provides a phylogenetically consistent and rank-normalized taxonomy for bacterial and archaeal genomes, built from whole-genome sequences including MAGs and single-amplified genomes. GTDB uses average nucleotide identity (ANI) and relative evolutionary divergence to classify genomes from species to domain, and it has become an essential resource for microbiologists.

GTDB-Tk and Phylogenomic Placement

The GTDB Toolkit (GTDB-Tk) assigns taxonomy to MAGs by placing them in a reference tree and using ANI to refine species-level assignments. This approach handles the large fraction of MAGs that represent novel lineages. In a study of plastic-polluted coastal sediment, 90% of recovered MAGs could only be classified at intermediate taxonomic levels in GTDB, indicating the presence of novel microbial lineages. The Type Strain Genome Server (TYGS) confirmed 3 novel orders, 16 families, and 28 genera among those MAGs.

Integrating Taxonomic Signals from Reads, Contigs, and MAGs

Taxonomic profiling can be performed at multiple levels. Read-based profiling assigns individual reads to taxa, while assembly-based profiling uses contigs and MAGs. The Read Annotation Tool (RAT) integrates taxonomic signals from reads, contigs, and MAGs into a single profile, achieving high precision and sensitivity. In high-diversity groundwater samples, RAT annotated a large fraction of metagenomic reads and called novel taxa at appropriate taxonomic ranks. This integrative approach provides a more complete view of community composition than any single method.

Handling Novel Lineages

Many MAGs cannot be assigned to a named species. This is expected and does not diminish their value. Novel lineages can be described using the SeqCode nomenclature framework, which accommodates genome-based taxonomy for uncultivated microorganisms. In deep-sea hydrothermal vent studies, researchers used phylogenomic analysis, GTDB-Tk assessment, 16S rRNA gene phylogeny, and average amino acid identity to identify at least nine genus-level clades and propose names for novel genera under SeqCode requirements.

Functional Analysis of Metagenome Assembled Genomes

Functional analysis interprets the gene content of a MAG to predict metabolic capabilities, ecological roles, and biotechnological potential. This analysis connects genomic data to biological function.

Gene Prediction and Annotation

Gene prediction identifies protein-coding sequences, tRNA genes, and rRNA operons within a MAG. Annotation assigns functions to those genes using databases such as KEGG, COG, and CAZy. The KBase platform supports functional assignment, metabolic modeling, pangenome functional profiling, and RNA-Seq analysis after MAG recovery. The Galaxy meta-omics workflows integrate metagenomics, metatranscriptomics, and metaproteomics, allowing researchers to link MAG gene content to expressed mRNA and protein levels.

Metabolic Reconstruction

Metabolic reconstruction maps annotated genes to biochemical pathways. This reveals what a microorganism can metabolize, which substrates it can use, and what products it can generate. In a study of Heimdallarchaeia MAGs from deep-sea cold seeps and hydrothermal vents, metabolic reconstruction revealed the potential for sulfate reduction, nitrate reduction, carbon fixation through an atypical Wood-Ljungdahl pathway, and light utilization through bacteriochlorophyll and carotenoid biosynthesis genes. These predictions suggested a mixotrophic lifestyle that gives these archaea flexibility in harsh deep-sea conditions.

Functional Annotation of Carbohydrate-Active Enzymes

Carbohydrate-active enzymes (CAZymes) are particularly relevant for agricultural and environmental samples. In a study of the bovine rumen microbiome, MAGs belonging to Bacteroidota and Spirochaetota showed potential for hydrolysis of complex plant cell wall polysaccharides, production of B-complex vitamins, and protein degradation. The ViMO web application visualizes CAZyme annotations, KEGG pathways, and mRNA and protein abundances within recovered MAGs.

Detection of Antibiotic Resistance and Virulence Genes

Functional annotation can identify clinically relevant features such as antibiotic resistance genes and virulence factors. The MetaflowX workflow includes functional annotation modules for detecting these features. In a hospital environment study, whole-genome sequencing of a Pseudomonas aeruginosa isolate identified four beta-lactam resistance genes and nine additional antimicrobial resistance genes, demonstrating the value of genome-based resistance surveillance.

Workflow Options and Tradeoffs

Multiple workflows exist for MAG reconstruction and analysis, each with different strengths and limitations. The choice depends on sample type, sequencing depth, computational resources, and research goals.

Integrated Pipelines

Integrated pipelines combine quality control, assembly, binning, refinement, and annotation into a single automated workflow. The MetaflowX workflow integrates reference-based and reference-free approaches, completing full metagenomic analyses up to 14-fold faster with 38% less disk usage than existing workflows. It recovered the highest number of high-quality and taxonomically diverse MAGs in benchmarking tests. The TOFU-MAaPO pipeline provides a portable, automated Nextflow pipeline for large-scale analysis, yielding 12% to 77% more high-quality MAGs than three established pipelines by integrating multiple complementary binning tools with a unified refinement strategy.

Platform-Based Approaches

The KBase platform offers an end-to-end workflow from raw reads to MAGs, with intuitive usability, data interoperability, and tool integration. The process takes about a day for smaller shotgun read libraries and up to about a week for larger libraries. KBase supports phylogenetic placement, functional assignment, metabolic modeling, pangenome functional profiling, and RNA-Seq analysis. The Galaxy platform provides modular workflows for meta-omics integration, with a standard module and an optimized module that improves MAG quality in complex samples through single- and co-assembly and dereplication after binning.

Containerized Desktop Solutions

For researchers who prefer local analysis, containerized solutions package tools with their dependencies. The TaxaScope workstation provides a graphical interface for genome quality assessment, annotation, phylogenomic inference, genome relatedness estimation, and functional profiling. It uses Docker and Podman containers to provide version-locked execution environments without requiring manual dependency management or Linux expertise. This approach addresses data privacy concerns, upload latency, and storage quotas associated with cloud platforms.

Choosing a Workflow

The 2Pipe workflow starts with a question to match users with the correct pipeline for MAG reconstruction. This reflects the reality that no single workflow suits all projects. Consider the following factors when choosing:

• Sample complexity and expected diversity
• Sequencing depth and read length
• Computational resources and time constraints
• Need for integration with other omics data
• Data privacy and sharing requirements
• User expertise and preference for graphical or command-line interfaces

Practical Implementation Steps

A reproducible MAG analysis requires careful planning and documentation. The following steps provide a practical framework.

Step 1: Define the Research Question and Sampling Design

The research question determines sequencing depth, replication, and analysis strategy. For community composition surveys, shallower sequencing may suffice. For recovering high-quality MAGs of rare or novel lineages, deeper sequencing is required. Ultra-deep metagenomic sequencing enabled reconstruction of 223 reliable quality MAGs from human urine samples, including multiple Escherichia coli subpopulations within individual samples. This resolution required high-depth sequencing and minimal host contamination.

Step 2: Quality Control of Raw Reads

Trim adapters, remove low-quality bases, and filter host or contaminant reads. Document the fraction of reads retained after each step. This step is critical because assembly quality depends on input read quality.

Step 3: Assembly

Assemble reads into contigs using an assembler appropriate for the data type. Hybrid assembly combining short and long reads can improve results. The Burkholderia gladioli genome study used combined Illumina short-read and PacBio long-read sequencing, producing a complete genome with two circular chromosomes totaling 8,148,635 bp.

Step 4: Binning

Group contigs into bins using coverage and composition signals. Multiple binning tools should be used and their results integrated. The TOFU-MAaPO pipeline integrates multiple complementary binning tools with a unified refinement strategy, yielding more high-quality MAGs than single-tool approaches. In the plastic-polluted sediment study, three binning tools produced 2,374 initial bins that were refined to 52 non-redundant MAGs.

Step 5: Quality Assessment and Refinement

Assess completeness and contamination with CheckM or BUSCO. Refine bins to remove contamination and recover missing sequences. Reassemble refined bins when necessary. Document all quality metrics for every MAG.

Step 6: Dereplication

Remove redundant MAGs that represent the same species or strain. Dereplication reduces the dataset to non-redundant genomes for downstream analysis. The Galaxy optimized workflow implements dereplication after binning to improve MAG quality in complex samples.

Step 7: Taxonomic Assignment

Classify MAGs using GTDB-Tk or equivalent tools. Report taxonomy at all assigned ranks. For novel lineages, note the highest rank at which classification was possible.

Step 8: Functional Annotation

Predict genes and assign functions using multiple databases. Integrate annotations to reconstruct metabolic pathways. For meta-omics studies, link gene content to expression data.

Step 9: Data Deposition

Deposit MAGs in public repositories such as NCBI and provide metadata following community standards. The NIH Genomic Data Sharing Policy governs data sharing for NIH-funded research. The FAIR Guiding Principles provide a framework for making data findable, accessible, interoperable, and reusable.

Records and Measurements

Maintaining detailed records is essential for reproducibility and for interpreting results. The following measurements should be recorded for every MAG analysis project.

Per-Sample Records

• Sample identifier, collection site, date, and environmental metadata
• DNA extraction method and sequencing platform
• Number of raw reads and bases
• Number of reads retained after quality control
• Assembly statistics including N50, number of contigs, and total assembled bases
• Number of bins produced and number of MAGs after refinement and dereplication

Per-MAG Records

• Completeness percentage
• Contamination percentage
• Strain heterogeneity
• Number of contigs and N50
• Total genome length and GC content
• Number of protein-coding sequences, tRNA genes, and rRNA operons
• Taxonomic assignment at each rank
• Functional annotation summary including CAZyme counts and KEGG pathway coverage

Quality Control Logs

• Versions of all software tools and databases
• Parameter settings for assembly, binning, and annotation
• Timestamps for each analysis step
• Computational resource usage

The Microbiome Datahub demonstrates the value of standardized records. It aggregates 214,427 MAGs from INSDC, re-annotates them with consistent methods, and provides standardized taxonomic assignments, phenotypic trait inference, and environmental metadata annotation. This resource makes previously deposited MAGs reusable for comparative analysis.

Common Failure Patterns

Several recurring problems undermine MAG analysis. Recognizing these patterns helps avoid wasted effort and misleading conclusions.

Overestimating MAG Quality

Completeness and contamination estimates from marker gene analysis can be inaccurate for unusual lineages. Lineage-specific markers may be absent from deeply diverged taxa, leading to underestimated completeness. Conversely, contamination from closely related strains may not be detected by marker gene duplication. Cross-validate quality estimates with multiple methods and inspect bin coverage profiles manually.

Ignoring Strain Heterogeneity

MAGs from communities with closely related strains often contain sequences from multiple strains. This produces inflated genome sizes, spurious gene duplications, and distorted metabolic predictions. The ultra-deep urine metagenome study identified multiple Escherichia coli subpopulations within individual samples, demonstrating that strain-level resolution is possible with sufficient sequencing depth. When strain heterogeneity is detected, consider whether the research question requires strain-level resolution or can tolerate population-level consensus genomes.

Overinterpreting Functional Predictions

Functional annotation predicts genetic potential, not actual activity. A gene may be present but not expressed, or expressed under conditions not present in the sampled environment. The Galaxy meta-omics workflows address this limitation by integrating metatranscriptomics and metaproteomics, linking gene content to mRNA and protein abundance. Without expression data, functional predictions should be framed as hypotheses.

Taxonomic Misassignment

Taxonomic assignment errors occur when MAGs are placed in reference trees with limited representation of related lineages. Novel lineages may be assigned to the nearest named taxon, creating false impressions of relatedness. The high proportion of MAGs classified only at intermediate taxonomic levels in the plastic-polluted sediment study illustrates this limitation. Report the confidence of taxonomic assignments and note when assignments are provisional.

Insufficient Sequencing Depth

Shallow sequencing produces fragmented assemblies and incomplete MAGs. This is particularly problematic for low-abundance community members. The TOFU-MAaPO pipeline analyzed 16,462 human gut metagenome samples from the Sequence Read Archive, demonstrating that large-scale analysis is feasible with appropriate computational resources. However, the quality of recovered MAGs depends on the depth of the original sequencing.

Computational Resource Mismanagement

Assembly and binning of large metagenomes require substantial memory and disk space. The MetaflowX workflow addresses this with efficient resource use, completing analyses up to 14-fold faster with 38% less disk usage. Plan computational requirements before starting, and use workflows designed for scalability.

Limitations and Interpretation Boundaries

MAG analysis has inherent limitations that constrain biological interpretation.

MAGs Are Consensus Genomes

A MAG represents a consensus of the reads assembled into it. If multiple strains of the same species are present, the MAG may contain chimeric sequences or lose strain-specific variation. This limits the resolution of population genetics and strain-level functional differences.

Functional Predictions Require Validation

Genes identified in a MAG may not be expressed under the conditions sampled. Metabolic predictions should be validated with cultivation studies, expression data, or biochemical assays where possible. The rumen microbiome study combined genomic predictions with phenotypic validation, demonstrating the value of experimental confirmation.

Database Bias Affects Annotation

Functional annotation depends on reference databases that are biased toward well-studied organisms. Genes from novel lineages may have no homologs in reference databases, leading to high fractions of hypothetical proteins. This limits functional interpretation for the most novel MAGs.

Taxonomic Frameworks Are Evolving

The Genome Taxonomy Database and SeqCode represent ongoing transitions in microbial taxonomy from phenotypic and single-gene frameworks to genome-based frameworks. Taxonomic assignments may change as reference databases expand and classification methods improve. Report the database version used for taxonomic assignment.

Data Sharing Requirements

Public deposition of MAGs and associated metadata is expected for published research. The NIH Genomic Data Sharing Policy specifies requirements for NIH-funded research, and the FAIR Guiding Principles provide a framework for data management. Plan for data deposition from the start of the project.

Safety and Regulatory Context

MAG analysis has implications for biosafety, biosecurity, and data governance.

Biosafety Considerations

MAGs reconstructed from clinical or environmental samples may contain genes associated with pathogenicity or antibiotic resistance. The Pseudomonas aeruginosa genome study identified multiple beta-lactam resistance genes and nine additional antimicrobial resistance genes in a hospital environmental isolate. Researchers working with such data should follow institutional biosafety guidelines and consider the implications of publishing resistance gene data.

Data Privacy and Governance

Metagenomic data from human samples may contain human reads and raise privacy concerns. The NIH Genomic Data Sharing Policy addresses these concerns for NIH-funded research. The TaxaScope workstation highlights data privacy concerns associated with cloud-based platforms, offering a local alternative for sensitive data.

Responsible Reporting

When reporting MAGs with potential clinical or agricultural significance, provide appropriate context and avoid overstatement. Functional predictions should be clearly distinguished from validated observations. The EMBL-EBI Training and NCBI Data Resources provide guidance on responsible data reporting and deposition.

Professional Escalation Criteria

Knowing when to seek expert assistance prevents costly errors and improves outcomes.

Escalate When Quality Metrics Are Unacceptable

If a large fraction of bins fails quality thresholds after refinement and reassembly, consult with a bioinformatics specialist. This may indicate problems with sequencing depth, assembly parameters, or binning strategy that require expert intervention.

Escalate When Taxonomic Assignment Is Ambiguous

If MAGs cannot be assigned to any known lineage or if assignments conflict between methods, consult with a microbial taxonomist. The Genome Taxonomy Database and SeqCode communities can provide guidance on describing novel lineages.

Escalate When Functional Predictions Have Clinical or Agricultural Implications

If functional analysis identifies genes with potential implications for human health, animal health, or agricultural productivity, consult with domain experts before drawing conclusions. The anastomotic leakage prediction study demonstrates the complexity of translating molecular data into clinical predictions, and similar caution applies to microbiome-based predictions.

Escalate When Computational Resources Are Insufficient

If the analysis exceeds available computational resources, consult with a high-performance computing specialist or consider using established workflows designed for scalability. The TOFU-MAaPO pipeline and MetaflowX workflow provide scalable options for large datasets.

At a Glance

Common Failure Patterns and Corrective Actions

Frequently Asked Questions

What is the difference between a MAG and an isolate genome?

A MAG is reconstructed from shotgun sequencing of a mixed microbial community, while an isolate genome comes from a single cultured organism. MAGs are typically incomplete, may contain contamination from other community members, and represent consensus sequences of closely related strains. Isolate genomes are usually complete or near-complete and represent a single strain.

How do I know if my MAG is high quality?

High-quality MAGs typically show completeness above 90% and contamination below 5%, as estimated by marker gene analysis tools such as CheckM and BUSCO. The MIMAG criteria provide community standards for reporting MAG quality. However, acceptable quality depends on the research question, and lower quality MAGs may still be useful for some analyses.

What is the Genome Taxonomy Database and why is it important?

The Genome Taxonomy Database (GTDB) provides a phylogenetically consistent and rank-normalized taxonomy for bacterial and archaeal genomes, built from whole-genome sequences including MAGs. It uses average nucleotide identity and relative evolutionary divergence to classify genomes from species to domain. GTDB has become an essential taxonomic resource because it handles the large fraction of MAGs that represent novel lineages.

How can I assign taxonomy to a MAG that does not match any known species?

Use GTDB-Tk or equivalent tools to place the MAG in a reference tree and determine the highest taxonomic rank at which confident assignment is possible. Many MAGs can only be classified at intermediate taxonomic levels, indicating novel lineages. Novel lineages can be described using the SeqCode nomenclature framework, which accommodates genome-based taxonomy for uncultivated microorganisms.

What functional analyses can I perform on MAGs?

Functional analysis includes gene prediction, annotation against databases such as KEGG, COG, and CAZy, metabolic pathway reconstruction, and detection of antibiotic resistance and virulence genes. For meta-omics studies, gene content can be linked to mRNA and protein expression data. The KBase platform and Galaxy workflows support these analyses.

How do I integrate MAG analysis with metatranscriptomics or metaproteomics?

Integrative meta-omics workflows combine metagenomics, metatranscriptomics, and metaproteomics to link gene content to expression and activity. The Galaxy platform provides workflows for this integration, and the ViMO web application visualizes MAG taxonomy, quality, CAZyme annotations, KEGG pathways, and mRNA and protein abundances.

What are the main limitations of MAG-based functional predictions?

Functional predictions identify genetic potential, not actual activity. Genes may be present but not expressed under sampled conditions. Database bias means genes from novel lineages may have no homologs in reference databases. Functional predictions should be validated with cultivation studies, expression data, or biochemical assays where possible.

Where should I deposit my MAGs and what metadata should I provide?

Deposit MAGs in public repositories such as NCBI, ENA, or DDBJ. Provide completeness, contamination, number of contigs, N50, total length, and strain heterogeneity for each MAG. Include environmental metadata following community standards. The NIH Genomic Data Sharing Policy specifies requirements for NIH-funded research, and the FAIR Guiding Principles provide a framework for data management.

Related Bioinformatics Guides

• Hidden Markov Models in Biological Sequence Analysis
• GWAS QC Steps: Structural Analysis and Computational Methodologies in Bioinformatics
• Metagenomics Taxonomic Classification: Kraken2 and Functional Annotation Pipelines
• Qiime2 Taxonomic Classification: Structural Analysis and Computational Methodologies in Bioinformatics
• FASTQ File Format: Decoding Phred Quality Scores and Quality Control Workflows

References and Further Reading

• EMBL-EBI Training. European Bioinformatics Institute.
• NCBI Data Resources. National Center for Biotechnology Information.
• Genomic Data Sharing Policy. National Institutes of Health.
• The FAIR Guiding Principles. Scientific Data.
• MetaflowX: a scalable and resource-efficient workflow for multi-strategy metagenomic analysis.. Nucleic acids research, 2025.
• Development of a Clinical Prediction Model for Anastomotic Leakage in Colorectal Surgery.. JAMA network open, 2025.
• Analytical and computational workflow for in-depth analysis of oxidized complex lipids in blood plasma.. Nature communications, 2022.
• High-Throughput Proteomic and Phosphoproteomic Analysis of Formalin-Fixed Paraffin-Embedded Tissues.. Molecular & cellular proteomics : MCP, 2025.
• Integrative meta-omics in Galaxy and beyond.. Environmental microbiome, 2023.
• Metagenome-assembled genome extraction and analysis from microbiomes using KBase.. Nature protocols, 2023.
• Automated Mag-Net Enrichment Unlocks Deep and Cost-Effective LC-MS Plasma Proteomics.. Journal of proteome research, 2026.
• Mining the plasma proteome: Evaluation of enrichment methods for depth and reproducibility.. Journal of proteomics, 2025.
• Genome-wide analysis and growth-promoting potential of &lt,i&gt,Burkholderia gladioli&lt,/i&gt, YNK-FB0053.. 2026.
• TaxaScope: a container-native, visualization-centric workstation for genome-based bacterial taxonomy.. 2026.
• TOFU-MAaPO: fast, scalable and reproducible analysis of large metagenome sequence data from the Sequence Read Archive.. 2026.
• Draft genome sequences of two &lt,i&gt,Ralstonia mannitolilytica&lt,/i&gt, strains (H3G44 and H3G46) isolated from the gut of captured &lt,i&gt,Tenualosa ilisha&lt,/i&gt,.. 2026.
• Whole Genome sequencing data of &lt,i&gt,Pseudomonas aeruginosa&lt,/i&gt, strain PA-Mosul-25, harboring the &lt,i&gt,bla&lt,/i&gt, &lt,sub&gt,VEB-9&lt,/sub&gt, ESBL gene, isolated from a hospital environment in Nineveh, Iraq.. 2026.
• Ultra-deep metagenomic sequencing enables reconstruction of diverse, high quality microbial genomes from human urine samples.. 2026.
• Integrating taxonomic signals from MAGs and contigs improves read annotation and taxonomic profiling of metagenomes. Nature Communications, 2024.
• Microbiome Datahub: an open-access platform integrating environmental metadata, taxonomy, and functional annotation for comprehensive metagenome-assembled genome datasets. Microbiome, 2026.
• Genome Taxonomy Database and SeqCode: Microbial taxonomy and nomenclature in the age of big sequence data. Biodiversity Information Science and Standards, 2023.
• Metagenome Sequencing and Recovery of 52 Microbial Genomes from Plastic-Polluted Coastal Sediment. Scientific Data, 2026.
• Metagenomic insights into Heimdallarchaeia clades from the deep-sea cold seep and hydrothermal vent. Environmental Microbiome, 2024.
• Genomic comparison of deep-sea hydrothermal genera related to Aeropyrum, Thermodiscus and Caldisphaera, and proposed emended description of the family Acidilobaceae.. Systematic and Applied Microbiology, 2024.
• Functional and Phylogenetic Characterization of Bacteria in Bovine Rumen Using Fractionation of Ruminal Fluid. Frontiers in Microbiology, 2022.
• Performance comparison between solid phase extraction and magnetic carbon nanotubes facilitated dispersive-micro solid phase extractions (Mag-CNTs/d-µSPE) of a cyanide metabolite in biological samples using GC-MS. Journal of Analytical Science and Technology, 2021.
• 2Pipe starts with a question: Matching you with the correct pipeline for MAG reconstruction. Msystems, 2026.
• SnakeMAGs: a simple, efficient, flexible and scalable workflow to reconstruct prokaryotic genomes from metagenomes. F1000research, 2023.

This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.