Metagenomics as a Tool for Microbial Genomics

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-27

Quick Answer

• Shotgun metagenomics lets you sequence all DNA in a microbial community and reconstruct individual genomes through assembly and binning, without prior cultivation.
• The practical path runs from DNA extraction through quality control, assembly, binning, taxonomic assignment, and genome-resolved analysis, with tools selected by data type and research question.
• A key limitation is that low-abundance organisms often fail to assemble into complete genomes, so results depend on sequencing depth and community complexity.

At a Glance

Introduction to Metagenomics and Microbial Genomics

Metagenomics is the study of genetic material recovered directly from environmental or clinical samples, bypassing the need to isolate and culture individual organisms. For bacterial genomics, this approach is essential because the vast majority of microbial species resist standard laboratory cultivation. Shotgun metagenomics sequences all DNA present in a sample, allowing researchers to examine community composition, functional potential, and evolutionary relationships without prior knowledge of which organisms are present.

The field has matured from early marker-gene surveys to genome-resolved metagenomics. In genome-resolved metagenomics, researchers assemble short reads into longer contiguous sequences, then group those sequences into bins that represent individual genomes. These metagenome-assembled genomes, or MAGs, enable the study of uncultivated bacteria at the genomic level. This capability has transformed microbial ecology, clinical microbiology, and public health surveillance.

For biology students, researchers, laboratory professionals, and life-science practitioners, the practical challenge is not understanding the concept but executing a reliable workflow. The path from raw reads to assembled genomes requires careful decisions at each stage. This article provides a practical pipeline for shotgun metagenomics, including quality control, assembly, binning, and genome-resolved analysis, with tool recommendations and the reasoning behind each step.

Core Principles of Shotgun Metagenomics

Shotgun metagenomics differs from targeted amplicon sequencing in a fundamental way. Amplicon sequencing, such as 16S rRNA gene sequencing, amplifies a specific genetic marker and provides a profile of which taxa are present. Shotgun metagenomics sequences all DNA fragments in a sample, providing both taxonomic composition and the genetic content of the community. This means you can identify which genes are present, including those related to antimicrobial resistance, virulence, and metabolic pathways.

The central principle is that the sequencing reads are a random sample of the DNA in the sample. The depth of sequencing determines how much of the community is captured. A high-complexity community with many species requires more sequencing depth to detect low-abundance members. The assembly process reconstructs longer contiguous sequences from overlapping reads, and the binning process groups those contigs into genome-like units.

The quality of the final genomes depends on the quality of the input data. Poor extraction, degraded DNA, or contamination will propagate through the pipeline. The same rigor applied to culture-based work, such as using authenticated reference strains and validated methods, applies to metagenomic workflows. The American Type Culture Collection provides authenticated biological materials that can serve as positive controls and reference standards for method validation. The Bacteriological Analytical Manual from the U.S. Food and Drug Administration provides official methods for food microbiology that include quality control principles applicable to any molecular workflow.

Practical Workflow for Genome-Resolved Metagenomics

The workflow from raw reads to assembled genomes follows a logical sequence. Each stage has specific tools and decision points. The following sections describe the pipeline in detail, with the reasoning behind each step and the common pitfalls to avoid.

Sample Collection and DNA Extraction

The quality of the DNA extraction determines the upper limit of the entire analysis. The goal is to obtain DNA that is representative of the community, free of inhibitors, and of sufficient quantity and quality for sequencing.

For bacterial communities, the extraction method must be able to lyse a wide range of cell types. Gram-positive bacteria have thick peptidoglycan layers that resist lysis. Gram-negative bacteria have an outer membrane that requires different treatment. Bead beating is a mechanical method that physically disrupts cells and is effective for most bacteria. Enzymatic digestion with lysozyme or mutanolysin can be added to improve lysis of difficult organisms. Commercial kits combine these approaches with purification steps to remove inhibitors.

The choice of extraction method affects the observed community composition. A method that lyses some cells better than others will bias the results. For example, a gentle lysis method may underrepresent gram-positive bacteria. The best approach is to use a method that has been validated for the sample type being studied and to document the method in the study records.

The DNA quantity and quality should be assessed before sequencing. A fluorometric assay is more accurate than a spectrophotometric assay for DNA quantification. The DNA integrity can be assessed by gel electrophoresis or an automated fragment analyzer. High-molecular-weight DNA is required for long-read sequencing, while short-read sequencing is more tolerant of fragmentation.

Sequencing Options and Depth

The choice of sequencing platform affects the assembly and binning results. Short-read sequencing, such as Illumina, produces reads of 150 to 300 base pairs with high accuracy. Short reads are well suited for assembly of most bacterial genomes, but they struggle with repetitive regions and can produce fragmented assemblies.

Long-read sequencing, such as Oxford Nanopore or PacBio, produces reads of thousands to tens of thousands of base pairs. Long reads span repetitive regions and produce more contiguous assemblies. The tradeoff is a higher error rate, which can be mitigated by hybrid assembly that combines long reads with short reads for error correction.

The sequencing depth is a critical decision. Depth is the average number of times each base in the genome is covered by reads. A higher depth improves the assembly of low-abundance organisms but increases the cost. For a simple community with a few dominant species, a depth of 10 to 20 times the genome size may be sufficient. For a complex community such as soil, a depth of 100 times or more may be needed to capture the rare biosphere.

The sequencing depth should be planned based on the research question. If the goal is to recover genomes of the dominant species, a lower depth is acceptable. If the goal is to recover genomes of rare species, a much higher depth is required. The depth is also affected by the amount of host DNA in the sample. A clinical sample with a high proportion of human DNA will require more sequencing to achieve the same bacterial depth.

Quality Control of Raw Reads

The raw sequencing data must be cleaned before assembly. The quality control step removes low-quality bases, adapter sequences, and contaminating DNA. This step is essential because low-quality reads can produce misassemblies and reduce the quality of the final genomes.

The first step is to assess the quality of the raw reads. FastQC is a widely used tool that provides a report on the quality scores, GC content, adapter content, and other metrics. The report should be examined for each sample to identify any issues.

The next step is to trim the reads. Trimming removes low-quality bases from the ends of the reads and removes adapter sequences. Trimmomatic is a popular tool that can perform both tasks. BBDuk is another option that is fast and can handle large datasets.

The reads should also be checked for contamination. The contamination can come from the host, from the reagents, or from the sequencing platform. The host contamination is a particular issue for clinical samples. The reads can be aligned to the host genome and the aligned reads removed. KneadData is a tool that performs this step.

The quality control step should be documented. The number of reads before and after trimming, the average quality score, and the number of reads removed should be recorded. This documentation is important for reproducibility and for troubleshooting.

Assembly of Metagenomic Reads

The assembly step reconstructs longer contiguous sequences, called contigs, from the overlapping reads. The assembly is a computationally intensive step that requires a specialized assembler designed for metagenomic data.

Metagenomic assemblers are designed to handle the complexity of a community with multiple organisms at different abundances. The assembler must be able to distinguish between the different organisms and assemble the reads into the correct contigs. The assembler must also be able to handle the uneven coverage that is typical of metagenomic data.

MEGAHIT is a fast and memory-efficient assembler that is well suited for large metagenomic datasets. metaSPAdes is another popular assembler that produces high-quality assemblies but requires more memory. metaFlye is a long-read assembler that is designed for the long-read data.

The choice of assembler depends on the data type and the computational resources. For short-read data, MEGAHIT and metaSPAdes are both good options. For long-read data, metaFlye is the preferred choice. The assembly can be improved by using a hybrid approach that combines short and long reads.

The assembly output is a set of contigs. The contigs vary in length, and the quality of the assembly is assessed by metrics such as the N50, which is the length of the contig at which 50 percent of the assembled bases are in contigs of that length or longer. A higher N50 indicates a more complete assembly.

Binning of Contigs into Genomes

The binning step groups the contigs into bins that represent individual genomes. The binning is based on the sequence composition and the coverage of the contigs. The sequence composition is the frequency of the nucleotides in the contig, which is similar for contigs from the same genome. The coverage is the number of reads that map to the contig, which is similar for contigs from the same genome.

The binning tools use these features to cluster the contigs. MetaBAT2 is a popular binning tool that uses the coverage and the composition. MaxBin2 is another tool that uses the same features. CONCOCT is a tool that uses the composition and the coverage, but it also uses the differential coverage across multiple samples.

The binning is an iterative process. The initial bins are often refined by using a tool such as DAS Tool, which combines the results of multiple binning tools to produce a more complete set of bins. The bins are then checked for quality.

The quality of the bins is assessed by two metrics: completeness and contamination. The completeness is the percentage of the genome that is present in the bin. The contamination is the percentage of the bin that comes from other genomes. The completeness and contamination are estimated by the presence of single-copy marker genes. A bin with a completeness of 90 percent and a contamination of 5 percent is considered a high-quality draft genome.

The quality of the bins is assessed using a tool such as CheckM. CheckM uses the single-copy marker genes to estimate the completeness and contamination. The bins that pass the quality thresholds are considered metagenome-assembled genomes, or MAGs.

Genome-Resolved Analysis

The final stage is the analysis of the MAGs. The analysis includes the taxonomic assignment, the functional annotation, and the comparative analysis.

The taxonomic assignment is the process of determining which organism the MAG represents. The taxonomy is assigned by comparing the MAG to a reference database. The Genome Taxonomy Database, or GTDB, is a widely used database that provides a standardized taxonomy for bacteria and archaea. GTDB-Tk is a tool that assigns the taxonomy using the GTDB.

The functional annotation is the process of identifying the genes in the MAG and assigning a function to each gene. The annotation is performed using a tool such as Prokka or Bakken. The annotation provides the gene names, the functional categories, and the metabolic pathways.

The comparative analysis is the process of comparing the MAGs to each other and to reference genomes. The comparison can be used to identify the differences between the strains, the presence of the genes of interest, and the evolutionary relationships.

The analysis of the MAGs is the final step in the pipeline. The results are used to answer the research question. The results are also used to generate the hypotheses for the future work.

Practical Implementation Steps

The following steps provide a practical guide for implementing the metagenomics pipeline in a laboratory setting.

Step 1: Define the Research Question and Sample Size

The first step is to define the research question. The question determines the experimental design, the sample size, and the sequencing depth. For example, a question about the effect of a treatment on the gut microbiome requires a different design than a question about the presence of a specific pathogen in a food sample.

The sample size is determined by the expected effect size and the variability of the community. A pilot study can be used to estimate the variability and to determine the sample size. The sample size should be large enough to detect the expected effect.

Step 2: Select the Sequencing Platform and Depth

The sequencing platform is selected based on the research question and the budget. The short-read sequencing is the most common and the most cost-effective for most applications. The long-read sequencing is used for the applications that require the complete assembly of the genomes.

The sequencing depth is determined by the complexity of the community and the research question. The depth should be sufficient to detect the organisms of interest.

Step 3: Perform the Quality Control and the Assembly

The quality control and the assembly are performed using the tools described above. The quality control is performed to remove the low-quality reads and the contamination. The assembly is performed to reconstruct the contigs.

The quality control and the assembly are the most computationally intensive steps. The steps require a computer with sufficient memory and processing power. The steps can be performed on a local server or on a cloud-based platform.

Step 4: Perform the Binning and the Quality Assessment

The binning is performed to group the contigs into the bins. The quality of the bins is assessed using the completeness and the contamination.

The binning is an iterative process. The bins are refined until the quality is acceptable. The bins that do not meet the quality thresholds are discarded.

Step 5: Perform the Genome-Resolved Analysis

The genome-resolved analysis is performed to assign the taxonomy, to annotate the genes, and to compare the genomes. The analysis is performed using the tools described above.

The analysis is the final step in the pipeline. The results are used to answer the research question.

Records and Measurements

The documentation of the pipeline is essential for the reproducibility and the quality of the results. The following records should be maintained.

Sample Metadata

The sample metadata should include the sample identifier, the sample type, the collection date, the collection location, and the storage conditions. The metadata should be recorded in a standardized format.

DNA Extraction Records

The DNA extraction records should include the extraction method, the kit used, the quantity of the DNA, and the quality of the DNA. The records should also include the lot number of the kit and the date of the extraction.

Sequencing Records

The sequencing records should include the sequencing platform, the sequencing depth, the library preparation method, and the date of the sequencing. The records should also include the number of reads and the quality of the reads.

Analysis Records

The analysis records should include the software versions, the parameters used, and the date of the analysis. The records should also include the output files and the quality metrics.

The records should be stored in a secure location and should be accessible to the research team. The records should be used to reproduce the analysis and to audit the results.

Common Failure Patterns and Troubleshooting

The metagenomics pipeline can fail at any stage. The following are the common failure patterns and the troubleshooting steps.

Low DNA Yield

A low DNA yield is a common problem. The yield can be low because of the poor lysis of the cells, the degradation of the DNA, or the loss of the DNA during the purification. The troubleshooting includes the use of a more aggressive lysis method, the use of a different extraction kit, and the use of a carrier to reduce the loss.

Poor Sequencing Quality

The poor sequencing quality can be caused by the low-quality DNA, the contamination, or the sequencing platform. The troubleshooting includes the re-extraction of the DNA, the re-purification of the DNA, and the re-sequencing of the library.

Fragmented Assembly

The fragmented assembly can be caused by the low sequencing depth, the high complexity of the community, or the presence of the repetitive regions. The troubleshooting includes the increase of the sequencing depth, the use of the long-read sequencing, and the use of a different assembler.

Poor Binning Quality

The poor binning quality can be caused by the low coverage of the contigs, the high similarity between the genomes, or the presence of the contamination. The troubleshooting includes the increase of the sequencing depth, the use of a different binning tool, and the use of the DASB tool to refine the bins.

Contamination

The contamination can be caused by the reagent, the environment, or the cross-contamination between the samples. The contamination can be detected by the use of the negative controls and the positive controls. The troubleshooting includes the use of the clean reagents, the use of the clean environment, and the use of the separate areas for the pre- and post-PCR.

Quality Controls and Standards

The quality controls are essential for the reliability of the results. The following controls should be included in the pipeline.

Positive Controls

The positive controls are the samples with a known composition. The positive controls are used to verify that the pipeline is working correctly. The positive controls can be a mock community with a known composition. The mock community can be a mixture of the DNA from the known bacteria. The American Type Culture Collection provides authenticated biological materials that can be used to create the mock communities.

Negative Controls

The negative controls are the samples that do not contain the DNA. The negative controls are used to detect the contamination. The negative control can be a sample of the extraction buffer or the water. The negative control should be processed through the entire pipeline.

Reference Standards

The reference standards are the samples with a known value. The reference standards are used to calibrate the pipeline. The reference standards can be a bacterial genome with a known sequence. The reference standard can be used to verify the assembly and the binning.

The quality controls should be included in the pipeline and the results should be reported. The quality controls are used to ensure the reliability of the results.

Biosafety and Laboratory Practice

The metagenomics pipeline involves the handling of the biological samples. The biosafety is an important consideration. The biosafety is the practice of the safe handling of the biological materials.

The biosafety is based on the risk assessment. The risk assessment is the process of the identification of the hazards and the evaluation of the risk. The risk assessment should be performed for each sample and each procedure.

The biosafety levels are the levels of the containment. The biosafety level 1 is the lowest level and the biosafety level 4 is the highest level. The biosafety level is determined by the risk of the sample.

The Biosafety in Microbiological and Biomedical Laboratories provides the guidelines for the biosafety. The guidelines include the use of the personal protective equipment, the use of the biosafety cabinet, and the use of the decontamination procedures.

The laboratory should follow the biosafety guidelines and the standard operating procedures. The laboratory should also be trained in the biosafety.

Antimicrobial Resistance and Genomic Surveillance

The metagenomics is a powerful tool for the surveillance of the antimicrobial resistance. The antimicrobial resistance is the ability of the bacteria to resist the effects of the antimicrobial drugs. The antimicrobial resistance is a global public health threat.

The World Health Organization defines the antimicrobial resistance as the resistance of the microorganisms to the antimicrobial drugs. The WHO has identified the antimicrobial resistance as a global health threat and has called for the surveillance of the antimicrobial resistance.

The metagenomics can be used to detect the antimicrobial resistance genes in the microbial community. The metagenomics can be used to identify the bacteria that carry the resistance genes. The metagenomics can also be used to track the spread of the resistance genes.

The metagenomics is a valuable tool for the surveillance of the antimicrobial resistance. The metagenomics can be used to monitor the resistance in the environment, the animals, and the humans. The metagenomics can also be used to detect the new resistance genes.

The antimicrobial susceptibility testing is the standard method for the detection of the resistance. The Clinical and Laboratory Standards Institute provides the standards for the antimicrobial susceptibility testing. The standards are used to determine the susceptibility of the bacteria to the drugs.

The metagenomics is a complementary tool to the antimicrobial susceptibility testing. The metagenomics can detect the resistance genes, while the susceptibility testing can determine the resistance phenotype. The combination of the two methods provides a more complete picture of the resistance.

Limitations and Evidence Gaps

The metagenomics has several limitations. The limitations should be considered when the interpreting the results.

The Inability to Distinguish Between the Live and the Dead Bacteria

The metagenomics sequences the DNA from the live and the dead bacteria. The DNA from the dead bacteria can be present in the sample and can be sequenced. The results can be biased by the DNA from the dead bacteria.

The Inability to Distinguish Between the Active and the Inactive Genes

The metagenomics sequences the DNA, but it does not measure the expression of the genes. The presence of a gene does not mean that the gene is expressed. The results can be biased by the presence of the inactive genes.

The Inability to Distinguish Between the Different Strains

The metagenomics can assemble the genomes of the different strains, but it can be difficult to distinguish between the strains. The strains can be very similar and can be assembled into the same bin.

The Inability to Detect the Low-Abundance Species

The metagenomics can fail to detect the low-abundance species. The low-abundance species may not be sequenced to a sufficient depth to be assembled. The low-abundance species may be missed in the results.

The Inability to Detect the RNA Viruses

The metagenomics sequences the DNA, but it does not sequence the RNA. The RNA viruses cannot be detected by the metagenomics.

The limitations should be considered when the interpreting the results. The limitations should be reported in the study.

Professional Escalation Criteria

The metagenomics pipeline can be complex. The following are the criteria for the escalation to a professional.

The Need for the Specialized Expertise

The metagenomics pipeline requires the specialized expertise in the bioinformatics. The laboratory may not have the expertise to perform the analysis. The laboratory should escalate to a bioinformatician or a core facility.

The Need for the Specialized Software

The metagenomics pipeline requires the specialized software. The software may not be available in the laboratory. The laboratory should escalate to a core facility or a cloud-based platform.

The Need for the Specialized Hardware

The metagenomics pipeline requires the specialized hardware. The hardware may not be available in the laboratory. The laboratory should escalate to a core facility or a cloud-based platform.

The Need for the Specialized Interpretation

The metagenomics pipeline produces the complex results. The results may require the specialized interpretation. The laboratory should escalate to a specialist in the field.

The escalation criteria are used to ensure the quality of the results. The escalation should be performed when the laboratory does not have the resources or the expertise.

Decision Framework for Selecting Metagenomic Tools and Parameters

The pipeline stages described above each offer multiple tool choices, but researchers often struggle with the practical question of which tool to select for a specific dataset and research objective. This section provides a structured decision framework that links experimental goals, data characteristics, and computational constraints to concrete tool and parameter choices. The framework is designed to be applied before the first read is processed, because tool selection affects downstream analysis quality and reproducibility.

Define the Decision Criteria Before Data Processing

The first step in the framework is to document the decision criteria before any analysis begins. This prevents the common failure pattern of selecting tools based on convenience or familiarity instead of data characteristics. The criteria should be recorded in the analysis records and include the following elements.

The research objective determines the required resolution. If the goal is to recover high-quality genomes of dominant community members, the analysis can prioritize assembly completeness and binning accuracy. If the goal is to detect low-abundance organisms or specific functional genes, the analysis must prioritize sequencing depth and sensitive read-based analysis instead of genome-resolved approaches.

The data type determines the assembler and binning strategy. Short-read data from Illumina platforms requires a different assembly approach than long-read data from Oxford Nanopore or PacBio. The error profile, read length, and coverage distribution all influence the choice of tools. The decision framework should record the sequencing platform, read length, and expected error rate for each dataset.

The computational resources available in the laboratory or through a core facility determine which tools are feasible. Some assemblers require substantial memory and processing time. The framework should include an honest assessment of available hardware, because a tool that exceeds the available memory will fail regardless of its theoretical suitability.

The sample complexity and expected community composition influence the binning strategy. A sample with many closely related strains requires a different approach than a sample with distantly related taxa. The expected abundance distribution, whether dominated by a few species or evenly distributed across many, affects the binning sensitivity.

Apply the Decision Framework by Analysis Stage

The framework is applied at each pipeline stage with a structured set of questions and decision rules. The following sections describe the decision points and the criteria for each choice.

Assembly Tool Selection

The assembly tool is selected based on the read type and the expected community complexity. For short-read data, the choice between MEGAHIT and metaSPAdes depends on the dataset size and the available memory. MEGAHIT is appropriate for large datasets where memory is limited and the community is complex. metaSPAdes is appropriate for smaller datasets where the additional memory requirement is acceptable and the assembly quality is the priority.

For long-read data, metaFlye is the primary choice. The decision framework should also consider whether a hybrid assembly is needed. A hybrid approach that combines long reads for scaffolding and short reads for error correction is appropriate when the long-read error rate is high and the short-read data are available.

The decision should be documented with the tool version, the parameters used, and the reason for the choice. This documentation is essential for reproducibility and for troubleshooting if the assembly fails.

Binning Tool Selection

The binning tool selection depends on the number of samples and the expected community structure. MetaBAT2 is a good default choice for single-sample binning because it is fast and produces high-quality bins for most datasets. MaxBin2 is an alternative that uses a different algorithm and can be used to cross-validate the results.

CONCOCT is appropriate when multiple samples are available and the differential coverage across samples can be used to improve the binning. The differential coverage approach is particularly useful for separating closely related strains that have different abundance patterns across the samples.

The decision framework should include a step for running multiple binning tools and combining the results with DAS Tool. This approach is more robust than relying on a single tool because different tools use different features and can recover different portions of the community. The combined result is a more complete set of bins.

Quality Threshold Selection

The quality thresholds for the MAGs should be defined before the binning is performed. The thresholds depend on the research question. A study that aims to describe the metabolic potential of a community may accept lower completeness thresholds. A study that aims to compare the genomes of specific strains should require higher completeness and lower contamination.

The standard thresholds for a high-quality draft genome are 90 percent completeness and 5 percent contamination. These thresholds are appropriate for most applications. The thresholds should be recorded in the analysis records and applied consistently across all samples.

The quality assessment should be performed with CheckM or a similar tool. The completeness and contamination estimates should be reported for each bin. The bins that do not meet the thresholds should be excluded from the downstream analysis or flagged as low quality.

Record the Decision Outcomes

The decision framework produces a set of records that are essential for the reproducibility and the interpretation of the results. The records should include the following items.

The decision log should record the research objective, the data type, the community characteristics, and the resource constraints. The log should also record the tool choices and the parameters for each stage of the pipeline.

The tool versions should be recorded for every software used in the analysis. The versions are important because the tools change over time and the results can vary between the versions.

The parameters should be recorded for every tool. The parameters include the quality thresholds, the k-mer sizes, the coverage cutoffs, and the other settings. The parameters should be recorded in a standardized format that can be used to reproduce the analysis.

The quality metrics should be recorded for every sample and every MAG. The metrics include the number of reads, the assembly N50, the completeness, and the contamination. The metrics are used to compare the samples and to identify the problems.

Common Failure Patterns in Tool Selection

The decision framework can fail when the criteria are not applied consistently. The following failure patterns are common and should be avoided.

The first failure pattern is selecting a tool based on the familiarity instead of the data type. A researcher who is familiar with MEGAHIT may use it for a long-read dataset, producing a poor assembly. The decision framework should be applied to the data type and the research objective.

The second failure pattern is using the default parameters without considering the data characteristics. The default parameters are designed for a typical dataset and may not be appropriate for a dataset with unusual characteristics. The parameters should be adjusted based on the data.

The third failure pattern is relying on a single binning tool. The single tool may miss the genomes that are recovered by another tool. The recommended approach is to run multiple tools and combine the results.

The fourth failure pattern is not documenting the decisions. The decisions are essential for the reproducibility and the interpretation of the results. The decisions should be recorded in the analysis records.

Professional Escalation Criteria for the Decision Framework

The decision framework can be applied by a researcher with the appropriate training. The following criteria indicate that the analysis should be escalated to a professional.

The first criterion is the need for the specialized expertise in the bioinformatics. The decision framework requires the knowledge of the tools and the parameters. A laboratory that does not have this expertise should escalate to a bioinformatician or a core facility.

The second criterion is the need for the specialized software. The tools described in the framework are not always available in the laboratory. The laboratory should escalate to a core facility or a cloud-based platform.

The third criterion is the need for the specialized interpretation. The results of the metagenomics pipeline require the interpretation by a specialist in the field. The laboratory should escalate to a specialist when the results are complex or when the research question requires the specialized knowledge.

The escalation criteria are used to ensure the quality of the results. The escalation should be performed when the laboratory does not have the resources or the expertise to apply the decision framework correctly.

Frequently Asked Questions

What is the difference between shotgun metagenomics and 16S amplicon sequencing?

Shotgun metagenomics sequences all DNA in a sample, providing both taxonomic and functional information. 16S amplicon sequencing targets a single gene and provides only taxonomic information. Shotgun metagenomics can assemble genomes and detect functional genes, while 16S sequencing cannot.

How much sequencing depth is needed for a metagenomics experiment?

The depth depends on the complexity of the community and the research question. A simple community with a few dominant species may require 10 to 30 million reads. A complex community such as soil may require 100 million reads or more. The depth should be sufficient to detect the organisms of interest.

What is a metagenome-assembled genome (MAG)?

A metagenome-assembled genome is a genome that is assembled from the metagenomic data. The MAG is a bin of contigs that represents a single organism. The MAG is a draft genome that can be used for the taxonomic and functional analysis.

How do I assess the quality of a metagenome-assembled genome?

The quality of a MAG is assessed using the completeness and the contamination. The completeness is the percentage of the genome that is present. The contamination is the percentage of the genome that is from other organisms. A high-quality MAG has a completeness of 90 percent or more and a contamination of 5 percent or less.

What is the best assembler for metagenomic data?

The best assembler depends on the data type. MEGAHIT is a fast and memory-efficient assembler for short-read data. metaSPAdes is a high-quality assembler for short-read data. metaFlye is a long-read assembler for long-read data. The choice of assembler depends on the sequencing platform and the research question.

How do I handle host contamination in my metagenomic data?

Host contamination is handled by aligning the reads to the host genome and removing the aligned reads. The tool KneadData can be used for this step. The host contamination is a common issue in clinical samples.

What is the role of the negative control in metagenomics?

The negative control is a sample that does not contain the DNA. The negative control is used to detect the contamination. The negative control should be included in the pipeline and the results should be reported.

How do I choose the right binning tool?

The choice of the binning tool depends on the data and the research question. MetaBAT2 is a popular tool that is fast and accurate. MaxBin2 is another tool that is accurate. CONCOKT is a tool that uses the clustering across multiple samples. The best approach is to use multiple tools and to combine the results with the DAS tool.

Using the Evidence

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References and Further Reading

• Biosafety in Microbiological and Biomedical Laboratories. CDC and NIH.
• Microbiology Resources. American Society for Microbiology.
• Antimicrobial Resistance. World Health Organization.
• Bacteriological Analytical Manual. U.S. Food and Drug Administration.
• Antimicrobial Susceptibility Testing. Clinical and Laboratory Standards Institute.
• Culture Collections. American Type Culture Collection.
• NCBI Pathogen Detection. National Center for Biotechnology Information.
• Deep learning methods in metagenomics: a review.. Microbial genomics, 2024.
• A practical guide to amplicon and metagenomic analysis of microbiome data.. Protein & cell, 2021.
• Microbial genomics amidst the Arctic crisis.. Microbial genomics, 2020.
• Impact of single-cell genomics and metagenomics on the emerging view of extremophile "microbial dark matter".. Extremophiles : life under extreme conditions, 2014.

This article is educational and does not replace validated laboratory procedures, clinical diagnosis, institutional biosafety review, or public-health guidance.