Evaluating Assembly Quality in Bacterial Genomes

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

Quick Answer

• Assess bacterial genome assembly quality using contiguity metrics like N50 and L50, completeness checks such as BUSCO, and contamination screening to determine fitness for downstream analysis.
• Establish minimum quality thresholds before assembly, then verify with reference-free and reference-based methods appropriate to your sequencing platform and research question.
• No single metric confirms assembly accuracy, and thresholds vary by sequencing technology, genome complexity, and intended application.

At a Glance

Understanding Assembly Quality in Bacterial Genomics

Bacterial genome assembly transforms raw sequencing reads into contiguous DNA sequences that represent the organism under study. The quality of that assembly determines whether downstream analyses such as gene annotation, antimicrobial resistance gene detection, phylogenetic placement, and comparative genomics produce reliable results. A poorly assembled genome can generate false gene truncations, missing genes, and incorrect structural conclusions.

The central problem for researchers is that assembly quality is not a single number. It is a combination of metrics that describe different aspects of the assembly. Contiguity metrics describe how long the assembled pieces are. Completeness metrics describe how much of the expected genome content is present. Accuracy metrics describe how many base-level errors exist. Contamination checks describe whether the assembly contains DNA from other organisms. Each metric answers a different question, and each has limitations.

Why Assembly Quality Matters for Downstream Analysis

The intended use of the assembly determines the quality standard you need. A draft assembly used for gene presence-absence screening may tolerate lower contiguity than an assembly used for structural variant analysis or for closing a genome. A clinical surveillance project tracking antimicrobial resistance genes requires accurate base calls in resistance determinants, because a single nucleotide error can change a resistance prediction. The World Health Organization describes antimicrobial resistance as a global public health threat, and genomic surveillance depends on assemblies that can support reliable gene detection.

The Relationship Between Sequencing Technology and Assembly Quality

Sequencing platform choice directly affects assembly quality. Short-read platforms produce reads of 150 to 300 bases with high per-base accuracy. These reads are assembled into contigs that are limited by repetitive regions. Long-read platforms produce reads of thousands to tens of thousands of bases, which can span repetitive elements and produce chromosome-level assemblies. Hybrid approaches combine both read types to achieve both accuracy and contiguity.

The quality thresholds you apply must reflect the technology used. A short-read assembly with an N50 of 100 kb may be excellent for that platform. A long-read assembly with the same N50 would be considered poor, because the platform should produce a single circular chromosome. Your quality assessment should compare your assembly to what is achievable with your sequencing approach, not to a universal standard.

Core Principles of Assembly Quality Assessment

Contiguity Metrics: N50 and L50

N50 is the most widely reported contiguity metric. It is defined as the length of the shortest contig in the set of contigs that together cover at least 50% of the total assembly length. If you order all contigs from longest to shortest and add their lengths until you reach half of the total assembly, the length of the last contig added is the N50. A higher N50 indicates a more contiguous assembly.

L50 is the number of contigs required to reach that 50% threshold. If the L50 is 1, a single contig covers half the assembly. If the L50 is 20, you need 20 contigs to cover half the assembly. The L50 is useful because it corrects a limitation of N50. A single large contig can inflate the N50 even when the rest of the assembly is highly fragmented. The L50 tells you how many contigs are actually contributing to that contiguity.

Related metrics include N90 and L90, which use the 90% threshold instead of 50%. These are more stringent because they require nearly the entire assembly to be represented in relatively few contigs. For bacterial genomes, N90 and L90 are often more informative than N50 because they capture the behavior of the smaller contigs.

Completeness Metrics

Completeness metrics assess whether the assembly contains the genes expected in a bacterial genome. The most widely used tool is BUSCO, which searches for a set of conserved single-copy orthologous genes that are expected to be present in nearly all genomes within a taxonomic group. For bacteria, the BUSCO dataset includes hundreds of genes that are conserved across the bacterial domain.

BUSCO reports three categories. Complete genes are present and match the expected length. Fragmented genes are present but only partially assembled. Missing genes are absent from the assembly. A high-quality bacterial assembly should have more than 95% complete BUSCO genes and fewer than 1 percent missing genes. Fragmented genes indicate that the assembly has breaks within conserved genes, which can happen when sequencing coverage is low or when the assembly algorithm fails to resolve a region.

Accuracy Metrics

Accuracy refers to the number of base-level errors in the assembly. These errors include substitutions, insertions, and deletions. Accuracy is more difficult to assess than contiguity or completeness because it requires a reference. For bacterial genomes, accuracy can be assessed by mapping reads back to the assembly and counting mismatches, or by comparing the assembly to a closely related reference genome.

The most common accuracy metric is the consensus quality value, which is a Phred-scaled score that estimates the probability of a base call being incorrect. A quality value of 30 corresponds to an error probability of 1 in 1,000. For bacterial genomes, a consensus quality of 40 or higher is often recommended for downstream applications that require high accuracy.

Contamination and Chimerism

Contamination occurs when DNA from another organism is present in the sequencing library. This can happen during sample collection, DNA extraction, or library preparation. Contamination can produce contigs that do not belong to the target genome, and it can also cause misassembly when the assembly algorithm joins reads from different organisms.

Chimerism occurs when the assembly algorithm incorrectly joins contigs from different regions of the genome or from different organisms. Chimeric contigs can produce false structural variants and false gene fusions. The most common method for detecting contamination is to compare the GC content and coverage of each contig to the expected values for the target organism. Contigs with unusual GC content or coverage are flagged for further investigation.

Practical Workflow for Assessing Assembly Quality

Step 1: Define Quality Thresholds Before Assembly

Set your quality thresholds before you begin assembly. The thresholds depend on your downstream application and your sequencing platform. For a draft genome intended for gene screening, you might accept an N50 of 20 kb and a BUSCO completeness of 90 percent. For a complete genome intended for structural analysis, you would require a single circular chromosome and a BUSCO completeness above 98 percent.

Document your thresholds in your laboratory notebook or electronic record. This documentation is important because it allows you to compare assemblies across samples and to justify your quality decisions in publications or public health reports.

Step 2: Run Assembly and Collect Raw Metrics

After assembly, collect the raw metrics from your assembler. Most assemblers report N50, L50, and the number of contigs. You can also use tools such as QUAST to calculate these metrics from the assembly file. Record the total assembly length, the number of contigs, the N50, the L50, and the GC content.

Compare the total assembly length to the expected genome size for your species. A bacterial genome that is significantly larger than expected may indicate contamination. A genome that is significantly smaller may indicate that the assembly is incomplete.

Step 3: Assess Completeness with BUSCO

Run BUSCO with the appropriate bacterial dataset. The BUSCO output will give you the percentage of complete, fragmented, and missing genes. A complete gene is one that is present in the assembly and matches the expected sequence. A fragmented gene is present but is broken into multiple pieces. A missing gene is absent.

Interpret the BUSCO results in the context of your sequencing platform. A short-read assembly may have more fragmented genes than a long-read assembly because short reads cannot span repetitive regions. If the BUSCO completeness is below your threshold, you may need to improve the assembly or collect additional data.

Step 4: Check for Contamination

Use coverage and GC content to check for contamination. Plot the GC content of each contig against its coverage. Contigs from the target organism should cluster together. Contigs with different GC content or coverage may be from a contaminant. You can also use a tool such as CheckM to estimate contamination and completeness from the assembly.

If you detect contamination, remove the contaminating contigs before proceeding. This is important because contamination can affect the BUSCO completeness and the downstream gene detection.

Step 5: Map Reads Back to the Assembly

Map the raw reads back to the assembly to assess accuracy. The mapping rate should be high, typically above 95 percent. Reads that do not map may indicate contamination or sequencing errors. The mapping also allows you to calculate the consensus quality score and to identify regions with low coverage.

Step 6: Compare to a Reference Genome

If a closely related reference genome is available, compare your assembly to the reference. This can identify structural differences and base-level errors. Use a tool such as MUMmer to align the assembly to the reference and to identify insertions, deletions, and substitutions.

Step 7: Document the Quality Assessment

Record the metrics in a standardized format. Include the assembly tool and version, the sequencing platform, the read depth, the N50, the L50, the BUSCO completeness, the contamination estimate, and the accuracy metrics. This documentation is important for reproducibility and for comparing assemblies across samples.

Records and Measurements

What to Record for Each Assembly

How to Use the Records

Use the records to compare assemblies across samples. If you are assembling multiple isolates from the same species, the quality metrics should be similar. A sample with a much lower N50 or a much higher contamination estimate may indicate a problem with the sample or the sequencing run.

Use the records to track improvements. If you change the assembly parameters or add additional sequencing data, the records will show whether the quality improved. This is important for optimizing the assembly workflow.

Common Failure Patterns

Low N50 with High BUSCO Completeness

This pattern indicates that the assembly is fragmented but contains most of the expected genes. It is common in short-read assemblies of genomes with many repetitive elements. The assembly may be usable for gene detection but not for structural analysis. If you need a more contiguous assembly, you may need to use long-read sequencing or a different assembly strategy.

High N50 with Low BUSCO Completeness

This pattern indicates that the assembly is contiguous but is missing genes. The missing genes may be in regions that were not sequenced or that were not assembled. This can happen when the read depth is too low or when the assembly algorithm fails to resolve a region. The assembly may be misleading because the contiguity suggests a high-quality assembly, but the missing genes indicate that the assembly is incomplete.

High Contamination Estimate

A contamination estimate above 5 percent indicates that the assembly contains DNA from other organisms. This can happen when the sample was contaminated during collection or when the sequencing library was contaminated. The contamination can produce false genes and false structural variants. You should remove the contamination before the downstream analysis.

Low Mapping Rate

A mapping rate below 95 percent indicates that many reads do not map to the assembly. This can be caused by contamination, sequencing errors, or a poor assembly. The unmapped reads may contain genes that are missing from the assembly. You should investigate the unmapped reads to determine the cause.

Limitations of Assembly Quality Metrics

N50 Does Not Measure Completeness

N50 measures contiguity, not completeness. An assembly with a high N50 can still be missing genes. The N50 only tells you that the contigs are long, not that they cover the entire genome. You must use a completeness metric such as BUSCO to assess whether the assembly contains the expected genes.

BUSCO is a Proxy for Completeness

BUSCO measures the presence of a set of conserved genes. It does not measure the presence of genes that are unique to the target species. A genome can have a high BUSCO completeness and still be missing species-specific genes. The BUSCO completeness is a useful proxy, but it is not a complete measure of genome completeness.

Accuracy Metrics Depend on the Reference

Accuracy metrics that compare the assembly to a reference genome are limited by the quality of the reference. If the reference has errors, the accuracy metrics will be incorrect. The accuracy metrics also depend on the evolutionary distance between the target and the reference. A distant reference will produce more apparent differences, which may be true differences instead of assembly errors.

Quality Thresholds Are Context-Dependent

There is no universal quality threshold for bacterial genomes. The thresholds depend on the sequencing platform, the genome complexity, and the intended application. A threshold that is appropriate for a simple genome with short-read sequencing may not be appropriate for a complex genome with long-read sequencing. You must define the thresholds in the context of your project.

Biosafety and Laboratory Context

Sample Handling and Biosafety

The quality of the assembly depends on the quality of the DNA. Contamination can be introduced during sample collection, DNA extraction, or library preparation. Follow the biosafety principles described in the Biosafety in Microbiological and Biomedical Laboratories to prevent contamination and to ensure the safety of the laboratory personnel. The BMBL provides guidance on risk assessment, containment, and decontamination for work with microorganisms.

Use of Authenticated Strains

The use of authenticated strains is important for the quality of the assembly. The American Type Culture Collection provides authenticated biological materials that can be used as references for quality control. The use of authenticated strains ensures that the reference genome is correct and that the assembly can be compared to a known standard.

Laboratory Practice and Quality

The American Society for Microbiology provides professional education and laboratory practice resources for microbiology. The ASM resources can be used to ensure that the laboratory practices are consistent with professional standards. The quality of the assembly depends on the quality of the laboratory practices, including the DNA extraction, the library preparation, and the sequencing.

Food Microbiology Context

The Bacteriological Analytical Manual from the U.S. Food and Drug Administration provides official methods for food microbiology. The BAM methods are relevant when the bacterial genome assembly is used for food safety applications. The quality of the assembly must be sufficient to support the official methods.

Professional Escalation Criteria

When to Seek Additional Expertise

You should seek additional expertise when the assembly quality does not meet your thresholds and you cannot identify the cause. This may include a bioinformatician, a genomics specialist, or a laboratory supervisor. The additional expertise can help you identify the cause of the quality problem and determine the appropriate corrective action.

When to Repeat the Assembly

You should repeat the assembly when the quality metrics are below the thresholds and the cause is likely to be the assembly parameters. You may need to change the assembler, the parameters, or the sequencing data. The repeated assembly should be assessed with the same quality metrics.

When to Collect Additional Data

You should collect additional data when the quality metrics are below the thresholds and the cause is likely to be insufficient sequencing. This may include additional sequencing reads, long-read sequencing, or a different sequencing platform. The additional data should be used to improve the assembly.

When to Report the Quality Problem

You should report the quality problem when the assembly is used for a public health or clinical application. The quality problem should be reported to the appropriate authority, such as the laboratory supervisor or the public health agency. The report should include the quality metrics and the cause of the problem.

Decision Framework for Assembly Acceptance and Rejection

Quality metrics become useful only when they feed into a clear decision about whether to proceed with downstream analysis, repeat the assembly, or collect additional data. Many researchers collect N50, BUSCO, and contamination values but stop short of translating those numbers into a documented go or no-go decision. A structured decision framework removes ambiguity and ensures that every assembly is evaluated against the same criteria.

Building a Two Tier Quality Gate System

A two tier quality gate system separates the assessment into an initial screening pass and a deeper verification pass. The initial screening pass uses fast, reference-free metrics to identify assemblies that are clearly unsuitable. The deeper verification pass applies more stringent checks to assemblies that pass the initial screen. This structure prevents wasted effort on assemblies that fail obvious quality checks while ensuring that borderline assemblies receive thorough evaluation.

The initial screening pass should include four checks. First, confirm that the total assembly length falls within a plausible range for the target species. A genome that is 30 percent larger than the expected size may contain contamination. A genome that is 30 percent smaller may be missing a replicon or a large genomic island. Second, verify that the number of contigs is within the expected range for your sequencing platform. Third, confirm that the BUSCO completeness exceeds the minimum threshold you defined before assembly. Fourth, verify that the contamination estimate from a tool such as CheckM is below 5 percent.

Assemblies that pass all four screening checks move to the verification pass. The verification pass adds three checks. First, map the raw reads back to the assembly and confirm that the mapping rate exceeds 95 percent. Second, examine the coverage distribution across the assembly. A uniform coverage distribution with no extreme peaks or valleys indicates a balanced assembly. Third, compare the assembly to a closely related reference genome if one is available. The comparison should identify any large structural discrepancies that would affect downstream analysis.

Assigning Quality Categories to Assemblies

Each assembly receives one of three quality categories after the two pass evaluation. The categories are pass, conditional pass, and fail. A pass category means the assembly meets all thresholds and is suitable for the intended downstream analysis. A conditional pass means the assembly meets the screening thresholds but has a specific limitation that must be documented and considered in the downstream analysis. A fail category means the assembly does not meet the thresholds and must be improved or repeated.

The conditional pass category is important because it acknowledges that some assemblies are usable despite a known limitation. For example, an assembly with a BUSCO completeness of 93 percent may be acceptable for a gene presence-absence screen if the missing genes are not relevant to the research question. The conditional pass requires you to document the limitation and to explain why the assembly remains suitable for the intended use. This documentation is essential for reproducibility and for defending the assembly in a publication or public health report.

The fail category triggers a corrective action. The corrective action depends on the cause of the failure. If the assembly fails because of low contiguity, you may need to add long-read sequencing data. If the assembly fails because of contamination, you may need to repeat the DNA extraction or the library preparation. If the assembly fails because of low completeness, you may need to increase the sequencing depth. The corrective action should be documented with the quality metrics that prompted it.

Building a Decision Matrix for Common Scenarios

A decision matrix helps you apply the quality categories consistently across samples. The matrix lists the common quality metric combinations and the recommended action for each combination. The matrix should be specific to your sequencing platform and your downstream application.

For a short-read assembly intended for gene detection, the matrix might look like this. An N50 above 20 kb with a BUSCO completeness above 95 percent and a contamination estimate below 5 percent receives a pass. An N50 between 10 and 20 kb with a BUSCO completeness above 95 percent receives a conditional pass, because the contiguity is lower than ideal but the gene content is complete. An N50 below 10 kb or a BUSCO completeness below 90 percent receives a fail.

For a long-read assembly intended for a complete genome, the matrix is more stringent. A single circular chromosome with a BUSCO completeness above 98 percent and a contamination estimate below 1 percent receives a pass. A single circular chromosome with a BUSCO completeness between 95 and 98 percent receives a conditional pass. A linear assembly or a BUSCO completeness below 95 percent receives a fail.

The decision matrix should be written down and shared with the laboratory team. This ensures that all researchers apply the same criteria to their assemblies. The matrix also provides a record of the quality standards used in the project, which is valuable for publications and for public health reporting.

Using the Decision Framework with Different Sequencing Platforms

The decision framework must be adapted to the sequencing platform. A short-read assembly and a long-read assembly have different expected quality profiles, and the decision thresholds must reflect those differences. The framework should be calibrated using the quality metrics that are achievable with your platform.

For short-read assemblies, the decision framework should focus on completeness and contamination because these are the metrics that are most reliable for this platform. Contiguity is expected to be lower, and the decision should not penalize an assembly for having a moderate N50 if the completeness and contamination are acceptable. The conditional pass category is particularly useful for short-read assemblies because it allows you to document the contiguity limitation while proceeding with the downstream analysis.

For long-read assemblies, the decision framework should focus on the circularity and the completeness. A long-read assembly that does not produce a single circular chromosome should be investigated, because the platform is expected to resolve the entire genome. The decision framework should also include a check for structural errors, because long-read assemblies can contain misjoins that are not detected by the standard metrics.

Handling Borderline Assemblies

Borderline assemblies are those that fall between the pass and fail thresholds. A BUSCO completeness of 93 percent when the threshold is 95 percent is a borderline case. A contamination estimate of 4.8 percent when the threshold is 5 percent is a borderline case. The decision framework should include a procedure for handling borderline assemblies.

The procedure should begin with a review of the raw metrics. Confirm that the metrics were calculated correctly and that the assembly file is the correct one. Then examine the specific regions that caused the borderline result. If the BUSCO completeness is borderline, identify which genes are missing or fragmented. If the contamination estimate is borderline, identify which contigs are flagged as contamination. This examination often reveals whether the borderline result is a true problem or a technical artifact.

If the borderline result is a true problem, the assembly should receive a conditional pass with a documented limitation. If the borderline result is a technical artifact, the assembly should receive a pass. The decision should be recorded with the reason for the decision. This record is important for the reproducibility of the assembly and for the interpretation of the downstream analysis.

Implementing the Decision Framework in a Laboratory Workflow

The decision framework should be integrated into the laboratory workflow so that every assembly is evaluated in the same way. The workflow should include a quality assessment step after the assembly and before the downstream analysis. The quality assessment step should use the decision matrix and the two tier gate system.

The workflow should also include a documentation step. The documentation should include the quality metrics, the decision category, and the reason for the decision. The documentation should be stored with the assembly files and the sequencing data. This documentation is important for the reproducibility of the analysis and for the audit of the laboratory practices.

The American Society for Microbiology provides professional education and laboratory practice resources that can help you integrate the decision framework into your workflow. The ASM resources emphasize the importance of standardized laboratory practices and the documentation of quality control. The Bacteriological Analytical Manual from the U.S. Food and Drug Administration also provides a model for the documentation of quality in food microbiology applications.

Using the Decision Framework for Public Health and Clinical Applications

The decision framework is particularly important for public health and clinical applications, where the assembly quality affects the reliability of the results. The World Health Organization describes antimicrobial resistance as a global public health threat, and genomic surveillance depends on assemblies that can support reliable gene detection. The decision framework ensures that the assemblies used for surveillance meet the quality standards required for the analysis.

For public health applications, the decision framework should include a stricter threshold for contamination. A contamination estimate above 1 percent may be unacceptable for a clinical sample, because the contamination can produce false resistance gene detections. The decision framework should also include a stricter threshold for completeness, because a missing resistance gene can lead to a false negative result.

The decision framework should also include a reporting step for public health applications. The quality metrics and the decision category should be reported with the assembly and the downstream analysis. This reporting is important for the interpretation of the results and for the comparison of results across laboratories.

The Role of the Decision Framework in Troubleshooting

The decision framework is also a troubleshooting tool. When an assembly fails, the framework helps you identify the cause of the failure. The framework directs you to the specific metric that failed and to the corrective action that is most likely to resolve the problem.

For example, if the assembly fails because of a low BUSCO completeness, the framework directs you to examine the sequencing depth and the assembly parameters. If the assembly fails because of a high contamination estimate, the framework directs you to examine the DNA extraction and the library preparation. The framework prevents you from applying a general corrective action that may not address the specific cause of the failure.

The troubleshooting process should be documented. The documentation should include the failed metric, the suspected cause, the corrective action, and the result of the corrective action. This documentation is valuable for the optimization of the assembly workflow and for the training of new laboratory personnel.

The Decision Framework as a Communication Tool

The decision framework is also a communication tool. It provides a common language for discussing assembly quality across the laboratory team, with collaborators, and with the public health authorities. The framework allows you to communicate the quality of an assembly in a standardized way, without relying on subjective judgment.

The framework also provides a basis for comparing assemblies across samples and across projects. If the same framework is used for all assemblies, the quality metrics can be compared directly. This comparison is important for the identification of samples that are outliers and for the optimization of the assembly workflow.

The framework should be shared with the laboratory team and with the collaborators. The framework should be included in the laboratory documentation and in the project reports. The framework should be updated as the laboratory practices and the sequencing technologies evolve.

Common Failure Patterns in the Decision Framework

The Assembly Passes the Screening but Fails the Verification

This pattern indicates that the assembly appears to be good based on the initial metrics but has a problem that is detected by the deeper checks. The most common cause is a low mapping rate, which indicates that many reads do not map to the assembly. The low mapping rate can be caused by contamination, sequencing errors, or a misassembly. The corrective action is to investigate the unmapped reads and to determine the cause.

The Assembly Fails the Screening but the Cause Is Not Clear

This pattern indicates that the assembly does not meet the thresholds, but the cause is not obvious from the metrics. The corrective action is to examine the assembly in more detail. The examination should include the coverage distribution, the GC content distribution, and the alignment to a reference genome. The examination should identify the specific regions that are causing the failure.

The Assembly Passes All Checks but the Downstream Analysis Produces Unexpected Results

This pattern indicates that the assembly quality is good, but the downstream analysis produces results that are inconsistent with the expected biology. The corrective action is to examine the downstream analysis and to confirm that the analysis is appropriate for the assembly. The analysis may need to be repeated with different parameters or with a different tool.

The Assembly Quality Varies Across Samples

This pattern indicates that the assembly quality is not consistent across samples. The corrective action is to examine the sample preparation and the sequencing runs. The variation may be caused by differences in the DNA quality, the sequencing depth, or the assembly parameters. The corrective action is to standardize the sample preparation and the sequencing protocols.

Professional Escalation Criteria for the Decision Framework

When to Seek Additional Expertise

You should seek additional expertise when the assembly fails the decision framework and you cannot identify the cause. The additional expertise may include a bioinformatician, a genomics specialist, or a laboratory supervisor. The additional expertise can help you identify the cause of the failure and determine the appropriate corrective action.

When to Repeat the Assembly

You should repeat the assembly when the assembly fails the decision framework and the cause is likely to be the assembly parameters. You may need to change the assembler, the parameters, or the sequencing data. The repeated assembly should be assessed with the same decision framework.

When to Collect Additional Data

You should collect additional data when the assembly fails the decision framework and the cause is likely to be insufficient sequencing. This may include additional sequencing reads, long-read sequencing, or a different sequencing platform. The additional data should be used to improve the assembly.

When to Report the Quality Problem

You should report the quality problem when the assembly is used for a public health or clinical application. The quality problem should be reported to the appropriate authority, such as the laboratory supervisor or the public health agency. The report should include the quality metrics and the cause of the problem.

Frequently Asked Questions

What is the difference between N50 and L50?

N50 is the length of the shortest contig in the set of contigs that together cover at least 50 percent of the assembly. L50 is the number of contigs required to reach that 50 percent threshold. A high N50 with a high L50 indicates that the contiguity is driven by a few large contigs, while a high N50 with a low L50 indicates that the contiguity is distributed across many contigs.

How do I interpret BUSCO completeness for a bacterial genome?

BUSCO completeness is the percentage of conserved single-copy genes that are present in the assembly. A complete gene is present and matches the expected sequence. A fragmented gene is present but is partially assembled. A missing gene is absent. For a bacterial genome, a BUSCO completeness above 95 percent is considered good, and a completeness below 90 percent indicates a problem.

What is the recommended N50 for a bacterial genome assembly?

The recommended N50 depends on the sequencing platform and the intended application. For a short-read assembly, an N50 of 20 to 50 kb is typical. For a long-read assembly, an N50 of 1 Mb or higher is expected. The N50 should be interpreted in the context of the sequencing platform and the genome complexity.

How do I detect contamination in a bacterial genome assembly?

Contamination can be detected by comparing the GC content and coverage of each contig to the expected values for the target organism. Contigs with unusual GC content or coverage may be from a contaminant. You can also use a tool such as CheckM to estimate the contamination. A contamination estimate above 5 percent indicates a problem.

What is the difference between a draft and a complete bacterial genome?

A draft genome is an assembly that is not complete. It may contain multiple contigs and may be missing some genes. A complete genome is an assembly that is a single circular chromosome with no gaps. The complete genome is the highest quality assembly and is required for some downstream applications.

How do I improve the quality of a bacterial genome assembly?

The quality of the assembly can be improved by increasing the sequencing depth, using long-read sequencing, or using a different assembler. The quality can also be improved by removing contamination and by using a reference genome to guide the assembly. The improvement should be assessed with the same quality metrics.

What is the role of the reference genome in assembly quality?

A reference genome can be used to assess the accuracy of the assembly. The assembly can be aligned to the reference to identify insertions, deletions, and base-level errors. The reference can also be used to guide the assembly and to improve the contiguity. The reference should be closely related to the target genome.

How do I report the quality of a bacterial genome assembly?

The quality of the assembly should be reported in a standardized format. The report should include the assembly tool and version, the sequencing platform, the read depth, the N50, the L50, the BUSCO completeness, the contamination estimate, and the accuracy metrics. The report should also include the thresholds that were used to assess the quality.

Related Bacteriology Guides

• Bacterial Contamination of Poultry: Salmonella and E. coli
• Bacterial Diseases of Chickens: A Comprehensive Overview
• Bacterial Growth Dynamics in Chicken: From Farm to Refrigeration
• Bacterial Pathogens in Poultry: Comprehensive Clinical Reference
• Bacterial Poultry Diseases: Comprehensive Overview and Classification

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.
• Assembly of long, error-prone reads using repeat graphs.. Nature biotechnology, 2019.
• Evaluating whole-genome sequencing quality metrics for enteric pathogen outbreaks.. PeerJ, 2021.
• Assessing genome assembly quality using the LTR Assembly Index (LAI).. Nucleic acids research, 2018.
• Genome assembly quality: assessment and improvement using the neutral indel model.. Genome research, 2010.

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