How to Use BV-BRC for Comparative Microbial Genomics

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

Quick Answer

BV-BRC is a free bacterial genomics platform that integrates genome sequences, annotations, and comparative analysis tools for researchers studying microbial pathogens and related organisms.
Start by searching for a genome of interest using the organism name or assembly accession, then use the Tree and Comparison tools to build phylogenetic trees and identify shared or unique genes.
The platform requires familiarity with bioinformatics concepts such as genome assembly quality, annotation, and phylogenetic inference, and results should be validated with laboratory methods before drawing clinical conclusions.

At a Glance

BV-BRC Feature	Primary Use	Input Required	Typical Output
Genome Browser	Visualize gene neighborhoods, annotations, and sequence features	Genome accession or organism name	Annotated genome view with gene coordinates and functional assignments
Treeview	Build and visualize phylogenetic trees from genome or protein sequences	Multiple genome accessions or sequence data	Phylogenetic tree with branch lengths and bootstrap support
Comparative Analysis	Identify shared and unique genes across selected genomes	Two or more genomes	Gene presence/absence tables and functional category summaries
Antimicrobial Resistance (AMR) Finder	Detect resistance genes in assembled genomes	Assembled contigs or genome accession	List of predicted AMR genes with associated annotations
Subspecies Typing	Assign sequence types or serotypes	Genome assembly	MLST or serotype prediction results

Understanding BV-BRC and Its Role in Microbial Genomics

The Bacterial and Viral Bioinformatics Resource Center, commonly called BV-BRC, is a public bioinformatics platform that combines genome sequence data, functional annotations, and comparative analysis tools. It was developed to support researchers who study bacterial pathogens, microbial ecology, and antimicrobial resistance. The platform is maintained by the National Institute of Allergy and Infectious Diseases and is freely accessible to the research community.

For laboratory professionals and biology students, BV-BRC serves as a central hub for storing, retrieving, and analyzing bacterial genomes. It integrates data from public repositories such as GenBank and RefSeq, and it provides a consistent annotation pipeline that allows users to compare genomes without needing to run their own annotation software. This is particularly useful for laboratories that do not have dedicated bioinformatics staff or high-performance computing resources.

The platform is designed to address a common problem in microbial genomics: the need to compare multiple genomes to understand evolutionary relationships, identify genes associated with virulence or resistance, and track the spread of pathogens. Without a unified platform, researchers would need to download raw sequence data, run separate annotation tools, and manually manage multiple files. BV-BRC simplifies this workflow by providing a single interface for genome retrieval, annotation, and comparative analysis.

The platform also supports the broader public health goal of antimicrobial resistance surveillance. The World Health Organization identifies antimicrobial resistance as a global health threat that requires coordinated surveillance and stewardship efforts. BV-BRC contributes to this effort by allowing researchers to screen genomes for resistance genes and compare them across isolates from different regions or time periods. This type of analysis is essential for understanding how resistance spreads and for informing public health interventions.

Core Principles of Comparative Microbial Genomics

Comparative microbial genomics is the study of differences and similarities between bacterial genomes. The core principle is that by comparing multiple genomes, researchers can identify genes that are conserved across a species, genes that are unique to specific strains, and genetic changes that may be associated with phenotypic traits such as virulence or antimicrobial resistance.

The first principle is the need for high-quality genome assemblies. The accuracy of any comparative analysis depends on the quality of the input data. Poorly assembled genomes can contain errors that lead to incorrect gene predictions or false differences between strains. BV-BRC provides quality metrics for each genome, including the number of contigs, the total length, and the N50 value, which is a measure of assembly continuity. Researchers should review these metrics before including a genome in a comparative analysis.

The second principle is the importance of consistent annotation. Gene annotation is the process of identifying the location and function of genes within a genome. Different annotation pipelines can produce different results, which makes it difficult to compare genomes that were annotated with different methods. BV-BRC uses a standardized annotation pipeline, which ensures that all genomes in the platform are annotated in a consistent manner. This consistency is essential for comparing gene content across multiple genomes.

The third principle is the use of appropriate phylogenetic methods. Phylogenetic analysis is used to infer the evolutionary relationships between genomes. The choice of method can affect the results, and researchers should understand the assumptions and limitations of each method. BV-BRC provides several phylogenetic tools, including the ability to build trees based on whole-genome sequences or on specific genes such as the 16S rRNA gene. The choice of method depends on the research question and the number of genomes being compared.

The fourth principle is the need for validation. Comparative genomics is a powerful tool, but it is not a substitute for laboratory experiments. Predictions about gene function or resistance should be validated using laboratory methods such as antimicrobial susceptibility testing. The Clinical and Laboratory Standards Institute provides standards for antimicrobial susceptibility testing, and these standards should be followed when validating resistance predictions. The results of a comparative analysis should be considered as hypotheses that need to be tested in the laboratory.

Getting Started with BV-BRC

Creating an Account and Navigating the Interface

BV-BRC is a public platform that does not require a login for basic searches and browsing. However, creating a free account allows users to save genomes, build private groups, and store analysis results. The account creation process is straightforward and requires only an email address and a password.

The main interface of BV-BRC is organized into several sections. The top navigation bar provides access to the main tools, including the search function, the genome browser, and the analysis tools. The home page displays a search box where users can enter a genome name, an accession number, or a taxonomic group. The platform also provides a set of quick links to common tasks, such as building a phylogenetic tree or comparing genomes.

The search function is the primary entry point for most analyses. Users can search for a specific organism by name, such as "Escherichia coli," or by a specific strain, such as "E. coli O157:H7." The search results will display a list of genomes that match the query, along with information about each genome, including the assembly quality, the number of genes, and the source of the sequence data.

Searching for Genomes

The search function in BV-BRC is designed to be flexible and to accommodate different types of queries. Users can search by organism name, by a specific strain, or by a sequence accession number. The search results are displayed in a table that can be sorted by various columns, such as the genome name, the assembly quality, or the date of the last update.

For example, a researcher interested in a specific strain of a pathogen can enter the strain name in the search box. The results will show all genomes that match that strain name, along with the assembly quality and the source of the sequence. The researcher can then select the genomes of interest and add them to a group for further analysis.

The search function also supports more advanced queries. Users can search for genomes that contain a specific gene, such as a gene that encodes a resistance protein. This is useful for identifying genomes that may be resistant to a particular antibiotic. The search can be refined by specifying the gene name or the protein function.

Understanding Genome Quality and Selection

The quality of a genome assembly is a critical factor in comparative genomics. A genome that is fragmented into many small contigs may contain errors or missing genes, which can affect the results of a comparative analysis. BV-BRC provides several quality metrics for each genome, including the number of contigs, the total length of the assembly, and the N50 value, which is a measure of the contiguity of the assembly.

The N50 value is the length of the shortest contig that, when combined with all longer contigs, covers at least 50 percent of the total assembly length. A higher N50 value indicates a more contiguous assembly, which is generally considered to be of higher quality. Researchers should select genomes with high N50 values and a low number of contigs for their comparative analyses.

The platform also provides a quality score for each genome, which is a composite measure of the assembly quality. The score is based on several factors, including the number of contigs, the presence of a complete set of core genes, and the absence of contamination. Researchers should use this score to filter out genomes that are of low quality.

Building a Genome Group

A genome group is a collection of genomes that you want to analyze together. BV-BRC allows users to create groups of genomes and save them for later use. This is useful for comparative analysis, as it allows you to select a set of genomes and then run multiple analyses on the same set.

To create a group, users can select the genomes of interest from a search result and then click the "Add to Group" button. The group can be named and saved to the user's account. Once a group is created, it can be used as the input for the phylogenetic tree tool, the comparative analysis tool, or the AMR finder.

The group can also be shared with other users, which is useful for collaborative projects. The sharing feature allows other users to view and use the group, but it does not allow them to modify the group unless they are given permission.

Building a Phylogenetic Tree

Selecting Genomes for the Tree

A phylogenetic tree is a diagram that shows the evolutionary relationships between a set of organisms. In BV-BRC, the phylogenetic tree is built using the Treeview tool. The first step is to select the genomes that will be included in the tree. The genomes can be selected from a search result or from a saved group.

The number of genomes that can be included in a tree is limited by the computational resources of the platform. For a large number of genomes, the tree may take a long time to build. The platform provides a progress indicator that shows the status of the tree-building process.

The choice of genomes is important. The tree should include a set of genomes that are representative of the diversity of the group. For example, if the goal is to study the evolution of a specific species, the tree should include genomes from different geographic regions and different time periods. The tree should also include an outgroup, which is a genome that is known to be distantly related to the group of interest. The outgroup is used to root the tree and to provide a reference point for the evolutionary relationships.

Building the Tree

The tree-building process in BV-BRC uses a method called the whole-genome alignment. The method aligns the genomes and then uses the alignment to infer the evolutionary relationships. The platform provides several options for the tree-building method, including the maximum-likelihood method and the neighbor-joining method.

The maximum-likelihood method is a statistical method that is considered to be more accurate than the neighbor-joining method. However, it is also more computationally intensive. The neighbor-joining method is a faster method that is often used for a large number of genomes. The choice of method depends on the number of genomes and the computational resources available.

The platform also provides an option to build a tree based on a specific gene, such as the 16S rRNA gene. This method is useful for the identification of species, as the 16S rRNA gene is highly conserved and is used for the classification of bacteria. However, the 16S rRNA gene may not provide enough resolution to distinguish between closely related strains.

Interpreting the Tree

The phylogenetic tree is displayed as a diagram with branches that connect the genomes. The length of the branches represents the amount of genetic change that has occurred. The tree also shows the support values for each branch, which indicate the confidence in the grouping.

The support values are typically expressed as a percentage. A support value of 100 percent indicates that the grouping is strongly supported by the data. A support value of less than 70 percent indicates that the grouping is not well supported and should be interpreted with caution.

The tree can be used to identify the evolutionary relationships between the genomes. The genomes that are closely related will be grouped together on the tree. The tree can also be used to identify the most recent common ancestor of a group of genomes.

Comparative Analysis Tools

The Comparative Analysis Tool

The comparative analysis tool in BV-BRC is used to compare the gene content of multiple genomes. The tool identifies the genes that are present in all of the genomes, the genes that are present in only some of the genomes, and the genes that are unique to a single genome.

The tool provides a table that shows the presence or absence of each gene in each genome. The table can be sorted by the gene name or by the number of genomes that contain the gene. The tool also provides a Venn diagram that shows the overlap of genes between the genomes.

The comparative analysis tool is useful for identifying genes that are associated with a specific phenotype. For example, if a set of genomes is known to be resistant to a specific antibiotic, the tool can be used to identify the genes that are present in the resistant genomes but absent in the susceptible genomes.

The AMR Finder

The AMR Finder is a tool that is used to identify antimicrobial resistance genes in a genome. The tool uses a database of known resistance genes and compares the genome sequence to the database. The tool provides a list of the resistance genes that are found in the genome, along with the protein that is associated with each gene.

The AMR Finder is a useful tool for the surveillance of antimicrobial resistance. The World Health Organization has identified antimicrobial resistance as a global health threat, and the surveillance of resistance genes is an important part of the response. The AMR Finder can be used to screen a large number of genomes for resistance genes, which can help to identify the spread of resistance.

The results of the AMR Finder should be validated using laboratory methods. The Clinical and Laboratory Standards Institute provides standards for antimicrobial susceptibility testing, and these standards should be used to confirm the resistance phenotype. The presence of a resistance gene does not always mean that the organism is resistant to the antibiotic, as the gene may not be expressed or may not be functional.

The Subspecies Finder

The Subspecies Finder is a tool that is used to identify the subspecies or sequence type of a genome. The tool uses a database of known sequence types and compares the genome sequence to the database. The tool provides a list of the sequence types that are found in the genome.

The Subspecies Finder is useful for the identification of the strain type of a bacterial isolate. The sequence type is a measure of the genetic diversity of a species, and it is used for the surveillance of pathogens. The tool can be used to identify the sequence type of a genome and to compare it to the sequence types of other genomes.

Practical Workflow for a Comparative Study

Step 1: Define the Research Question

The first step in a comparative study is to define the research question. The question should be specific and should be answerable with the data that is available. For example, the question could be "What genes are present in the genomes of a specific strain of a pathogen that are not present in the genomes of a non-pathogenic strain?" or "What is the evolutionary relationship between the genomes of a specific species?"

The research question will determine the type of analysis that is performed. If the question is about the gene content, the comparative analysis tool will be used. If the question is about the evolutionary relationships, the phylogenetic tree tool will be used.

Step 2: Select the Genomes

The next step is to select the genomes that will be included in the analysis. The genomes should be selected based on the research question. The genomes should be representative of the diversity of the group, and they should be of high quality.

The genomes can be selected from the search results or from a saved group. The genomes should be checked for quality before they are included in the analysis. The quality metrics, such as the N50 value and the number of contigs, should be reviewed.

Step 3: Run the Analysis

The analysis is run using the appropriate tool. The tool will be selected based on the research question. The analysis may take some time to complete, depending on the number of genomes and the complexity of the analysis.

The results of the analysis should be reviewed and interpreted. The results should be checked for any errors or inconsistencies. The results should be compared to the known information about the genomes.

Step 4: Validate the Results

The results of the analysis should be validated using laboratory methods. The validation is important to confirm the predictions that are made by the analysis. The validation may involve the use of laboratory methods such as antimicrobial susceptibility testing or the polymerase chain reaction (PCR).

The validation should be performed using the standards that are provided by the Clinical and Laboratory Standards Institute. The standards provide the methods for the antimicrobial susceptibility testing, and they should be used to confirm the resistance phenotype.

Step 5: Document the Results

The results of the analysis should be documented. The documentation should include the research question, the methods that were used, the results of the analysis, and the validation of the results. The documentation should be clear and should be sufficient for the results to be reproduced by another researcher.

The documentation should also include the limitations of the analysis. The limitations should include the quality of the data, the methods that were used, and the potential for errors.

Records and Measurements

The records and measurements that are used in a comparative study are important for the reproducibility of the results. The records should include the following:

The genome accession numbers and the source of the sequence data
The quality metrics for each genome, including the N50 value and the number of contigs
The parameters that were used for the analysis, such as the tree-building method
The results of the analysis, including the phylogenetic tree and the gene presence/absence table
The results of the validation, including the antimicrobial susceptibility testing results

The records should be stored in a secure location and should be accessible to the other members of the research team. The records should be updated as the analysis is performed.

Common Failure Patterns and How to Avoid Them

Using Low-Quality Genomes

One of the most common failures in a comparative analysis is the use of low-quality genomes. The low-quality genomes can contain errors, which can lead to incorrect predictions. The errors can be caused by the assembly process, which can produce a genome that is missing genes or that has genes that are incorrectly assembled.

The failure can be avoided by the selection of high-quality genomes. The quality of the genomes should be checked before the analysis is performed. The quality metrics, such as the N50 value and the number of contigs, should be reviewed.

Misinterpreting the Phylogenetic Tree

The phylogenetic tree is a visual representation of the evolutionary relationships between the genomes. The tree can be misinterpreted if the support values are not considered. The support values indicate the probability of the grouping, and the groupings with a low support value should be interpreted with caution.

The misinterpretation can be avoided by the review of the support values. The support values should be reviewed for each branch of the tree. The branches with a low support value should be interpreted with caution.

Overinterpreting the AMR Finder Results

The AMR Finder is a useful tool for the identification of resistance genes. However, the results of the AMR Finder should not be overinterpreted. The presence of a resistance gene does not always mean that the organism is resistant to the antibiotic. The gene may not be expressed, or the gene may not be functional.

The overinterpretation can be avoided by the validation of the results. The results should be validated using the antimicrobial susceptibility testing. The testing should be performed using the standards that are provided by the Clinical and Laboratory Standards Institute.

Limitations of the Platform

The BV-BRC platform is a powerful tool for the comparative analysis of bacterial genomes. However, the platform has some limitations that should be considered.

The first limitation is the quality of the data. The platform is dependent on the data that is submitted by the research community. The data can contain errors, which can affect the results of the analysis. The errors can be introduced during the sequencing or the assembly of the genome.

The second limitation is the computational resources. The platform is a public resource, and the computational resources are limited. The analysis of a large number of genomes can take a long time, and the platform may not be able to handle the analysis of a very large number of genomes.

The third limitation is the need for the validation. The results of the analysis are predictions, and they should be validated using laboratory methods. The validation is important to confirm the predictions and to ensure that the results are accurate.

Biosafety and Laboratory Practice

The use of BV-BRC is a computational analysis, and it does not involve the handling of live organisms. However, the results of the analysis may be used to guide the laboratory work. The laboratory work should be performed in accordance with the biosafety guidelines that are provided by the Centers for Disease Control and Prevention (CDC) and the National Institutes of Health (NIH). The guidelines are provided in the Biosafety in Microbiological and Biomedical Laboratories (BMBL) document.

The BMBL document provides the principles of biosafety, including the risk assessment, the containment, and the decontamination. The risk assessment should be performed before the laboratory work is started. The risk assessment should consider the organism that is being handled, the procedures that are being performed, and the potential for the exposure.

The containment should be appropriate for the risk level of the organism. The containment can include the use of a biological safety cabinet, the use of personal protective equipment, and the use of the appropriate laboratory practices.

The decontamination should be performed after the laboratory work is completed. The decontamination can include the use of a disinfectant or the use of an autoclave.

The laboratory work should also be performed in accordance with the standards that are provided by the American Society for Microbiology (ASM). The ASM provides the resources for the professional microbiology education and the laboratory practice. The ASM also provides the workforce context for the microbiology profession.

Professional Escalation Criteria

The results of a comparative analysis should be reviewed by a qualified professional. The professional should have the expertise in the field of microbial genomics and should be able to interpret the results of the analysis.

The escalation should be performed by the contact of the appropriate public health authority. The public health authority can provide the guidance on the next steps and can help to coordinate the response.

Decision Framework for Selecting Genomes in Comparative Studies

A recurring challenge in BV-BRC comparative work is deciding which genomes to include when a search returns dozens or hundreds of assemblies. Researchers often default to including every available genome, which slows down tree construction and can obscure meaningful biological signal with redundant or low-quality data. A structured decision framework helps you move from a broad search result to a defensible genome set that answers your specific research question without overloading the platform.

Step 1: Define the Inclusion Criteria Before Searching

Write down the inclusion criteria before you open the search box. The criteria should flow directly from your research question. For example, if you are studying the emergence of a resistance gene in a clinical lineage, your criteria might include the species, the geographic region, the isolation year range, and the presence of a specific resistance gene annotation. If you are building a phylogenetic tree to understand species boundaries, your criteria should include taxonomic classification, assembly quality thresholds, and a requirement for a known outgroup.

Write these criteria in a lab notebook or a spreadsheet column. This record becomes the first entry in your analysis documentation and prevents scope creep when you see hundreds of search results. The American Society for Microbiology emphasizes the importance of clear laboratory practice and professional standards, and a written inclusion criteria is a direct application of that principle to computational work.

Step 2: Apply Quality Filters Before Biological Filters

BV-BRC provides quality metrics for every genome, including the number of contigs, the total assembly length, and the N50 value. The platform also provides a quality score that reflects assembly completeness and the absence of contamination. Apply these filters first because they are objective and do not depend on your research question.

A practical threshold for most comparative studies is to exclude genomes with an N50 below 20,000 base pairs or with more than 500 contigs. These thresholds are not universal standards, and you should adjust them based on your organism and your research question. For example, a study of a highly repetitive pathogen may need to accept lower N50 values because the organism is difficult to assemble. The key is to set a threshold, record it, and apply it consistently to every genome in your search result.

The quality score provided by BV-BRC is a composite measure that includes the presence of a complete set of core genes. A genome that is missing core genes may be incomplete or contaminated. Exclude genomes with a low quality score unless you have a specific reason to include them, and record that reason in your documentation.

Step 3: Apply the Diversity Sampling Rule

After quality filtering, you will often still have more genomes than you can reasonably analyze. The diversity sampling rule states that you should select genomes that represent the known diversity of the group instead of selecting every genome that passes the quality filter. This rule is especially important for phylogenetic tree construction, where including too many closely related genomes can create long, unresolved branches and slow down the analysis.

To apply the diversity rule, sort your filtered genomes by collection year, geographic region, or sequence type. Select a small number of genomes from each category. For example, if you are studying a pathogen that has been collected from five continents over a 20 year period, select two or three genomes from each continent and each time period. This approach captures the evolutionary and geographic diversity of the group without overwhelming the analysis.

The diversity rule also applies to the selection of an outgroup. The outgroup should be a genome that is known to be distantly related to the group of interest. The outgroup provides a reference point for the root of the tree. Without an outgroup, the tree will be unrooted, and the direction of evolution will be unclear. Select an outgroup that is closely enough related to be aligned with your genomes but distantly enough related to provide a clear reference.

Step 4: Use the Redundancy Check

A common failure in comparative genomics is the inclusion of multiple genomes that are nearly identical to each other. This can happen when the same strain has been sequenced multiple times or when a single outbreak has produced many closely related isolates. Redundant genomes do not add new information to a comparative analysis, but they do increase the computational load and can bias the results.

BV-BRC provides a way to check for redundancy through the average nucleotide identity (ANI) tool. ANI is a measure of the genetic similarity between two genomes. Genomes with an ANI above 99 percent are considered to be the same strain and are redundant for most comparative purposes. You can use the ANI tool to compare your selected genomes and remove any that are above this threshold.

If you do not want to run an ANI comparison, you can use a simpler proxy. Sort your genomes by the number of genes and the total assembly length. Genomes with nearly identical gene counts and assembly lengths are likely to be redundant. This proxy is not as accurate as ANI, but it is a useful first pass.

Step 5: Record the Selection Decision

Every genome that is included in or excluded from your analysis should be recorded. The record should include the genome accession number, the reason for inclusion or exclusion, and the date of the decision. This record is part of the documentation that is required for a reproducible analysis.

The record should be stored in a spreadsheet or a text file that is kept with the other analysis files. The record should be updated if you change your inclusion criteria or if you add new genomes to the analysis. The record should be clear enough that another researcher can understand why each genome was selected.

Troubleshooting the Genome Selection Process

Problem: The Search Returns Too Many Genomes

If the search returns more genomes than you can reasonably analyze, the first step is to refine the search query. Add additional filters, such as the collection year, the geographic region, or the source of the isolate. The search function in BV-BRC supports these filters, and they can reduce the number of results to a manageable number.

If the search still returns too many genomes, apply the quality filters and the diversity sampling rule. The quality filters will remove the low-quality genomes, and the diversity rule will reduce the number of genomes to a representative set. If you still have too many genomes, consider whether your research question is too broad. A question that asks about the evolution of a whole species will require more genomes than a question that asks about the evolution of a single lineage.

Problem: The Tree Is Slow to Build

A slow tree-building process is usually caused by too many genomes or by genomes that are too large. The tree-building process in BV-BRC uses a whole-genome alignment, which is computationally intensive. If the tree is slow to build, reduce the number of genomes in the analysis.

The first step is to check the number of genomes in your group. If you have more than 50 genomes, consider reducing the number to 20 or 30. The diversity rule can help you select a representative set. The second step is to check the quality of the genomes. A genome with a large number of contigs can slow down the alignment process. Remove the low-quality genomes from the group.

Problem: The Tree Has Low Support Values

Low support values indicate that the grouping is not well supported by the data. This can be caused by a lack of phylogenetic signal in the genomes or by the inclusion of genomes that are too closely related. The first step is to check the support values and identify the branches with low support. The second step is to consider the genomes that are included in the tree. If the tree includes many closely related genomes, the branches between them may have low support because there is not enough genetic difference to resolve the relationships.

The solution is to increase the diversity of the genomes in the tree. Add genomes that are more distantly related to the group of interest. This will provide more phylogenetic signal and may improve the support values. The solution is to use a different tree-building method. The maximum-likelihood method is more accurate than the neighbor-joining method, but it is also more computationally intensive. If the support values are low, try the maximum-likelihood method.

Problem: The AMR Finder Results Are Unexpected

The AMR Finder is a tool that identifies antimicrobial resistance genes in a genome. The results can be unexpected if the genome contains a gene that is not known to be associated with the resistance phenotype. The first step is to check the quality of the genome. A low-quality genome may contain a gene that is not actually present in the organism.

The second step is to validate the results using laboratory methods. The Clinical and Laboratory Standards Institute provides standards for antimicrobial susceptibility testing, and these standards should be used to confirm the resistance phenotype. The presence of a resistance gene does not always mean that the organism is resistant to the antibiotic. The gene may not be expressed, or the gene may not be functional.

Records and Measurements for the Selection Process

The records for the selection process should be stored in a spreadsheet that is part of the analysis documentation. The spreadsheet should include the following columns:

Genome accession number
Organism name
Collection year
Geographic region
N50 value
Number of contigs
Quality score
Inclusion or exclusion decision
Reason for the decision

The spreadsheet should be updated as the selection process is performed. The spreadsheet should be stored in a secure location and should be accessible to the other members of the research team. The spreadsheet should be updated as the analysis is performed.

The records should also include the parameters that were used for the analysis. The parameters include the tree-building method, the number of genomes, and the quality thresholds. The parameters should be recorded in the analysis documentation.

Professional Escalation Criteria for the Selection Process

The selection process is a computational analysis, and it does not involve the handling of live organisms. However, the results of the analysis may be used to guide the laboratory work. The laboratory work should be performed in accordance with the biosafety guidelines that are provided by the Centers for Disease Control and Prevention and the National Institutes of Health. The guidelines are provided in the Biosafety in Microbiological and Biomedical Laboratories document.

The escalation should be performed if the results of the analysis are unexpected or if the results have a significant impact on the public health. The escalation should be performed if the results indicate the presence of a novel resistance gene or a novel pathogen. The escalation should be performed by the contact of the appropriate public health authority. The public health authority can provide the guidance on the next steps and can help to coordinate the response.

The escalation should also be performed if the selection process reveals a genome that is of a quality that is so low that it cannot be used for the analysis. The low-quality genome may indicate a problem with the sequencing or the assembly process. The problem should be reported to the appropriate authority.

Frequently Asked Questions

What is the difference between BV-BRC and other genome databases?

BV-BRC is a specialized platform that is designed for the comparative analysis of bacterial genomes. It integrates the data from the other databases, such as NCBI, and provides a set of tools for the analysis. The other databases, such as NCBI, are more general and provide the data for a wide range of organisms.

How do I select the best genomes for a comparative analysis?

The best genomes for a comparative analysis are the genomes that are of high quality and that are representative of the diversity of the group. The quality of the genomes should be reviewed using the quality metrics, such as the N50 value and the number of contigs. The genomes should be selected to include the diversity of the group.

What is the difference between the maximum-likelihood and the neighbor-joining methods?

The maximum-likelihood method is a statistical method that is considered to be more accurate than the neighbor-joining method. The maximum-likelihood method is also more computationally intensive. The neighbor-joining method is a faster method that is often used for a large number of genomes.

How do I validate the results of the AMR Finder?

The results of the AMR Finder should be validated using the antimicrobial susceptibility testing. The testing should be performed using the standards that are provided by the Clinical and Laboratory Standards Institute. The presence of a resistance gene does not always mean that the organism is resistant to the phenotype.

Can I use BV-BRC for the analysis of a large number of genomes?

The platform can be used for the analysis of a large number of genomes, but the analysis may take a long time. The platform is a shared resource, and the computational resources are limited. The analysis of a very large number of genomes may not be possible.

What are the limitations of the phylogenetic tree?

The phylogenetic tree is a visual representation of the evolutionary relationships between the genomes. The tree is based on the data that is available, and the tree may not be accurate if the data is not complete. The tree should be interpreted with caution, and the support values should be considered.

How do I document the results of a comparative analysis?

The results of a comparative analysis should be documented in a clear and reproducible manner. The documentation should include the data that was used, the methods that were used, the results of the analysis, and the validation of the results. The documentation should be sufficient for the results to be reproduced by another researcher.

What should I do if I find a potential new pathogen?

If the results of the analysis indicate a potential new pathogen, the results should be escalated to the appropriate public health authority. The public health authority can provide the guidance on the next steps and the coordination of the response.

Using the Evidence

Source	Best use in this topic	Important limitation
Biosafety in Microbiological and Biomedical Laboratories	official guidance	Check the linked page for current local requirements
Microbiology Resources	official guidance	Check the linked page for current local requirements
Antimicrobial Resistance	official guidance	Check the linked page for current local requirements

Related Bacteriology Guides

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