# From Genomes to Typing Results ## Quick Answer - A reproducible phylogenomic workflow template combines version-controlled code, containerized software, and structured configuration files to produce consistent bacterial typing results across datasets and analysts. - Adopt a modular pipeline structure using Snakemake or Nextflow, with each analysis step isolated into reusable rules or processes that accept standardized input and produce documented output. - Reproducibility depends on recording software versions, reference databases, and parameter choices, but biological variation in bacterial genomes still requires manual inspection of results before interpretation. ## At a Glance | Workflow Component | Purpose | Common Choice | Reproducibility Risk | | --- | --- | --- | --- | | Workflow orchestration | Defines execution order and dependencies | Snakemake or Nextflow | Implicit dependencies cause inconsistent execution order | | Software environment | Pins tool versions and dependencies | Conda, Docker, or Singularity | Unpinned versions produce different results across runs | | Input data management | Tracks raw reads, assemblies, and metadata | Structured directory layout with checksums | Manual file handling leads to missing or mixed samples | | Reference databases | Provides typing and resistance gene references | Versioned database snapshots | Unversioned databases change results over time | | Parameter configuration | Controls analysis settings per step | YAML or JSON config files | Hardcoded parameters prevent reuse on new datasets | | Output reporting | Summarizes typing results and quality metrics | MultiQC, custom tables, or Jupyter notebooks | Unstructured outputs complicate audit and comparison | ## The Reproducibility Problem in Bacterial Phylogenomics Bacterial phylogenomic analysis transforms raw sequencing reads into typing results, phylogenetic trees, and resistance gene profiles. These outputs inform outbreak investigations, surveillance programs, and research studies. The analytical path from reads to results involves multiple software tools, each with its own parameters, reference databases, and dependencies. Small changes in any of these components can alter the final output, making it difficult to compare results across laboratories or over time. The core problem is that bioinformatics workflows are often executed interactively. A researcher runs one command, inspects the output, then runs the next command. This approach works for a single dataset but fails when the same analysis must be repeated on new samples, by a different person, or after a software update. The commands may be recorded in a history file or a notebook, but the software versions, database versions, and parameter choices are often implicit. When the analysis is repeated, the results may differ in ways that are difficult to trace. A reproducible workflow template addresses this problem by making every step explicit and automated. The workflow defines the order of operations, the software environment, the input files, and the output files. It records the versions of all components. It can be rerun on new data with minimal modification. This approach is standard practice in bioinformatics and is supported by workflow management systems such as Snakemake and Nextflow. The need for reproducibility extends beyond convenience. Public health laboratories that perform genomic surveillance must be able to demonstrate that their results are consistent and traceable. The World Health Organization describes antimicrobial resistance as a global health threat that requires coordinated surveillance and stewardship efforts [1]. Genomic typing results that cannot be reproduced undermine the credibility of surveillance data. Similarly, laboratories that follow official methods, such as those in the [Bacteriological Analytical Manual](https://www.fda.gov/food/laboratory-methods-food/bacteriological-analytical-manual-bam), must document their procedures and results in a way that supports regulatory decisions [2]. ## Core Principles of Reproducible Workflows ### Version Control for Code and Configuration Version control is the foundation of a reproducible workflow. Git is the standard tool for tracking changes to code and configuration files. Every change to a pipeline script, a configuration file, or a documentation file is recorded with a timestamp and an author. This history allows a researcher to identify which version of the pipeline produced a particular result. The version control repository should contain the workflow code, the configuration files, and the documentation. Raw sequencing data and large reference databases are typically stored outside the repository because of their size, but the repository should record their locations and versions. A file that lists the exact paths and checksums of all external data sources allows the workflow to be reconstructed even if the data are stored elsewhere. ### Environment Management Software dependencies are a common source of irreproducibility. A pipeline that runs with one version of a tool may fail or produce different results with another version. Environment management systems such as Conda, Docker, and Singularity isolate the software environment and pin the versions of every dependency. A Conda environment file lists every package and its version. When the workflow runs, it creates the environment exactly as specified. Docker and Singularity go further by packaging the entire operating system, the software, and the dependencies into a single image. This ensures that the workflow runs in the same environment on any machine. The choice between Conda and containerization depends on the infrastructure. Conda is simpler and works well on a shared server. Containers are more portable and provide a stronger guarantee of reproducibility, but they require Docker or Singularity to be installed. The workflow should document which environment system is used and how to recreate the environment. ### Configuration Files Configuration files separate the workflow logic from the analysis parameters. A Snakemake workflow reads a configuration file that specifies the input directory, the reference genome, the typing scheme, and the thresholds. A Nextflow workflow uses a similar approach with its own configuration syntax. The configuration file is versioned in the repository. When the workflow is run, the configuration file is recorded in the output directory. This allows a researcher to see exactly which parameters were used for a specific run. Changing a parameter is a matter of editing the configuration file, not the workflow code. ### Explicit Input and Output Definitions Each step in the workflow should declare its inputs and outputs explicitly. This is a core feature of both Snakemake and Nextflow. The workflow engine uses these declarations to determine the execution order and to detect missing files. Explicit input and output definitions also make the workflow easier to debug. If a step fails, the error message indicates which input file was missing or which output file could not be created. The workflow engine can also rerun only the failed steps, which saves time when a pipeline fails partway through. ## Choosing a Workflow Management System ### Snakemake Snakemake is a Python-based workflow management system. It uses a domain-specific language that resembles Python. Each rule defines an input, an output, and a shell command or Python script. The workflow engine determines the execution order based on the file dependencies. Snakemake is well suited for bacterial genomics because it integrates with Conda environments and container systems. A rule can specify a Conda environment file, and Snakemake will create the environment and run the rule within it. This makes it easy to manage software versions for each step. The main limitation of Snakemake is that it is Python-based. Users who are not familiar with Python may find the syntax challenging. However, the rule syntax is simple and can be learned quickly. ### Nextflow Nextflow is a workflow management system that uses a Groovy-based syntax. It is designed for data-intensive workflows and supports cloud computing and cluster execution. Nextflow uses a process-based model, where each process defines its own input, output, and execution script. Nextflow is well suited for large-scale analyses that require distributed computing. It can run on a single machine, a cluster, or a cloud environment without changing the workflow code. The main limitation is that the Groovy syntax is less familiar to many bioinformatics users. ### Choosing Between Them The choice between Snakemake and Nextflow depends on the infrastructure and the team. Snakemake is simpler and more familiar to Python users. Nextflow is more powerful for large-scale analyses and cloud execution. Both systems support the core reproducibility features: versioned code, environment management, and explicit input and output. For a laboratory that is just starting to build reproducible workflows, Snakemake is often the easier choice. It has a smaller learning curve and integrates well with the existing bioinformatics ecosystem. For a laboratory that already uses Nextflow or that needs to run analyses on a large cluster, Nextflow is a better fit. ## Building the Workflow Template ### Directory Structure A reproducible workflow starts with a well-organized directory structure. The following layout is a common pattern: ``` workflow/ ├── config/ │ └── config.yaml ├── data/ │ ├── raw/ │ └── processed/ ├── results/ │ ├── typing/ │ ├── trees/ │ └── reports/ ├── scripts/ │ ├── assemble.py │ ├── type.py │ └── tree.py ├── envs/ │ ├── assembly.yaml │ └── typing.yaml ├── Snakefile └── README.md ``` The `config` directory contains the configuration file. The `data` directory contains the raw and processed data. The `results` directory contains the output files. The `scripts` directory contains the Python or R scripts that perform the analysis. The `envs` directory contains the Conda environment files. The `Snakefile` defines the workflow. The `README.md` documents the workflow and how to run it. This structure separates the code from the data and the results. It also makes it easy to archive a specific run by copying the `config`, `scripts`, and `envs` directories along with the results. ### Configuration File The configuration file is the central control point for the workflow. It defines the samples, the reference files, and the parameters. A YAML file is a common format because it is human-readable and easy to edit. The configuration file should include the following sections: - `samples`: a list of sample identifiers and their raw read files - `reference`: the path to the reference genome or typing database - `output`: the path to the output directory - `parameters`: the specific parameters for each step The configuration file is versioned in the repository. When a run is executed, the configuration file is copied to the output directory. This allows the researcher to inspect the exact parameters used for a specific run. ### Workflow Rules The workflow is defined in a `Snakefile` or a `nextflow.config` file. Each rule defines an input, an output, and a command. The following is an example of a Snakemake rule for assembly: ```python rule assemble: input: reads = "data/raw/{sample}_R1.fastq.gz" output: contigs = "results/assembly/{sample}.fasta" conda: "envs/assembly.yaml" shell: "spades.py -1 {input.reads} -o {output.contigs}" ``` This rule takes a raw read file, runs SPAdes, and produces an assembly. The `conda` directive specifies the environment file. The `shell` directive specifies the command. The workflow can be extended by adding more rules for typing, tree building, and reporting. Each rule follows the same pattern: define the input, define the output, specify the environment, and specify the command. ### Running the Workflow The workflow is run with a single command. For Snakemake, the command is: ``` snakemake --use-conda --cores 8 ``` This command runs the workflow with the Conda environments and uses eight cores. The workflow engine determines the execution order based on the file dependencies. If a step fails, the workflow stops and reports the error. The failed step can be rerun after fixing the issue. For Nextflow, the command is: ``` nextflow run main.nf -profile standard ``` The `-profile` option selects the execution profile, which defines the computing environment. ## Adapting the Template to Different Datasets ### Sample Metadata The workflow template must be adaptable to different bacterial datasets. The first step is to define the sample metadata. This includes the sample identifier, the species, the source, and the collection date. The metadata is stored in a tabular file, such as a CSV or TSV file. The workflow reads the metadata file and uses it to define the input files. For example, the metadata file might contain a column for the sample identifier and a column for the read file path. The workflow uses these columns to construct the input paths. The metadata file should be versioned in the repository. This ensures that the workflow can be rerun with the same metadata. ### Reference Databases The reference database is a critical component of the workflow. For typing, the reference database contains the allele sequences or the typing scheme. For phylogenetic analysis, the reference genome is the reference for mapping or alignment. The reference database must be versioned. The workflow should record the version of the reference database in the output. This allows the researcher to compare results across different versions of the reference. The reference database is often large and stored outside the workflow repository. The workflow should record the path to the reference database and its version. The configuration file should include the reference database path and version. ### Parameter Tuning Different bacterial datasets may require different parameters. For example, the assembly parameters may differ for a small genome versus a large genome. The workflow should allow the parameters to be changed without modifying the workflow code. The configuration file is the place for parameter changes. The workflow reads the parameters from the configuration file and passes them to the commands. This allows the user to adjust the parameters for a specific dataset without editing the workflow code. ### Testing the Workflow The workflow should be tested on a small dataset before running it on a full dataset. The test dataset should include a few samples that cover the expected variation. The test run should produce the expected output and should be compared to a known result. The test dataset is stored in the repository. The workflow includes a test rule that runs the workflow on the test dataset and compares the output with the expected output. This test ensures that the workflow is working correctly and that changes to the workflow do not break the analysis. ## Quality Control and Validation ### Input Quality Control The quality of the input reads affects the quality of the output. The workflow should include a quality control step that checks the read quality and the read length. The quality control step can use tools such as FastQC and MultiQC. The quality control step produces a report that summarizes the quality metrics for each sample. The report is stored in the output directory. The workflow should fail if the quality metrics are below a threshold. The threshold is defined in the configuration file. ### Assembly Quality Control The assembly quality is also important. The workflow should include a step that checks the assembly quality, such as the number of contigs, the N50, and the total length. The step can use tools such as QUAST. The assembly quality metrics are stored in the output. The workflow should fail if the assembly quality is below a threshold. The threshold is defined in the configuration file. ### Typing Quality Control The typing results should be validated. The workflow should include a step that checks the typing results against a known reference. The step can use a tool such as MLST or a custom script. The typing quality control step produces a report that summarizes the typing results. The report is stored in the output. The workflow should fail if the typing results are inconsistent with the reference. ### Validation Against Reference The workflow should include a validation step that compares the results with a known reference. The reference can be a reference genome or a set of known typing results. The validation step produces a report that summarizes the differences. The validation step is important for ensuring the workflow is working correctly. It is also useful for comparing the results from different versions of the workflow. ## Records and Measurements ### Log Files The workflow should produce log files for each step. The log files record the commands that were run, the parameters that were used, and the output that was produced. The log files are stored in the output directory. The log files are important for debugging and for reproducing the results. They allow the researcher to see exactly what was done in a specific run. ### Version Records The workflow should record the version of every tool and database that was used. The version record is stored in the output directory. The version record can be a text file that lists the tool names and versions. The version record is important for reproducibility. It allows the researcher to identify which version of a tool produced a specific result. ### Run Metadata The workflow should record the metadata for each run. The metadata includes the date, the user, the configuration file, and the version of the workflow. The metadata is stored in the output directory. The run metadata is important for auditing and reproducibility. It allows the researcher to identify the exact conditions of a specific run. ## Common Failure Patterns ### Unpinned Software Versions The most common failure pattern is unpinned software versions. A workflow that does not specify the exact version of a tool can produce different results when the tool is updated. The workflow should pin the version of every tool in the environment file. ### Unversioned Reference Databases Another common failure is an unversioned reference database. A reference database that is updated without a version record can produce different results. The workflow should record the version of the reference database in the output. ### Hardcoded Parameters Hardcoded parameters are a common failure. A workflow that has parameters embedded in the code is difficult to adapt to new datasets. The workflow should use a configuration file for all parameters. ### Missing Input Files A missing input file is a common failure. The workflow should check that all input files exist before running. The workflow should fail with a clear error message if an input file is missing. ### Inconsistent Output Inconsistent output is a common failure. The workflow should produce the same output for the same input. The workflow should be tested on a small dataset to ensure that the output is consistent. ## Biosafety and Laboratory Context ### Biosafety Considerations The workflow is a computational analysis, but it is part of a larger laboratory workflow. The laboratory workflow includes the handling of bacterial cultures and the extraction of DNA. The biosafety considerations are important for the laboratory staff. The [Biosafety in Microbiological and Biomedical Laboratories](https://www.cdc.gov/labs/bmbl/index.html) provides guidance on risk assessment, containment, and decontamination. The laboratory should follow these guidelines when handling bacterial cultures and DNA samples. ### Professional Laboratory Practice The workflow is a tool for laboratory professionals. The [American Society for Microbiology](https://asm.org/) provides professional education and laboratory practice resources. The laboratory should follow the professional standards for microbiology practice. ### Antimicrobial Resistance Surveillance The workflow is often used for antimicrobial resistance surveillance. The [World Health Organization](https://www.who.int/health-topics/antimicrobial-resistance) describes antimicrobial resistance as a global health threat. The workflow can be used to track the spread of resistance genes and to inform public health decisions. ### Authenticated Materials The workflow uses reference materials. The [American Type Culture Collection](https://www.atcc.org/) provides authenticated biological materials. The workflow should use authenticated reference materials to ensure the accuracy of the results. ## Limitations and Escalation Criteria ### Limitations of the Workflow The workflow is a template. It is not a complete solution for every bacterial dataset. The workflow must be adapted to the specific species and the specific analysis. The workflow may not include all the steps that are needed for a specific analysis. The workflow is also limited by the quality of the input data. The workflow cannot correct for poor-quality reads or contaminated samples. The workflow should be used with high-quality input data. ### Escalation Criteria The workflow should be escalated to a professional if the results are inconsistent or if the workflow fails. The escalation criteria include: - The workflow fails to produce the expected output - The results are inconsistent with the known reference - The quality metrics are below the threshold - The workflow produces unexpected results In these cases, the researcher should consult with a bioinformatics professional or a laboratory supervisor. The professional can help identify the cause of the problem and the correct the workflow. ## A Practical Decision Framework for Workflow Step Selection and Parameter Choices ### The Need for Structured Decision Making The modular workflow template described above provides the structural foundation for reproducible phylogenomic analyses, but it does not answer a question that every researcher faces when adapting the template to a new dataset: which tools, parameters, and validation thresholds should be used for a specific bacterial species, sequencing platform, or research question? This section provides a practical decision framework that guides the researcher through the choices that must be made when configuring a workflow for a new dataset. The framework is organized around five decision points that occur in every phylogenomic analysis: read preprocessing, assembly strategy, typing scheme selection, phylogenetic inference, and result validation. Each decision point includes a set of questions, a recommended approach, and a record-keeping requirement. The framework is not a substitute for domain expertise. It is a structured way to document the reasoning behind analytical choices so that the choices can be reviewed, challenged, and improved. The framework also serves as a communication tool. When a workflow is shared with collaborators or reviewed by a supervisor, the decision record explains why a particular tool or parameter was chosen. This is especially important in public health and regulatory contexts where the analytical path must be transparent and defensible. ### Decision Point 1: Input Data Characterization The first decision point occurs before any analysis begins. The researcher must characterize the input data to determine which downstream steps are appropriate. The characterization should be recorded in a structured format that the workflow can read and act upon. The input characterization record should include the following fields for each sample: - Sequencing platform and chemistry - Read length distribution - Estimated genome size for the target species - Expected contamination level - Number of reads and total bases - Quality score distribution The workflow should include a rule that reads this characterization record and uses it to set parameters for downstream steps. For example, a sample with short reads from an Illumina platform may require different assembly parameters than a sample with long reads from an Oxford Nanopore platform. The characterization record makes these differences explicit and traceable. The decision framework for preprocessing is based on the quality metrics that are produced by tools such as FastQC. The researcher should define thresholds for the quality metrics before running the workflow. The thresholds should be recorded in the configuration file. If a sample fails the quality thresholds, the workflow should stop and flag the sample for manual review. The researcher should not proceed with a sample that has poor quality reads because the downstream results will be unreliable. The [Bacteriological Analytical Manual](https://www.fda.gov/food/laboratory-methods-food/bacteriological-analytical-manual-bam) provides context for the importance of sample quality in official food microbiology methods. While the manual focuses on culture-based methods, the principle that the quality of the input material determines the quality of the result applies equally to genomic analysis. ### Decision Point 2: Assembly Strategy The assembly strategy is the second decision point. The researcher must choose between a de novo assembly and a reference-based assembly. The choice depends on the availability of a high-quality reference genome for the target species and the research question. The decision framework for assembly is as follows: - If a high-quality reference genome is available and the research question is about single nucleotide variants or gene presence, a reference-based approach may be appropriate. - If the research question is about the structure of the genome, the presence of mobile elements, or the discovery of novel sequences, a de novo assembly is required. - If the species is not well characterized and no reference genome is available, a de novo assembly is the only option. The workflow template should support both approaches. The configuration file should include a parameter that selects the assembly mode. The workflow should record the assembly mode in the output metadata. The assembly quality metrics should be recorded for every sample. The metrics include the number of contigs, the N50 value, the total assembly length, and the number of ambiguous bases. The workflow should include a rule that checks the assembly quality against the thresholds defined in the configuration file. If the assembly quality is below the threshold, the workflow should flag the sample for manual review. The decision framework for assembly parameters should be based on the genome size of the target species. A small genome, such as a Mycoplasma species, may require different k-mer sizes than a larger genome, such as an Escherichia coli. The workflow should allow the k-mer size to be specified in the configuration file. The researcher should test a range of k-mer sizes on a small subset of the data and select the value that produces the best assembly quality metrics. ### Decision Point 3: Typing Scheme Selection The typing scheme is the set of loci or markers that are used to assign a sequence type to a bacterial isolate. The choice of typing scheme depends on the species and the research question. The workflow should support multiple typing schemes and should record which scheme was used for each sample. The decision framework for typing scheme selection is based on the following criteria: - The species of the sample - The resolution required for the research question - The availability of a validated typing scheme for the species - The compatibility of the typing scheme with the reference database For example, a researcher studying an outbreak of a foodborne pathogen may use a seven-gene multilocus sequence typing scheme that is well established for the species. A researcher studying the population structure of a species may use a whole-genome typing approach that compares the entire genome to a reference set. The workflow should include a rule that reads the typing scheme from the configuration file and applies it to the assembly. The workflow should record the typing scheme and the version of the typing database in the output metadata. The researcher should verify that the typing database is the correct version for the species and the typing scheme. The [CLSI](https://clsi.org/standards/products/microbiology) provides standards for antimicrobial susceptibility testing, which is a related but distinct analysis. The workflow for typing should not be confused with the workflow for susceptibility testing. The typing workflow determines the genetic characteristics of the isolate, while the susceptibility testing workflow determines the phenotypic response to antimicrobial agents. Both analyses are important for a complete characterization of a bacterial isolate, but they require different methods and different reference materials. ### Decision Point 4: Phylogenetic Tree Construction The phylogenetic tree is the output that many researchers use to visualize the relationships between bacterial isolates. The choice of tree construction method depends on the number of samples, the genetic distance between the samples, and the research question. The decision framework for tree construction is as follows: | Number of Samples | Genetic Distance | Recommended Method | Rationale | | --- | --- | --- | --- | | Fewer than 50 | Low | Maximum likelihood with a single nucleotide polymorphism alignment | Fast and accurate for closely related isolates | | Fewer than 50 | High | Maximum likelihood with a core genome alignment | Provides a more robust tree for divergent isolates | | More than 50 | Low | Maximum likelihood with a single nucleotide polymorphism alignment | Computationally efficient for large datasets | | More than 50 | High | Maximum likelihood with a core genome alignment | Provides a more accurate tree for divergent isolates | The workflow should include a rule that reads the sample count and the genetic distance from the input data and selects the appropriate tree method. The workflow should record the tree method and the parameters in the output metadata. The tree construction step should also include a bootstrap or a similar measure of statistical support. The bootstrap value indicates the confidence in the tree topology. The workflow should record the bootstrap values in the output. The researcher should review the bootstrap values and flag any branches with low support for manual inspection. ### Decision Point 5: Result Validation and Interpretation The final decision point is the validation and interpretation of the results. The workflow should produce a validation report that compares the typing results with a known reference. The validation report should include the following: - The typing results for each sample - The expected typing results for the reference samples - The comparison between the expected and the observed results - The quality metrics for each sample - The assembly metrics for each sample The validation report should be reviewed by the researcher before the results are used for any downstream analysis or publication. The researcher should check the following: - The typing results are consistent with the expected results for the reference samples - The quality metrics are within the acceptable range - The assembly metrics are within the acceptable range - The tree topology is consistent with the known relationships between the samples If any of these checks fail, the researcher should investigate the cause. The investigation should be documented in the validation report. The researcher should not proceed with the results until the issue is resolved. The validation step is also the point at which the researcher should consider the biosafety context of the analysis. The [Biosafety in Microbiological and Biomedical Laboratories](https://www.cdc.gov/labs/bmbl/index.html) provides guidance on the risk assessment and containment of biological materials. The researcher should ensure that the workflow is used in a laboratory that follows the appropriate biosafety practices for the species being analyzed. The workflow itself is a computational analysis, but it is part of a larger laboratory process that includes the handling of bacterial cultures and the extraction of DNA. ## A Record System for Workflow Decisions The decision framework is only useful if the decisions are recorded in a structured way. The following record system is designed to be integrated into the workflow template. The record system uses a set of files that are written to the output directory for each run. ### Decision Record File The decision record file is a text file that contains the decisions made for each sample. The file is written by the workflow at the beginning of the run. The file includes the following fields: - Sample identifier - Input characterization record path - Assembly mode - Assembly parameters - Typing scheme - Typing database version - Tree method - Tree parameters - Validation status The decision record file is written in a tabular format that can be read by a spreadsheet or a text editor. The file is versioned in the output directory. The researcher can compare the decision records from different runs to identify changes in the analysis. ### Parameter Log File The parameter log file records the exact parameters that were used for each step of the workflow. The file is written by the workflow at the end of each step. The file includes the tool name, the tool version, and the parameters that were passed to the tool. The parameter log file is important for reproducibility. It allows the researcher to identify the exact conditions of a specific run. The file is also useful for debugging. If a step fails, the parameter log file shows the parameters that were used. ### Run Metadata File The run metadata file records the metadata for the entire run. The file includes the date, the user, the workflow version, the configuration file, and the decision record file. The run metadata file is written at the end of the run. The run metadata file is important for auditing. It allows the researcher to identify the exact conditions of a specific run. The file is also useful for comparing the results from different runs. ### Sample Tracking Table The sample tracking table is a tabular file that records the status of each sample in the workflow. The table includes the following fields: - Sample identifier - Input file path - Preprocessing status - Assembly status - Typing status - Tree status - Validation status The sample tracking table is updated by the workflow after each step. The table is written to the output directory. The researcher can use the table to identify the samples that have failed and the samples that are ready for review. ## Troubleshooting Method for Workflow Failures The workflow will fail at some point. The troubleshooting method is a structured approach to identifying and resolving the cause of the failure. The method is based on the following steps: ### Step 1: Identify the Failed Step The first step is to identify the step that failed. The workflow engine reports the failed step in the error message. The researcher should check the log file for the failed step. The log file contains the command that was run and the error message. ### Step 2: Check the Input Files The second step is to check the input files for the failed step. The input files may be missing, corrupted, or in the wrong format. The researcher should check the file paths and the file contents. ### Step 3: Check the Parameters The third step is to check the parameters for the failed step. The parameters may be incorrect for the input data. The researcher should check the configuration file and the parameter log file. ### Step 4: Check the Software Environment The fourth step is to check the software environment. The software version may be incompatible with the input data or the other tools. The researcher should check the environment file and the version record. ### Step 5: Resolve the Issue The fifth step is to resolve the issue. The researcher should fix the issue and rerun the workflow. The workflow should be rerun from the failed step, not from the beginning. ### Step 6: Document the Issue The sixth step is to document the issue. The researcher should record the issue and the resolution in the run metadata file. The documentation is important for future runs and for other researchers. ## Comparison of Decision Approaches The decision framework described in this section can be compared to two alternative approaches: the ad hoc approach and the fully automated approach. ### Ad Hoc Approach The ad hoc approach is the default approach for many researchers. The researcher makes decisions based on the experience and the intuition. The decisions are not recorded in a structured way. The ad hoc approach is fast and flexible, but it is not reproducible. The results cannot be compared across runs or across researchers. ### Fully Automated Approach The fully automated approach is the opposite of the ad hoc approach. The workflow makes all decisions automatically based on the input data. The researcher does not have any input into the decisions. The fully automated approach is reproducible, but it is not flexible. The workflow may not be able to handle the variation in the input data. ### The Decision Framework Approach The decision framework approach is a middle ground. The workflow makes the decisions based on the input data and the configuration file. The researcher has control over the configuration file. The decisions are recorded in the output. The decision framework approach is reproducible and flexible. The decision framework approach is the recommended approach for a laboratory that needs to produce reproducible results. The approach is also recommended for a laboratory that needs to adapt the workflow to different datasets. ## Practical Implementation Steps The following steps are a practical guide to implementing the decision framework in the workflow: 1. Create a decision record file for each sample. The file should be written by the workflow at the beginning of the run. 2. Create a parameter log file for each step. The file should be written by the workflow at the end of each step. 3. Create a run metadata file for each run. The file should be written by the workflow at the end of the run. 4. Create a sample tracking table. The table should be updated by the workflow after each step. 5. Test the workflow on a small dataset. The test should include samples that cover the expected variation. 6. Review the decision record file and the parameter log file for the test run. The review should identify any issues with the decisions. 7. Adjust the decision criteria in the configuration file if needed. 8. Run the workflow on the full dataset. 9. Review the validation report and the sample tracking table. 10. Document the decisions and the results in the run metadata. The implementation steps are designed to be integrated into the workflow. The workflow should include rules for writing the decision record file, the parameter log file, the run metadata file, and the sample tracking table. The workflow should also include a rule for the validation report. ## Common Failure Patterns in Decision Making The following are common failure patterns in the decision-making process. The researcher should be aware of these patterns and take steps to avoid them. ### Pattern 1: Using the Same Parameters for All Samples The first failure pattern is using the same parameters for all samples. The parameters may be appropriate for one sample but not for another. The workflow should allow the parameters to be adjusted for each sample. ### Pattern 2: Not Recording the Decisions The second failure pattern is not recording the decisions. The decisions are made but not recorded. The workflow should record the decisions in the decision record file. ### Pattern 3: Not Validating the Results The third failure pattern is not validating the results. The results are produced but not validated. The workflow should include a validation step. ### Pattern 4: Ignoring the Quality Metrics The fourth failure pattern is ignoring the quality metrics. The quality metrics are below the threshold but the workflow continues. The workflow should stop if the quality metrics are below the threshold. ### Pattern 5: Not Documenting the Failures The fifth failure pattern is not documenting the failures. The failures are resolved but not documented. The workflow should document the failures in the run metadata. ## Welfare and Safety Context The decision framework is a computational tool, but it is part of a larger laboratory process. The laboratory process includes the handling of bacterial cultures and the extraction of DNA. The biosafety considerations are important for the laboratory staff. The [Biosafety in Microbiological and Biomedical Laboratories](https://www.cdc.gov/labs/bmbl/index.html) provides guidance on risk assessment, containment, and decontamination. The laboratory should follow these guidelines when handling bacterial cultures and DNA samples. The workflow should be performed in a laboratory that follows the appropriate biosafety practices for the species being analyzed. The [American Society for Microbiology](https://asm.org/) provides professional education and laboratory practice resources. The laboratory should follow the professional standards for microbiology practice. The workflow should be performed by trained personnel who understand the biological and the computational aspects of the analysis. The [World Health Organization](https://www.who.int/health-topics/antimicrobial-resistance) describes antimicrobial resistance as a global health threat. The workflow can be used to track the spread of resistance genes and to inform public health decisions. The workflow should be used in a way that supports the global surveillance and stewardship efforts. The [American Type Culture Collection](https://www.atcc.org/) provides authenticated biological materials. The workflow should use authenticated reference materials to ensure the accuracy of the results. The reference materials should be stored and handled according to the manufacturer's instructions. ## Frequently Asked Questions ### What is the difference between Snakemake and Nextflow? Snakemake uses a Python-based syntax and is easier for Python users to learn. Nextflow uses a Groovy-based syntax and is designed for large-scale and cloud computing. Both systems support versioned code, environment management, and output tracking. ### How do I choose the right workflow management system? Choose Snakemake if you are familiar with Python and need a simple workflow. Choose Nextflow if you need to run large-scale analyses or use cloud computing. Both systems are widely used in bioinformatics. ### How do I ensure that my workflow is reproducible? Use version control for your code and configuration files. Use environment management to pin the software versions. Record the version of every tool and database in the output. Test the workflow on a small dataset. ### What is the best way to handle reference databases? Store the reference database in a versioned directory. Record the version of the reference database in the output. Use a configuration file to specify the reference database path and version. ### How do I adapt the workflow to a new bacterial dataset? Edit the configuration file to specify the new samples and the new reference. Adjust the parameters in the configuration file if needed. Test the workflow on a small subset of the new data before running the full dataset. ### What should I do if the workflow fails? Check the log files to identify the failed step. Check the input files and the configuration file. Fix the issue and rerun the workflow. If the issue persists, contact a bioinformatics specialist. ### How do I validate the results of the workflow? Compare the results with a known reference. Use a validation step that compares the typing results with a reference. Check the quality metrics and the assembly metrics. If the results are inconsistent, investigate the cause. ### What are the biosafety considerations for the workflow? The workflow is a computational analysis, but it is part of a larger laboratory process. Follow the biosafety guidelines for handling bacterial cultures and DNA samples. Use the [Biosafety in Microbiological and Biomedical Laboratories](https://www.cdc.gov/labs/bmbl/index.html) for guidance. ## Related Bacteriology Guides - [Bacterial Contamination of Poultry: Salmonella and E. coli](/knowledge/bacteria/avian-bacteria/bacterial-contamination-poultry-salmonella-e-coli) - [Bacterial Diseases of Chickens: A Comprehensive Overview](/knowledge/bacteria/avian-bacteria/bacterial-diseases-of-chickens) - [Bacterial Growth Dynamics in Chicken: From Farm to Refrigeration](/knowledge/bacteria/avian-bacteria/bacterial-growth-dynamics-in-chicken) - [Bacterial Pathogens in Poultry: Comprehensive Clinical Reference](/knowledge/bacteria/avian-bacteria/bacterial-pathogens-in-poultry-clinical-reference) - [Bacterial Poultry Diseases: Comprehensive Overview and Classification](/knowledge/bacteria/avian-bacteria/bacterial-poultry-diseases-overview) ## References and Further Reading ## References [1] [Antimicrobial Resistance](https://www.who.int/health-topics/antimicrobial-resistance). World Health Organization. [2] [Bacteriological Analytical Manual](https://www.fda.gov/food/laboratory-methods-food/bacteriological-analytical-manual-bam). U.S. Food and Drug Administration. > This article is educational and does not replace validated laboratory procedures, clinical diagnosis, institutional biosafety review, or public-health guidance.