chewBBACA

chewBBACA stands for "BSR-Based Allele Calling Algorithm". The "chew" part could be thought of as "Comprehensive and Highly Efficient Workflow" but at this point still it needs a bit of work to make that claim so we just add "chew" to add extra coolness to the software name. BSR stands for BLAST Score Ratio as proposed by Rasko DA et al.

chewBBACA is a comprehensive pipeline including a set of functions for the creation and validation of whole genome and core genome MultiLocus Sequence Typing (wg/cgMLST) schemas, providing an allele calling algorithm based on Blast Score Ratio that can be run in multiprocessor settings and a set of functions to visualize and validate allele variation in the loci. chewBBACA performs the schema creation and allele calls on complete or draft genomes resulting from de novo assemblers.

chewBBACA has been published (version 2.0.5 at the time) in Microbial Genomics under the title: chewBBACA: A complete suite for gene-by-gene schema creation and strain identification - Link to paper

When using chewBBACA please use the following citation:

Silva M, Machado MP, Silva DN, Rossi M, Moran-Gilad J, Santos S, Ramirez M, Carriço JA. 2018. chewBBACA: A complete suite for gene-by-gene schema creation and strain identification. Microb Genom 4:000166. doi:10.1099/mgen.0.000166

Important Notes.
Useful links.
Installation.
Quick Start.
Detailed Usage.
1. Whole Genome Multilocus Sequence Typing (wgMLST) schema creation.
2. Allele call using a cg/wgMLST schema.
3. Determine annotations for loci in the schema.
4. Evaluate wgMLST call quality per genome.
5. Defining the cgMLST schema.
6. Evaluate your schema.
7. Adapt an external schema.
FAQ.
Citation.

Important Notes

chewBBACA only works with python 3 (automatic testing for Python 3.8 and Python 3.9 with GitHub Actions).
We strongly recommend that users install and use BLAST 2.9.0+ with chewBBACA, as chewBBACA's processes have been extensively tested with that version of BLAST.
chewBBACA includes Prodigal training files for some species. You can consult the list of Prodigal training files that are readily available here. We strongly recommend using the same Prodigal training file for schema creation and allele calling to ensure consistent results.
chewBBACA defines an allele as a complete Coding DNA Sequence, with start and stop codons according to the NCBI genetic code table 11 identified using Prodigal 2.6.0 . It will automatically exclude any allele for which the DNA sequence does not contain start or stop codons and for which the length is not multiple of three. Alleles that contain ambiguous bases are also excluded.
Make sure that your FASTA files are UNIX format. If they were created in Linux or MacOS systems they should be in the correct format, but if they were created in Windows systems, you should do a quick conversion using for example dos2unix.

Useful links

Check the wiki pages for a much more thorough chewBBACA walkthrough.
An extensive tutorial repository is available as example on how to run an analysis pipeline using chewBBACA.
Look through the list of schemas in Chewie-NS. Chewie-NS allows chewBBACA users to download and update cg/wgMLST schemas, allowing the easy sharing of results, while ensuring the reproducibility and consistency of these steps.
Use BBACA gitter if you have any pressing question. Chat can be faster and better than email for troubleshooting purposes.
A ready to use docker image automatically built from the latest version of chewBBACA in Ubuntu 16.04.
chewBBACA is available as a Galaxy module many thanks to Stefano Morabito and Arnold Knijn for EURL VTEC in ISS, Rome!
Check our Changelog to know more about the latest changes!

Installation

Install the latest released version using conda:

conda create -c bioconda -c conda-forge -n chewie chewbbaca=2.8.5 blast=2.9

Install using pip:

pip3 install chewbbaca

chewBBACA has the following dependencies:

Python dependencies (defined in the requirements file, should be automatically installed when using conda or pip):

numpy>=1.14.0
scipy>=0.13.3
biopython>=1.70
plotly>=1.12.9
SPARQLWrapper>=1.8.0
requests>=2.2.1
pandas>=0.22.0

Main dependencies:

BLAST 2.9.0+
Prodigal 2.6.0
MAFFT (for schema evaluation only)

Installation through conda should take care of all dependencies. If you install through pip you will need to ensure that you have BLAST, Prodigal and MAFFT installed and added to the PATH.

Quick Start

Create a schema

Option 1 - Genome assemblies

Include all genome assemblies (complete or draft genome assemblies in FASTA format) in a directory and adapt the following template command:

chewBBACA.py CreateSchema -i InputAssemblies -o OutputSchemaFolder --ptf ProdigalTrainingFile

Option 2 - Adapt an external schema

Include all loci files (one FASTA file per locus, each file contains all alleles for a specific locus) in a directory and adapt the following template command:

chewBBACA.py PrepExternalSchema -i ExternalSchemaFastaFiles -o OutputSchemaFolder --ptf ProdigalTrainingFile

Perform allele calling

Determine the allelic profiles for genome assemblies:

chewBBACA.py AlleleCall -i InputAssemblies -g OutputSchemaFolder/SchemaName -o OutputFolderName

Use a subset of the loci in a schema:

chewBBACA.py AlleleCall -i InputAssemblies -g OutputSchemaFolder/SchemaName -o OutputFolderName --gl LociList.txt

Important:

Genome assemblies and loci files from external schemas must be in FASTA format.
We strongly advise users to provide a Prodigal training file and to keep using the same training file to ensure consistent results (the training file used for schema creation is added to the schema's directory and automatically detected at start of allele calling without the need to pass it to the --ptf parameter).
Use the --cpu parameter to enable parallelization and considerably reduce execution time.
The file passed to the --gl parameter must have one locus identifier per line (a locus identifier is the name of the FASTA file that contains the locus alleles).

Detailed Usage

i. Whole Genome Multilocus Sequence Typing (wgMLST) schema creation

Create a schema seed based on a set of FASTA files with genome assemblies or coding sequences.

Basic usage:

chewBBACA.py CreateSchema -i /path/to/InputAssemblies -o /path/to/OutputFolderName --n SchemaName --ptf /path/to/ProdigalTrainingFile --cpu 4

Parameters:

-i, --input-files: (Required) Path to the directory that contains the input FASTA files. Alternatively, a single file with a list of paths to FASTA files, one per line.

-o, --output-directory: (Required) Output directory where the process will store intermediate files and create the schema's directory.

--n, --schema-name: (Optional) Name given to the folder that will store the schema files (default: schema_seed).

--ptf, --training-file: (Optional) Path to the Prodigal training file. We strongly advise users to provide a Prodigal training file and to keep using the same training file to ensure consistent results (default: None).

--bsr, --blast-score-ratio: (Optional) BLAST Score Ratio value. Sequences with alignments with a BSR value equal to or greater than this value will be considered as sequences from the same gene (default: 0.6).

--l, --minimum-length: (Optional) Minimum sequence length value. Coding sequences shorter than this value are excluded (default: 201).

--t, --translation-table: (Optional) Genetic code used to predict genes and to translate coding sequences (default: 11).

--st, --size-threshold: (Optional) CDS size variation threshold. Added to the schema's config file and used to identify alleles with a length value that deviates from the locus length mode during the allele calling process (default: 0.2).

--cpu, --cpu-cores: (Optional) Number of CPU cores that will be used to run the CreateSchema process (will be redefined to a lower value if it is equal to or exceeds the total number of available CPU cores)(default: 1).

--b, --blast-path: (Optional) Path to the BLAST executables (default: assumes BLAST executables were added to PATH).

--pm, --prodigal-mode: (Optional) Prodigal running mode (default: single).

--CDS: (Optional) If provided, input is a single or several FASTA files with coding sequences (default: False).

--no-cleanup: (Optional) If provided, intermediate files generated during process execution are not removed at the end (default: False).

Outputs:

OutputFolderName
├── SchemaName
│   ├── short
│   │   ├── GenomeID_proteinN_short.fasta
│   │   ├── ...
│   │   └── GenomeID_proteinN_short.fasta
│   ├── GenomeID_proteinN.fasta
│   ├── ...
│   ├── GenomeID_proteinN.fasta
│   └── Training_file.trn
├── invalid_cds.txt
└── cds_coordinates.tsv

One fasta file per distinct gene identified in the schema creation process in the OutputFolderName/SchemaName directory. The name attributed to each fasta file in the schema is based on the genome of origin of the first allele identified for that gene and on the order of gene prediction (e.g.: GCA-000167715-protein12.fasta, first allele for the gene was identified in an assembly with the prefix GCA-000167715 and the gene was the 12th gene predicted by Prodigal in that assembly). The OutputFolderName/SchemaName directory also contains a directory named short that includes fasta files with the representative sequences for each locus. The training file passed to create the schema is also included in OutputFolderName/SchemaName and will be automatically detected during the allele calling process. A file with the locations of the identified genes in each genome passed to create the schema, cds_coordinates.tsv, and a file with the list of alleles predicted by Prodigal that were excluded in the subsequent steps, invalid_cds.txt, are included in OutputFolderName.

ii. Allele call using a cg/wgMLST schema

Perform allele calling to determine the allelic profiles of a set of samples in FASTA format. The process identifies new alleles, assigns an integer identifier to those alleles and adds them to the schema.

Basic usage:

chewBBACA.py AlleleCall -i /path/to/InputAssemblies -g /path/to/SchemaName -o /path/to/OutputFolderName --cpu 4

Parameters:

-i, --input-files: (Required) Path to the directory with the genome FASTA files or to a file with a list of paths to the FASTA files, one per line.

-g, --schema-directory: (Required) Path to the schema directory with the loci FASTA files.

-o, --output-directory: (Required) Output directory where the allele calling results will be stored (will create a subdirectory named "results_<TIMESTAMP>" if the path passed by the user already exists).

--ptf, --training-file: (Optional) Path to the Prodigal training file. Default is to get training file from the schema's directory (default: searches for a training file in the schema's directory).

--gl, --genes-list: (Optional) Path to a file with the list of genes in the schema that the process should identify alleles for (default: False).

--l, --minimum-length: (Optional) Minimum sequence length accepted for a coding sequence to be included in the schema (default: uses value defined in schema config file. Default value added to the config file is 0).

--t, --translation-table: (Optional) Genetic code used to predict genes and to translate coding sequences. Must match the genetic code used to create the training file (default: uses value defined in schema config).

--st, --size-threshold: (Optional) CDS size variation threshold. If set to a value of 0.2, alleles with size variation +-20 percent will be classified as ASM/ALM (default: uses value defined in schema config).

--cpu, --cpu-cores: (Optional) Number of CPU cores that will be used to run the AlleleCall process (will be redefined to a lower value if it is equal to or exceeds the total number of available CPU cores/threads)(default: 1).

--b, --blast-path: (Optional) Path to the BLAST executables. Use this option if chewBBACA cannot find the BLASTp and makeblastdb executables or if you want to use anoter BLAST installation that is not the one added to the PATH (default: assumes BLAST executables were added to PATH).

--pm, --prodigal-mode: (Optional) Prodigal running mode (default: single).

--cds, --cds-input: (Optional) Input files contain coding sequences (one Fasta file per strain). Skips gene prediction with Prodigal (default: False).

--no-inferred: (Optional) If provided, the process will not add the sequences of inferred alleles to the schema (default: False).

--output-unclassified: (Optional) Create a Fasta file with unclassified coding sequences (default: False).

--output-missing: (Optional) Create a Fasta file with coding sequences classified as NIPH, NIPHEM, ASM, ALM, PLOT3, PLOT5 and LOTSC (default: False).

--no-cleanup: (Optional) If provided, intermediate files generated during process execution are not removed at the end (default: False).

--hash-profile: (Optional) Create TSV file with hashed allelic profiles. Profiles can be hashed with any of the hash algorithms implemented in the hashlib and zlib libraries (default: None).

--mode: (Optional) Execution mode (1: only exact matches at DNA level; 2: exact matches at DNA and Protein level; 3: exact matches and minimizer-based clustering to find similar alleles based on BSR+0.1; 4: runs the full process to find exact matches and similar matches based on BSR value) (default: 4).

By default, the AlleleCall process uses the Prodigal training file included in the schema's directory and it is not necessary to pass a training file to the --ptf parameter. If a text file with a list of gene identifiers, one per line, is passed to the --gl parameter, the process will only perform allele calling for the genes in that list.

Outputs:

OutputFolderName
├── cds_coordinates.tsv
├── invalid_cds.txt
├── loci_summary_stats.tsv
├── results_statistics.tsv
├── results_contigsInfo.tsv
├── results_alleles.tsv
├── paralogous_counts.tsv
├── paralogous_loci.tsv
└── logging_info.txt

The cds_coordinates.tsv file contains the coordinates (genome unique identifier, contig identifier, start position, stop position, protein identifier attributed by chewBBACA sequentially and coding strand) of the coding sequences identified in each genome.

The invalid_cds.txt file contains the list of alleles predicted by Prodigal that were excluded based on sequence size and presence of ambiguous bases.

The loci_summary_stats.tsv file contains the counts for each classification type (EXC, INF, PLOT3, PLOT5, LOTSC, NIPH, NIPHEM, ALM, ASM, LNF) and the total number of classified CDS (non-LNF) per locus.

The results_statistics.tsv file contains the total number of exact matches (EXC), inferred new alleles (INF), loci on contig tips (PLOT3/PLOT5), loci identified on contigs smaller than the matched schema representative (LOTSC), non-informative paralogous hits (NIPH/NIPHEM), alleles larger than locus length mode (ALM), alleles smaller than locus length mode (ASM) and loci not found (LNF) classifications attributed for each genome.

The results_contigsInfo.tsv file contains the loci positions in the genomes analyzed. The first column contains the name of the genome files used in the allele calling and the other columns (with loci names in the headers) the locus position information or the classification attributed by chewBBACA if it was not an exact match or inferred allele.

The results_alleles.tsv file contains the allelic profiles determined for the input samples. The first column has the identifiers of the genome assemblies for which the allele call was performed. The remaining columns contain the allele call data for loci present in the schema, with the column headers being the locus identifiers.

The paralogous_counts.tsv file contains the list of paralogous loci and the number of times those loci matched a CDS that was also similar to other loci in the schema.

The paralogous_loci.tsv file contains the sets of paralogous loci identified per genome (genome identifier, identifiers of the paralogous loci and the coordinates of the CDS that is similar to the group of paralogous loci).

The logging_info.txt contains summary information about the allele calling process.

If the --output-unclassified parameter is provided, the process will create a FASTA file with the DNA sequences of the distinct CDS that were not classified.

If the --output-missing parameter is provided, the process will create a FASTA file with the DNA sequences of the CDS classified as PLOT3, PLOT5, LOTSC, NIPH, NIPHEM, ALM and ASM.

If the --hash-profiles parameter is provided, the process will use the provided hash algorithm to create a TSV file with hashed profiles (each allele identifier is substituted by the hash of the DNA sequence).

Please visit the Wiki section about Allele Calling if you want to know more about the types of classifications attributed by chewBBACA and the output files.

iii. Determine annotations for loci in the schema

The UniprotFinder process can be used to retrieve annotations for the loci in the schema through requests to UniProt's SPARQL endpoint and through alignment against the reference proteomes for a set of taxa.

Basic usage:

chewBBACA.py UniprotFinder -i /path/to/SchemaName -o /path/to/OutputFolderName -t /path/to/cds_info.tsv --taxa "Species Name" --cpu 4

Parameters:

-i, --input-files: (Required) Path to the schema's directory or to a file with a list of paths to loci FASTA files, one per line.

-o, --output-directory: (Required) Output directory where the process will store intermediate files and save the final TSV file with the annotations.

-t, --protein-table: (Optional) Path to the "cds_coordinates.tsv" file created by the CreateSchema process (default: None).

--bsr: (Optional) BLAST Score Ratio value. This value is only used when a taxon/taxa is provided and local sequences are aligned against reference proteomes (default: 0.6).

--cpu, --cpu-cores: (Optional) Number of CPU cores used to run the process (default: 1).

--taxa: (Optional) List of scientific names for a set of taxa. The process will search for and download reference proteomes with terms that match any of the provided taxa (default: None).

--pm: (Optional) Maximum number of proteome matches to report (default: 1).

--no-sparql: (Optional) If provided, the process will not search for annotations through UniProt's SPARQL endpoint.

Outputs:

The /path/to/OutputFolderName directory contains a TSV file, schema_annotations.tsv, with the information found for each locus. By default, the process searches for annotations through UniProt's SPARQL endpoint, reporting the product name and UniProt URL for local loci with an exact match in UniProt's database. If the cds_coordinates.tsv file is passed to the -t parameter, the output file will also include the information in that file. The --taxa parameter receives a set of taxa names and searches for reference proteomes that match the provided terms. The reference proteomes are downloaded and the process aligns schema representative sequences against the reference proteomes to include additional information in the schema_annotations.tsv file based on matches against the sequences in the reference proteomes.

iv. Evaluate wgMLST call quality per genome

Basic usage:

chewBBACA.py TestGenomeQuality -i /path/to/AlleleCall/results/results_alleles.tsv -n 12 -t 200 -s 5 -o /path/to/OutputFolderName

-i Path to file with a matrix of allelic profiles (i.e. results_alleles.tsv).

-n Maximum number of iterations. Each iteration removes a set of genomes over the defined threshold (-t) and recalculates loci presence percentages.

-t Maximum threshold. This threshold represents the maximum number of missing loci allowed, for each genome independently, before removing the genome.

-s Step to add to each threshold (suggested 5).

-o Path to the output directory that will store output files.

The output is a HTML file with a plot with all thresholds and a removedGenomes.txt file with information about which genomes were removed per threshold when it reaches a stable point (no more genomes are removed).

Example of an output can be seen here. The example uses an original set of 714 genomes and a scheme consisting of 3266 loci with -n 12, -t 300 and -s 5 passed to arguments.

v. Defining the cgMLST schema

Determine the set of loci that constitute the core genome based on a threshold.

Basic usage:

chewBBACA.py ExtractCgMLST -i /path/to/AlleleCall/results/results_alleles.tsv -o /path/to/OutputFolderName

-i, --input-file: (Required) Path to input file containing a matrix with allelic profiles.

-o, --output-directory: (Required) Path to the directory where the process will store output files.

--t, --threshold: (Optional) Genes that constitute the core genome must be in a proportion of genomes that is at least equal to this value. (e.g 0.95 to get a matrix with the loci that are present in at least 95% of the genomes) (default: 1).

--r, --genes2remove: (Optional) Path to file with a list of genes/columns to remove from the matrix (one gene identifier per line, e.g. the list of genes listed in the RepeatedLoci.txt file created by the AlleleCall process) (default: False).

--g, --genomes2remove: (Optional) Path to file with a list of genomes/rows to remove from the matrix (one genome identifier per line, e.g. list of genomes to be removed based on the results from the TestGenomeQuality process) (default: False).

Note: The matrix with allelic profiles created by the ExtractCgMLST process can be imported into PHYLOViZ to visualize and explore typing results.

vi. Evaluate your schema

Evaluate the number of alelles and allele size variation for the loci in a schema or for a set of selected loci. Provide information about problematic alleles per locus and individual pages for each locus with a plot with allele size, a Neighbor Joining tree based on a multiple sequence alignment (MSA) and a visualization of the MSA.

See an example here

Basic usage:

chewBBACA.py SchemaEvaluator -i /path/to/SchemaName -o /path/to/OutputFolderName --cpu 4

-i, --input-files: (Required) Path to the schema's directory or path to a file containing the paths to the FASTA files of the loci that will be evaluated, one per line.

-o, --output: (Required) Path to the output directory where the report HTML files will be generated.

-a, --annotations: (Optional) Path to the TSV table created by the UniprotFinder process.

--ta, --translation-table: (Optional) Genetic code used to translate coding sequences (default: 11).

--th, --threshold: (Optional) Allele size variation threshold. If an allele has a size within the interval of the locus mode -/+ the threshold, it will be considered a conserved allele (default: 0.05).

--ml, --minimum-length: (Optional) Minimum sequence length accepted for a coding sequence to be included in the schema.

--cpu: (Optional) Number of CPU cores to use to run the process (default: 1).

--light: (Optional) Skips the indepth analysis of the individual schema loci, including MAFFT (default: False).

--no-cleanup: (Optional) Does not remove intermediate files after report generation.

Please consult the SchemaEvaluator's wiki page for more information.

vii. Adapt an external schema

The PrepExternalSchema process enables the adaptation of external schemas so that it is possible to use those schemas with chewBBACA. An external schema may be a set of sequences from any number of genes that have been selected for a particular study or it may be a schema that has already been defined and is available for download from some well known databases, such as Ridom, BIGSdb and Enterobase.

Basic usage:

chewBBACA.py PrepExternalSchema -i /path/to/ExternalSchemaFastaFiles -o /path/to/OutputFolderName --ptf /path/to/ProdigalTrainingFile --cpu 4

-i, --input-files: (Required) Path to the folder containing the FASTA files, one FASTA file per gene/locus (alternatively, a file with a list of paths can be given).

-o, --output-directory: (Required) The directory where the output files will be saved (will create the directory if it does not exist).

--ptf, --training-file: (Optional) Path to the Prodigal training file that will be included in the adapted schema (default: None).

--bsr, --blast-score-ratio: (Optional) The BLAST Score Ratio value that will be used to adapt the external schema (default: 0.6).

--l, --minimum-length: (Optional) Minimum sequence length accepted. Sequences with a length value smaller than the value passed to this argument will be discarded (default: 0).

--t, --translation-table: (Optional) Genetic code to use for CDS translation. Must match the genetic code used to create the training file (default: 11).

--st, --size-threshold: (Optional) CDS size variation threshold. At the default value of 0.2, alleles with size variation +-20 percent when compared to the representative will not be included in the final schema (default: 0.2).

--cpu, --cpu-cores: (Optional) The number of CPU cores to use (default: 1).

FAQ

Q: What strains should I use for Schema Creation?

A: The set of genome assemblies used for schema creation should be carefully selected to avoid the inclusion of spurious loci in the schema seed that result from low quality assemblies (e.g.: genome assemblies resulting from low quality sequencing data, highly fragmented genome assemblies, genome assemblies with many frameshifted proteins, genome length too large or too small). A set of high quality genome assemblies, ideally complete genomes, that capture the diversity of the species or lineage of interest should result in a good schema seed.

Q: Allele calling just crashed, do I need to start over?

A: chewBBACA should allow you to continue where you stopped, just re-run the same command and you should be prompted to continue the allele call or use the flag --fc. If the process keeps crashing with the same set of inputs, it is very likely that one or more of the inputs is misformatted or has a format that is incompatible with chewBBACA. Please ensure that your input files are genome assemblies or coding sequences in FASTA format. Consider opening an issue to report the problem, we will do our best to help solve the issue and user feedback is very important for the continued improvement of chewBBACA.

Q: I ran all the steps and my cgMLST loci size is smaller than traditional MLST...does this even work?

A: In order to have a robust definition of a cgMLST schema for a given bacterial species, a set of representative strains of the diversity of a given species should be selected. Furthermore, since cgMLST schema definition is based on pre-defined thresholds, only when a sufficient number of strains have been analyzed can the cgMLST schema be considered stable. This number will always depend on the population structure and diversity of the species in question. cgMLST schemas are defined as the set of loci that are present in all strains under analysis, but defining a smaller loci presence threshold, such as 95%, might be necessary to include very frequent genes or ubiquitous genes that are not present in some strains due to sequencing/assembly limitations. The quality of the genome assemblies is also an important factor that can impact profoundly the MLST schema definition (e.g.: genome assemblies with a high number of missing loci, contaminated genome assemblies, misclassified genome assemblies). The TestGenomeQuality process can be used to identify genome assemblies that are responsible for a considerable loss of loci. You can pass a list of genomes to remove to the ExtractCgMLST process to exclude those genomes from the analysis that determines the set of loci that constitute the core-genome. Identifying and removing low quality genome assemblies can significantly improve the determination of the core-genome.

Q: Can I use a schema from an external source?

A: Yes. The PrepExternalSchema process enables the adaptation of external schemas so that it is possible to use those schemas with chewBBACA. An external schema may be a set of sequences from any number of genes that have been selected for a particular study or it may be a schema that has already been defined and is available for download from some well known databases, such as Ridom cgMLST, BIGSdb and Enterobase.

Q: Which species already have a training file?

A: At the moment:

Acinetobacter baumannii
Campylobacter jejuni
Enterococcus faecium
Escherichia coli
Haemophilus influenzae
Legionella pneumophila
Listeria monocytogenes
Salmonella enterica enteritidis
Staphylococcus aureus
Staphylococcus haemolyticus
Streptococcus agalactiae
Streptococcus canis
Streptococcus dysgalactiae
Streptococcus equi
Streptococcus pneumoniae
Streptococcus pyogenes
Yersinia enterocolitica

get them here.

Q: My favorite species has no training file. What can I do?

A: You can propose a new one to be added to the repository or create your own training files. To create a training file make sure you have Prodigal installed and run the following command:

prodigal -i myGoldStandardGenome.fna -t myTrainedFile.trn -p single

chewBBACA
chewBBACA copied to clipboard

Metadata

chewBBACA

Contents

Important Notes

Useful links

Installation

Quick Start

Create a schema

Perform allele calling

Detailed Usage

i. Whole Genome Multilocus Sequence Typing (wgMLST) schema creation

ii. Allele call using a cg/wgMLST schema

iii. Determine annotations for loci in the schema

iv. Evaluate wgMLST call quality per genome

v. Defining the cgMLST schema

vi. Evaluate your schema

vii. Adapt an external schema

FAQ

Q: What strains should I use for Schema Creation?

Q: Allele calling just crashed, do I need to start over?

Q: I ran all the steps and my cgMLST loci size is smaller than traditional MLST...does this even work?

Q: Can I use a schema from an external source?

Q: Which species already have a training file?

Q: My favorite species has no training file. What can I do?

Citation

← Metadata

Owner

Metadata

chewBBACA chewBBACA copied to clipboard

Metadata

chewBBACA

Contents

Important Notes

Useful links

Installation

Quick Start

Create a schema

Perform allele calling

Detailed Usage

i. Whole Genome Multilocus Sequence Typing (wgMLST) schema creation

ii. Allele call using a cg/wgMLST schema

iii. Determine annotations for loci in the schema

iv. Evaluate wgMLST call quality per genome

v. Defining the cgMLST schema

vi. Evaluate your schema

vii. Adapt an external schema

FAQ

Q: What strains should I use for Schema Creation?

Q: Allele calling just crashed, do I need to start over?

Q: I ran all the steps and my cgMLST loci size is smaller than traditional MLST...does this even work?

Q: Can I use a schema from an external source?

Q: Which species already have a training file?

Q: My favorite species has no training file. What can I do?

Citation

← Metadata

Owner

Metadata

chewBBACA
chewBBACA copied to clipboard