FAQ

What strains should I use for Schema Creation?

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.

Should I worry about file naming best practices for input files?

Yes, definitely. Ideally, input FASTA files should have short names without blank spaces or special characters (e.g.: !@#?$^*()+). It is also important to ensure each input file has a short (maximum 30 characters) and unique basename prefix (everything before the first . in the basename). chewBBACA uses the basename prefix as a unique identifier during process execution and to label the final results. chewBBACA will warn you and exit if any files share the same basename prefix (e.g.: GCF_002209245.1_ASM220924v1_genomic.fna and GCF_002209245.2_ASM220924v2_genomic.fna). chewBBACA accepts input files with the following file extensions: .fasta, .fna, .ffn, .fa and .fas.

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

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 results of the ExtractCgMLST module can help in identifying genome assemblies that are responsible for a considerable loss of loci. You can provide a file with the list of genome assemblies that you’ve identified as being of low quality to exclude them from the cgMLST analysis. Identifying and removing low quality genome assemblies can significantly improve the determination of the core-genome.

Can I use a schema from an external source?

Yes. The PrepExternalSchema module 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, BIGSdb-Pasteur and Enterobase. You can also download schemas compatible with chewBBACA from 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.

How can I identify genetic clusters based on the results generated by the AlleleCall module?

We recommend that you use ReporTree to identify genetic clusters based on the allelic profiles (contained in the results_alleles.tsv output file) determined by chewBBACA. ReporTree includes functionalities to identify genetic clusters at any distance threshold level(s), obtain summary reports with relevant statistics computed based on sample metadata, identify regions of cluster stability, etc. Cluster nomenclature can be maintained and updated in subsequent analyses, which is especially useful in surveillance-oriented workflows. Check the publication and the GitHub repository to know more about ReporTree.

Is it possible to perform allele calling without adding novel alleles to the schema?

Yes, you can use the --no-inferred parameter to specify that you do not want to add novel alleles identified during allele calling. This can be useful to avoid contaminating the schema when you are not sure if the genome assemblies are from the same taxon as the schema was created for or if they might be of low quality (e.g. contaminated, highly fragmented, high number of ambiguous bases, etc.).

I would like to inspect the sequences that were not classified during allele calling. Is there an output with information about those sequences?

chewBBACA can create a FASTA file, unclassified_sequences.fasta, with the coding sequences that were not classified if you pass the --output-unclassified parameter.

Is there any information about the sequences classified as non-EXC or non-INF?

chewBBACA can create a FASTA file, missing_classes.fasta, and a TSV file, missing_classes.tsv, with the list of coding sequences classified as ASM/ALM/PLOT3/PLOT5/LOTSC/NIPH/NIPHEM if you pass the --output-missing parameter.

Can I get the list of new alleles identified during allele calling?

The results_alleles.tsv file includes the INF- prefix in the identifiers of newly inferred alleles. chewBBACA can also create a FASTA file, novel_alleles.fasta, with the sequences of all new alleles if you pass the --output-novel parameter.

Are the schemas created with chewBBACA v2 compatible with chewBBACA v3?

To use a schema created with chewBBACA v2 in chewBBACA v3, you need to use the PrepExternalSchema module to convert the schema to a format fully compatible with chewBBACA v3. The adaptation process removes the files terminating in bsr.txt from the short directory and reformats the sequence headers.

Which species already have a training file?

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.

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

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

How should I cite chewBBACA?

If you use chewBBACA, please cite:

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