Usage#

ggCaller has two main modes: Gene-calling and Querying.

Gene-calling#

Gene-calling predicts and annotates genes within a pangenome de Bruijn Graph (DBG), before conducting orthologue clustering and pangenome analysis using Panaroo.

Predicting genes#

To generate an input for ggCaller, create a directory containing of all the sequences you wish to analyses. We recommend placing all samples of the same type in a single directory; place read and assembly files in separate directories.

Important

Ensure you have write access to the directories where the FASTA/FASTQ files are saved, as ggCaller saves intermediate FMINDEX files in the same locations.

If not using Docker, generate the input file for ggCaller, navigate inside the directory containing the genomes, and run:

ls -d -1 $PWD/*.fasta > input.txt

If using Docker, you must navigate to the directory containing the fasta files and run:

ls -d -1 *.fasta > input.txt

This will generate a list of all the .fasta files in the directory. Change this extension as required.

Important

All of the below commands can be run with docker installations, however they must be run as: docker run --rm -it -v $(pwd):/workdir samhorsfield96/ggcaller:latest ggcaller <commands>. This command must be run within the same directory as the .fasta files and input.txt. All paths provided must be relative, as absolute paths will not work within the docker container.

DBG building with reads or assemblies is different, with k-mers that appear only once being removed from the graph. Therefore it is important to specify whether input.txt contains reads or assemblies.

Important

Assemblies with many Ns generate disjointed DBGs leading to underclustering. To ensure optimal performance, avoid using assemblies containing Ns.

To run ggCaller with just assemblies:

ggcaller --refs input.txt

To run ggCaller with just reads:

ggcaller --reads input.txt

To run ggCaller with reads and assemblies:

ggcaller --refs input1.txt --reads input2.txt

Important

We haven’t extensively tested calling genes within read datasets yet. Exercise caution when interpreting results.

ggCaller can also be run on a pre-built Bifrost DBG and its associated colours file:

ggcaller --graph input.gfa --colours colours.color.bfg

This assumes all sequences used to build the graph are assemblies. If only some sequences are assemblies and the rest are reads, specify which files are references using --refs:

ggcaller --graph input.gfa --colours colours.color.bfg --refs input1.txt

If all sequences are reads, specify --not-ref:

ggcaller --graph input.gfa --colours colours.color.bfg --not-ref

Results from all commands above will be saved to a directory called ggCaller_output by default. To change this, specify --out <path>. Note that ggCaller will overwrite results if an already existing directory is specified.

By default, ggCaller will generate:

  • Predicted genes (nucleotide and amino-acid) in FASTA format

  • Gene presence/absence matrix in CSV and RTAB formats

  • Pre/post Panaroo quality control gene graphs in GML format

  • Structural variant presence/absence in RTAB format

  • Summary graph: gene frequency, cluster size and rarefaction curve

  • Roary-style gene frequency statistics

  • A pangenome reference FASTA, containing all cluster centroids

  • A gene presence/absence neighbour joining tree in NWK format

Additionally, ggCaller generates some intermediate files:

  • Two Bifrost files, a GFA file and BFG_COLORS file, with the same file path as input.txt

  • FMINDEX files for each of the sample FASTAs with the same file path the input files.

Annotating genes#

ggCaller comes with two default databases for functional annotation of genes. - Bacterial and Viral databases from Uniprot, used by DIAMOND - HMM profiles from Prokka, used by HMMER3

Important

Ensure you are connected to the internet when first running ggCaller as these databases are downloaded automatically. Subsequent runs can be conducted offline.

There are three sensitivity levels for annotation:

  • fast: only DIAMOND in fast mode

  • sensitive: only DIAMOND in sensitive mode

  • ultrasensitive: HMMER3 and DIAMOND in sensitive mode

For example, to run DIAMOND only in fast mode, run:

ggcaller --refs input.txt --annotation fast

By default these commands will annotate using DIAMOND with the Bacteria uniprot database. To change this to the Viruses database, run:

ggcaller --refs input.txt --annotation fast --diamonddb Viruses

Custom databases can also be specified for both DIAMOND using --diamonddb and HMMER3 using --hmmdb. DIAMOND databases must be amino-acid FASTA files. HMMER3 databases must be HMM-profile .HAMAP files built using hmmbuild which is part of the HMMER3 package.

To run with custom DIAMOND and HMMER3 databases:

ggcaller --refs input.txt --annotation ultrasensitive --diamonddb annotation.fasta --hmmdb annotation.HAMAP

Annotation is not on by default. If annotation is specified, ggCaller will additionally generate:

  • GFF files for each input genome in a separate directory GFF

  • Annotations will be added to gene call FASTA files

Aligning genes#

ggCaller also supports generation of within-cluster and core genome alignments using MAFFT.

There are two alignment algorithms implemented:

  • def or default, which uses the standard MAFFT multiple sequence alignment algorithm. This is faster when aligning <=500 sequences in a cluster.

  • ref or reference, which uses reference-guided alignment. This is faster when aligning >500 sequences in a cluster.

There are also two modes for alignment:

  • core aligns genes only within core clusters, and generates a concatenated core genome alignment.

  • pan aligns genes within all clusters (pangenome alignment), as well as generating a concatenated core genome alignment.

To generate a core genome alignment using default MAFFT, run:

ggcaller --refs input.txt --aligner def --alignment core

To generate a pangenome alignment using reference-guided MAFFT, run:

ggcaller --refs input.txt --aligner ref --alignment pan

To change the frequency of genes deemed to be core, use –core-threshold (default = 0.95, or 95% frequency). For example, only include genes found at 100% frequency:

ggcaller --refs input.txt --aligner def --alignment core --core-threshold 1.0

Alignment is off by default. If specified, ggCaller will additionally generate:

  • Core genome alignment in FASTA format

  • Core genome Neighbour-joining tree in NWK format

  • Per-cluster alignment files in FASTA format in a separate directory aligned_gene_sequences

  • Per-cluster VCF file generated by SNP-SITES in separate directory VCF

Quality control and clustering#

ggCaller implements Panaroo to identify spurious clusters that are generated by assembly fragmentation and contamination.

Panaroo identifies spurious clusters as those with <2 edges in the gene graph. Spurious clusters are then removed based on their population frequency, determined by three settings:

  • strict; remove spurious clusters with <5% frequency. Good for datasets >100 genomes where rare plasmids are not expected.

  • moderate; remove spurious clusters with <1% frequency (default). Good for datasets <=100 genomes where rare plasmids are not expected.

  • sensitive; do not remove clusters. Good for datasets where rare plasmids are expected.

For example, to run ggCaller in strict mode:

ggcaller --refs input.txt --clean-mode strict

More information can be found here.

If you use the full pipeline of ggCaller, also please cite Panaroo.

Querying#

Querying maps a set of query DNA sequences to an annotated DBG, identifying genes that the query overlaps with.

Saving datastructures#

Annotate a DBG as before, adding the --save flag. This will write the intermediate datastructures containing DBG coordinates of the predicted genes to a directory called ggc_data.

Important

We suggest using an annotation database, either the default ones provided or a custom one, as this will enable better functional analysis of your queries.

For example, run with sensitive annotation and save intermediate files:

ggcaller --refs input.txt --annotation sensitive --save

Querying the DBG#

Queries sequences can either be in multi-FASTA format, or in a single file with each sequence on its own line.

Provide paths to the DBG .gfa and .color.bfg files, the ggc_data directory and query file:

ggcaller --query queries.fasta --graph inputs.gfa --colours inputs.color.bfg --data ggCaller_output/ggc_data

By default, mapped queries >=80% matching k-mers to a given colour will be returned. This can be changed using --query-id flag.

To return queries with 100% match:

ggcaller --query queries.fasta --graph inputs.gfa --colours inputs.color.bfg --data ggCaller_output/ggc_data --query-id 1.0

Interpreting results#

Results will be output in matched_queries.fasta in the specified output directory. This is a multi-FASTA file describing all annotated genes that overlap with the query sequences.

An example format is below:

>Isolate10_9298 ggcID=10_9298 QUERY=Query_A;Query_B annotation=FUNCTION A;FUNCTION B;
ATGTTAAATAAAGTCAAAACTAAAGCCTTAATTAGTGTCGGAGCAGTGGCTGCAACTAGCTAG

The header contains:

  • Sample name and gene number (Isolate10_9298)

  • ggCaller identifier (ggcID field)

  • Mapped query sequences or IDs (QUERY field) separated by semi-colons. These will be fasta IDs if queries file is a FASTA, otherwise DNA sequence.

  • Annotation(s) (annotation field) separated by semi-colons

Parallelisation#

ggCaller is fully parallelised using OpenMP and python multiprocessing. By default ggCaller runs single-threaded.

To specify the number of threads:

ggcaller --refs input.txt --threads 8