Integrations

The files written by GECCO are standard TSV and GenBank files, so they should be easy to use in downstream analyses. However, some common use-cases are already covered to reduce the need for custom scripts.

AntiSMASH

Since v0.7.0, GECCO can natively output JSON files that can be loaded into the AntiSMASH viewer as external annotations. To do so, simply run your analysis with the --antismash-sideload option to generate an additional file:

$ gecco run -g KC188778.1.gbk -o output_dir --antismash-sideload

The output folder will contain an additional JSON file compared to usual runs:

$ tree output_dir
output_dir
├── KC188778.1_cluster_1.gbk
├── KC188778.1.clusters.tsv
├── KC188778.1.features.tsv
└── KC188778.1.sideload.json

0 directories, 4 files

That JSON file can be loaded into the AntiSMASH result viewer. Check Upload extra annotations, and upload the *.sideload.json file:

When AntiSMASH is done processing your sequences, the Web viewer will display BGCs found by GECCO as subregions next to the AntiSMASH clusters.

GECCO-specific metadata (such as the probability of the predicted type) and configuration (recording the --threshold and --cds values passed to the gecco run command) can be seen in the dedicated GECCO tab.

BiG-SLiCE

GECCO outputs GenBank files that only contain standard features, but BiG-SLiCE requires additional metadata to load BGCs for analysis.

Since v0.7.0, the gecco convert subcommand can convert GenBank files obtained with a typical GECCO run into files than can be loaded by BiG-SLiCE. Just run the command after gecco run using the same folder as the input:

$ gecco run -g KY646191.1.gbk -o bigslice_dir/dataset_1/KY646191.1/
$ gecco convert gbk -i bigslice_dir/dataset_1/KY646191.1/ --format bigslice

This will create a new region file for each GenBank file, which will be detected by BiG-SLiCE. Provided you organised the folders in the appropriate structure, it should look like this:

$ tree bigslice_dir
bigslice_dir
├── dataset_1
│   └── KC188778.1
│       ├── KC188778.1_cluster_1.gbk
│       ├── KC188778.1.clusters.tsv
│       ├── KC188778.1.features.tsv
│       └── KC188778.1.region1.gbk
├── datasets.tsv
└── taxonomy
    └── dataset_1_taxonomy.tsv

3 directories, 6 files

BiG-SLiCE will be able to load and render the BGCs found by GECCO:

Warning

Because of the way BiG-SLiCE loads BGCs coming from GECCO, they are always marked as being fragmented.