Deepbgc

- Added DEEPBGC_DOWNLOADS_DIR info to download command
- Added default values to help annotations
- Fixed pfam description annotation, now pfams are annotated with text "description" qualifier

See https://github.com/Merck/deepbgc#train-deepbgc-on-your-own-data for more information about training.

- GeneSwap_Negatives.pfam.tsv - Generated artificial negatives used to train the DeepBGC model
- MIBiG.activity.csv - Chemical product activity for all MIBiG 1.4 BGCs
- MIBiG.classes.csv - Chemical product class for all MIBiG 1.4 BGCs
- MIBiG.pfam.tsv - Sequence of Pfam domains of all MIBiG 1.4 BGCs used to train the DeepBGC model
- pfam2vec.csv - Pfam2vec embedding (100-dimensional vectors) for all Pfam domain IDs
- templates - Directory with JSON model templates for training
- pfam2vec-pfam31-corpus-p0.001.txt.bz2 - **NEW** Pfam ID corpus used to train pfam2vec (p-value 0.001, original pfam2vec was trained with a less strict p-value of 0.01). Compressed using bzip2.

Models

Models are downloaded automatically using `deepbgc download`

- deepbgc.pkl - DeepBGC detection model trained on MIBiG 1.4 dataset
- clusterfinder_original.pkl - ClusterFinder detection model with original parameters
- clusterfinder_retrained.pkl - ClusterFinder detection model, trained on MIBiG 1.4 dataset
- clusterfinder_geneborder.pkl - ClusterFinder model switching only on gene borders, trained on MIBiG 1.4 dataset
- product_class.pkl - Random Forest classifier predicting product class, trained on MIBiG 1.4 dataset
- product_activity.pkl - Random Forest classifier predicting product activity, trained on MIBiG 1.4 dataset

Example results
- example - Result of full DeepBGC pipeline on ClusterFinder_Annotated_Contigs.full.gbk
- DeepBGC_Example_Result.ipynb - Jupyter notebook previewing contents of the example result folder

Changelog

- DeepBGC now accepts and outputs GenBank files
- You can now train your own BGC detection model using `deepbgc train`
- Data dependencies and models are now automatically downloaded using `deepbgc download`
- Compatibility with Python 2.7, Python 3.4+

Training and validation data

Code release including trained models.