plasflow_processing

Scripts used during the development of PlasFlow.

All preprocessing steps producing kmer counts used for the neural network training can be run using command:

  bash PlasFlow_preprocessing.sh

Alternatively, all steps can be ran individually:

Use get_refseq_genomes_complete.sh to download complete genomes (Archaea and Bacteria)
Run get_refseq_catalog_filter.sh to get accessions numbers of each downloaded sequence and filtered RefSeq catalog files
Run Rscript create_annotation_of_data.R to create the annotation table based on the RefSeq catalog (filtered to contain only downloaded sequences).
- Uses rentrez to collect data from NCBI - may be slow
Run RScript generate_fragments_for_training.R
- May require large amounts of RAM - especially for longer kmers (hexamers, heptamers)
Run training using PlasFlow_train.py script - requires TensorFlow 0.10.0 (see requirements for PlasFlow)
- As an input kmern_split_raw_counts_tax_classes_numeric_annotated_filtered_tax.tsv files should be used (where n is a kmer length)
- Typical invocation of a script:
```
python PlasFlow_train.py --input kmern_split_raw_counts_tax_classes_numeric_annotated_filtered_tax.tsv --hidden1 20 --hidden2 10 --activation relu --modeldir kmern_split_20_10_neurons_relu --steps 50000
```
will run training on specified file, using 20 neurons in first hidden layer and 10 neurons in second hidden layer, using relu activation function and 50000 steps of training. Model will be saved in the kmern_split_20_10_neurons_relu folder

smaegol/plasflow_processing