Step 3.3: Untargeted mode

This approach does the following:

1. generates multiple models, one per truth set for the training set, from single window data;
2. scans each chromosome array (in 200 bp non-overlapping bins) to generate the predictions for SV_start, SV_end and noSV;
3. combine the predictions and compare the results with the truth set of the test set.

The scripts to run are summarized in this SGE script:

T0_S1_generate_training_data.py The first argument of this script is either:

• 'positive': generate the positive set, with labels SV_start and SV_end
• 'negative': generate the negative set, with label noSV The other arguments are:
• chrlist: list of chromosome to generate the positive/negative set for;

...to be finished