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View Code? Open in Web Editor NEWTAD-aware annotation of CNVs
License: MIT License
TAD-aware annotation of CNVs
License: MIT License
Dear TADA team,
I'm having some problems with the use of the function: "predict variants"
I followed two different approaches:
A) I used data available from the folder "tests" and I ran the code below:
predict_variants -c tests/test_config_pred.yml -o .
I got the following error:
Traceback (most recent call last):
File "/home/frequena/.conda/envs/py3.6/bin/predict_variants", line 11, in <module>
load_entry_point('tada==0.2', 'console_scripts', 'predict_variants')()
File "/home/frequena/.conda/envs/py3.6/lib/python3.6/site-packages/tada-0.2-py3.6.egg/tada/predict_variants.py", line 79, in main
predict(cfg, output)
File "/home/frequena/.conda/envs/py3.6/lib/python3.6/site-packages/tada-0.2-py3.6.egg/tada/predict_variants.py", line 53, in predict
for label, annotated_cnvs in labeled_cnv_dicts:
UnboundLocalError: local variable 'labeled_cnv_dicts' referenced before assignment
B) I created a .bed file with a single CNV:
1 126439621 135430043
....I annotated it correctly and I got a file with the name: Annotated_PATHOGENIC.csv
Next, I ran predict_variants
predict_variants -c config_del_default.yml -o .
...and I got the following error:
File "/home/frequena/.conda/envs/py3.6/bin/predict_variants", line 11, in <module>
load_entry_point('tada==0.2', 'console_scripts', 'predict_variants')()
File "/home/frequena/.conda/envs/py3.6/lib/python3.6/site-packages/tada-0.2-py3.6.egg/tada/predict_variants.py", line 79, in main
predict(cfg, output)
File "/home/frequena/.conda/envs/py3.6/lib/python3.6/site-packages/tada-0.2-py3.6.egg/tada/predict_variants.py", line 34, in predict
cnv_dicts.append(pickle.load(cnv_dict))
_pickle.UnpicklingError: unpickling stack underflow
This is the config file used:
TADS:
RAW: "data/Dixon_2015_stability_formatted_TADs.bed"
ANNOTATED: "data/Annotated_Default_TADs.p"
ANNOTATIONS:
GENES: "data/gnomad_genes_pli_loeuf_HI.bed"
EXONS: "data/HAVANA_exon.merged.bed.gz"
ENHANCERS: "data/fantom5_enhancer_phastcon_average.bed"
CTCF: "data/H1_hESC_CTCF_peaks_idr_optimal.bed"
DDG2P: "data/DDG2P_genes.bed"
POINT: "data/extracted_po_pairs.bed"
CNVS:
RAW:
NON_PATHOGENIC: "./cnv_test.bed"
PATHOGENIC: "./cnv_test.bed"
ANNOTATED:
NON_PATHOGENIC: "Annotated_PATHOGENIC.csv"
PATHOGENIC: "Annotated_PATHOGENIC.csv"
FEATURES: "extended"
CLASSIFIER: "rf"
KWARGS:
max_depth: None
max_features: 'auto'
min_samples_leaf: 5
min_samples_split: 4
n_estimators: 500
oob_score: True
PRETRAINED_MODEL:
Please let me know if you need any further detail!
Thank you!
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