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from __future__ import print_function
import keras
from keras.models import Sequential, Model, load_model
from keras import backend as K
import tensorflow as tf
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import aparent.visualization as vis
Using TensorFlow backend.
Load APARENT model
-- Load APARENT, which has been trained to predict the isoform abundance and cut profile of a proximal PAS given a fixed background distal PAS (trained on random 3' UTR APA MPRA data).
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#Load base APARENT model
save_dir = os.path.join(os.getcwd(), '../saved_models')
model_name = 'aparent_large_lessdropout_all_libs_no_sampleweights.h5'
model_path = os.path.join(save_dir, model_name)
aparent_model = load_model(model_path)
WARNING:tensorflow:From /home/johli/anaconda3/envs/aparent/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. WARNING:tensorflow:From /home/johli/anaconda3/envs/aparent/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version. Instructions for updating: Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
/home/johli/anaconda3/envs/aparent/lib/python3.6/site-packages/keras/engine/saving.py:292: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.
warnings.warn('No training configuration found in save file: '
Example: PSMC6 Gene variant prediction
-- Predict the isoform log fold change w.r.t the wildtype PAS for every possible SNV).
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#Example PAS sequence from APADB (gene = PSMC6)
#PAS Sequence
seq = 'AGATAGTGGTATAAGAAAGCATTTCTTATGACTTATTTTGTATCATTTGTTTTCCTCATCTAAAAAGTTGAATAAAATCTGTTTGATTCAGTTCTCCTACATATATATTCTTGTCTTTTCTGAGTATATTTACTGTGGTCCTTTAGGTTCTTTAGCAAGTAAACTATTTGATAACCCAGATGGATTGTGGATTTTTGAATATTAT'
#Predict mutation map (single-nucleotide variants)
vis.mut_map(
aparent_model,
seq,
[(100, 'T', 'red')],
seq_start=70-45,
seq_end=70+6+45,
isoform_start=80,
isoform_end=110,
figsize=(14, 6),
height_ratios=[4, 1, 1.5],
logodds_clip=1.0
)
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