deepTarget

Authors: Byunghan Lee , Junghwan Baek , Seunghyun Park , Sungroh Yoon

Version: 0.1

License: GPL-v3

Contributed by: Ziga Avsec

Cite as: https://arxiv.org/pdf/1603.09123.pdf

Trained on:

Type: custom

Postprocessing:

'''deepTarget miRNA target prediction - http://data.snu.ac.kr/pub/deepTarget/ '''

Create a new conda environment with all dependencies installed
kipoi env create deepTarget
source activate kipoi-deepTarget
Install model dependencies into current environment
kipoi env install deepTarget
Test the model
kipoi test deepTarget --source=kipoi
Make a prediction
cd ~/.kipoi/models/deepTarget
kipoi predict deepTarget \
  --dataloader_args='{'mirna_fasta_file': 'example_files/miRNA.fasta', 'mrna_fasta_file': 'example_files/3UTR.fasta', 'query_pair_file': 'example_files/miRNA-mRNA_query.txt'}' \
  -o '/tmp/deepTarget.example_pred.tsv'
# check the results
head '/tmp/deepTarget.example_pred.tsv'
Get the model
import kipoi
model = kipoi.get_model('deepTarget')
Make a prediction for example files
pred = model.pipeline.predict_example()
Use dataloader and model separately
# setup the example dataloader kwargs
dl_kwargs = {'mirna_fasta_file': 'example_files/miRNA.fasta', 'mrna_fasta_file': 'example_files/3UTR.fasta', 'query_pair_file': 'example_files/miRNA-mRNA_query.txt'}
import os; os.chdir(os.path.expanduser('~/.kipoi/models/deepTarget'))
# Get the dataloader and instantiate it
dl = model.default_dataloader(**dl_kwargs)
# get a batch iterator
it = dl.batch_iter(batch_size=4)
# predict for a batch
batch = next(it)
model.predict_on_batch(batch['inputs'])
Make predictions for custom files directly
pred = model.pipeline.predict(dl_kwargs, batch_size=4)
Get the model
library(reticulate)
kipoi <- import('kipoi')
model <- kipoi$get_model('deepTarget')
Make a prediction for example files
predictions <- model$pipeline$predict_example()
Use dataloader and model separately
# Get the dataloader
setwd('~/.kipoi/models/deepTarget')
dl <- model$default_dataloader(mirna_fasta_file='example_files/miRNA.fasta', mrna_fasta_file='example_files/3UTR.fasta', query_pair_file='example_files/miRNA-mRNA_query.txt')
# get a batch iterator
it <- dl$batch_iter(batch_size=4)
# predict for a batch
batch <- iter_next(it)
model$predict_on_batch(batch$inputs)
Make predictions for custom files directly
pred <- model$pipeline$predict(dl_kwargs, batch_size=4)

Schema

Inputs

Dictionary of numpy arrays

Name: mirna_int_seq

    Shape: (40,) 

    Doc: miRNA query sequences in the integer form: A:1, C:2, G:3, T:4, U:4.

Name: mrna_int_seq

    Shape: (40,) 

    Doc: mRNA target sequence in the integer form: A:1, C:2, G:3, T:4, U:4.


Targets

Single numpy array

Name: None

    Shape: (1,) 

    Doc: Prediction probability


Dataloader

Relative path: .

Version: 0.1

Doc: deepTarget miRNA target prediction - http://data.snu.ac.kr/pub/deepTarget/

Authors: Byunghan Lee , Junghwan Baek , Seunghyun Park , Sungroh Yoon

Type: PreloadedDataset

License: GPL-v3


Arguments

mirna_fasta_file : mirna fasta file

mrna_fasta_file : mrna fasta file of interest

query_pair_file : ??mapping between mrna and mirna fasta


Model dependencies
conda:
  • theano==0.8.2
  • numpy==1.10.4

pip:
  • keras==0.3.3

Dataloader dependencies
conda:
  • anaconda::biopython==1.66
  • scikit-learn==0.17.1
  • theano==0.8.2
  • numpy==1.10.4

pip:
  • keras==0.3.3