DeepFlyBrain

Authors: Ibrahim Ihsan Taskiran , Jasper Janssens , Sara Aibar , Stein Aerts

License: MIT

Contributed by: Ibrahim Ihsan Taskiran , Jasper Janssens , Sara Aibar , Stein Aerts

Cite as: TBA

Type: None

Postprocessing: None

Trained on: Accessible genomic sites.

Source files

Specialized deep learning model on Kenyon cells, T neurons, and glia chromatin accessibility data of adult fly brain

Create a new conda environment with all dependencies installed

kipoi env create DeepFlyBrain
source activate kipoi-DeepFlyBrain

Test the model

kipoi test DeepFlyBrain --source=kipoi

Make a prediction

kipoi get-example DeepFlyBrain -o example
kipoi predict DeepFlyBrain \
  --dataloader_args='{"intervals_file": "example/intervals_file", "fasta_file": "example/fasta_file"}' \
  -o '/tmp/DeepFlyBrain.example_pred.tsv'
# check the results
head '/tmp/DeepFlyBrain.example_pred.tsv'

Create a new conda environment with all dependencies installed

kipoi env create DeepFlyBrain
source activate kipoi-DeepFlyBrain

Get the model

import kipoi
model = kipoi.get_model('DeepFlyBrain')

Make a prediction for example files

pred = model.pipeline.predict_example(batch_size=4)

Use dataloader and model separately

# Download example dataloader kwargs
dl_kwargs = model.default_dataloader.download_example('example')
# Get the dataloader and instantiate it
dl = model.default_dataloader(**dl_kwargs)
# get a batch iterator
batch_iterator = dl.batch_iter(batch_size=4)
for batch in batch_iterator:
    # predict for a batch
    batch_pred = 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('DeepFlyBrain')

Make a prediction for example files

predictions <- model$pipeline$predict_example()

Use dataloader and model separately

# Download example dataloader kwargs
dl_kwargs <- model$default_dataloader$download_example('example')
# Get the dataloader
dl <- model$default_dataloader(dl_kwargs)
# get a batch iterator
it <- dl$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)

Get the docker image

docker pull kipoi/kipoi-docker:deepflybrain-slim

Get the full sized docker image

docker pull kipoi/kipoi-docker:deepflybrain

Get the activated conda environment inside the container

docker run -it kipoi/kipoi-docker:deepflybrain-slim

Test the model

docker run kipoi/kipoi-docker:deepflybrain-slim kipoi test DeepFlyBrain --source=kipoi

Make prediction for custom files directly

# Create an example directory containing the data
mkdir -p $PWD/kipoi-example 
# You can replace $PWD/kipoi-example with a different absolute path containing the data 
docker run -v $PWD/kipoi-example:/app/ kipoi/kipoi-docker:deepflybrain-slim \
kipoi get-example DeepFlyBrain -o /app/example 
docker run -v $PWD/kipoi-example:/app/ kipoi/kipoi-docker:deepflybrain-slim \
kipoi predict DeepFlyBrain \
--dataloader_args='{'intervals_file': '/app/example/intervals_file', 'fasta_file': '/app/example/fasta_file'}' \
-o '/app/DeepFlyBrain.example_pred.tsv' 
# check the results
head $PWD/kipoi-example/DeepFlyBrain.example_pred.tsv

Install apptainer

https://apptainer.org/docs/user/main/quick_start.html#quick-installation-steps

Make prediction for custom files directly

kipoi get-example DeepFlyBrain -o example
kipoi predict DeepFlyBrain \
--dataloader_args='{"intervals_file": "example/intervals_file", "fasta_file": "example/fasta_file"}' \
-o 'DeepFlyBrain.example_pred.tsv' \
--singularity 
# check the results
head DeepFlyBrain.example_pred.tsv

Schema

Inputs

List of numpy arrays

Name: None

Shape: (500, 4)

Doc: DNA sequence

Targets

Single numpy array

Name: topic

Shape: (81,)

Doc: Topic Prediction

Dataloader

Defined as: .

Doc: Data-loader returning one-hot encoded sequences given genome intervals

Authors: Ibrahim Ihsan Taskiran

Type: None

License: MIT

Arguments

intervals_file : intervals file bed3

fasta_file : Reference genome FASTA file path.

ignore_targets (optional): if True, don't return any target variables

Model dependencies

conda:

python=3.7
h5py=2.10.0
keras=2.2.4
tensorflow=1.14.0
pip=21.0.1

pip:

Dataloader dependencies

conda:

python=3.7
cython=0.29.23
bioconda::pybedtools=0.8.2
bioconda::pysam=0.16.0.1
bioconda::pyfaidx=0.6.1
numpy=1.19.5
pandas=1.1.5

pip:

kipoiseq