DeepSTARR

Authors: Bernardo P. de Almeida

License: MIT

Contributed by: Bernardo P. de Almeida

Cite as: https://doi.org/10.1101/2021.10.05.463203

Type: None

Postprocessing: None

Trained on: Developmental and housekeeping quantitative enhancer activity. Held-out second half of chromosome 2R.

Source files

Model predicting the activities of developmental and housekeeping enhancers in Drosophila S2 cells

Create a new conda environment with all dependencies installed

kipoi env create DeepSTARR
source activate kipoi-DeepSTARR

Test the model

kipoi test DeepSTARR --source=kipoi

Make a prediction

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

Create a new conda environment with all dependencies installed

kipoi env create DeepSTARR
source activate kipoi-DeepSTARR

Get the model

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

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('DeepSTARR')

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:deepstarr-slim

Get the full sized docker image

docker pull kipoi/kipoi-docker:deepstarr

Get the activated conda environment inside the container

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

Test the model

docker run kipoi/kipoi-docker:deepstarr-slim kipoi test DeepSTARR --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:deepstarr-slim \
kipoi get-example DeepSTARR -o /app/example 
docker run -v $PWD/kipoi-example:/app/ kipoi/kipoi-docker:deepstarr-slim \
kipoi predict DeepSTARR \
--dataloader_args='{'intervals_file': '/app/example/intervals_file', 'fasta_file': '/app/example/fasta_file'}' \
-o '/app/DeepSTARR.example_pred.tsv' 
# check the results
head $PWD/kipoi-example/DeepSTARR.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 DeepSTARR -o example
kipoi predict DeepSTARR \
--dataloader_args='{"intervals_file": "example/intervals_file", "fasta_file": "example/fasta_file"}' \
-o 'DeepSTARR.example_pred.tsv' \
--singularity 
# check the results
head DeepSTARR.example_pred.tsv

Schema

Inputs

Single numpy array

Name: None

Shape: (249, 4, 1)

Doc: DNA sequence

Targets

Single numpy array

Name: None

Shape: (2,)

Doc: Developmental and housekeeping enhancer activity

Dataloader

Defined as: kipoiseq.dataloaders.SeqIntervalDl

Doc: Dataloader for a combination of fasta and tab-delimited input files such as bed files. The dataloader extracts regions from the fasta file as defined in the tab-delimited `intervals_file` and converts them into one-hot encoded format. Returned sequences are of the type np.array with the shape inferred from the arguments: `alphabet_axis` and `dummy_axis`.

Authors: Ziga Avsec , Roman Kreuzhuber

Type: Dataset

License: MIT

Arguments

intervals_file : bed3+<columns> file path containing intervals + (optionally) labels

fasta_file : Reference genome FASTA file path.

num_chr_fasta (optional): True, the the dataloader will make sure that the chromosomes don't start with chr.

label_dtype (optional): None, datatype of the task labels taken from the intervals_file. Example: str, int, float, np.float32

use_strand (optional): reverse-complement fasta sequence if bed file defines negative strand. Requires a bed6 file

Model dependencies

conda:

python=3.8
h5py=3.6.0
pip=22.0.4
keras=2.7.0
tensorflow=2.7.0

pip:

protobuf==3.20

Dataloader dependencies

conda:

bioconda::pybedtools
bioconda::pyfaidx
bioconda::pyranges
numpy
pandas

pip:

kipoiseq