BPNet-OSKN

Authors: Ziga Avsec

License: MIT

Contributed by: Ziga Avsec

Cite as: https://doi.org/10.1101/737981

Type: None

Postprocessing: None

Trained on: ChIP-nexus data in mm10. test chromosomes 1, 8, 9, validation chromosomes 2, 3, 4

Source files

BPNet model predicting the ChIP-nexus profiles of Oct4, Sox2, Nanog and Klf4

Create a new conda environment with all dependencies installed
kipoi env create BPNet-OSKN
source activate kipoi-BPNet-OSKN
Test the model
kipoi test BPNet-OSKN --source=kipoi
Make a prediction
kipoi get-example BPNet-OSKN -o example
kipoi predict BPNet-OSKN \
  --dataloader_args='{"intervals_file": "example/intervals_file", "fasta_file": "example/fasta_file"}' \
  -o '/tmp/BPNet-OSKN.example_pred.tsv'
# check the results
head '/tmp/BPNet-OSKN.example_pred.tsv'
Create a new conda environment with all dependencies installed
kipoi env create BPNet-OSKN
source activate kipoi-BPNet-OSKN
Get the model
import kipoi
model = kipoi.get_model('BPNet-OSKN')
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$get_model('BPNet-OSKN')
Make a prediction for example files
predictions <- model$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:bpnet-oskn-slim
Get the full sized docker image
docker pull kipoi/kipoi-docker:bpnet-oskn
Get the activated conda environment inside the container
docker run -it kipoi/kipoi-docker:bpnet-oskn-slim
Test the model
docker run kipoi/kipoi-docker:bpnet-oskn-slim kipoi test BPNet-OSKN --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:bpnet-oskn-slim \
kipoi get-example BPNet-OSKN -o /app/example 
docker run -v $PWD/kipoi-example:/app/ kipoi/kipoi-docker:bpnet-oskn-slim \
kipoi predict BPNet-OSKN \
--dataloader_args='{'intervals_file': '/app/example/intervals_file', 'fasta_file': '/app/example/fasta_file'}' \
-o '/app/BPNet-OSKN.example_pred.tsv' 
# check the results
head $PWD/kipoi-example/BPNet-OSKN.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 BPNet-OSKN -o example
kipoi predict BPNet-OSKN \
--dataloader_args='{"intervals_file": "example/intervals_file", "fasta_file": "example/fasta_file"}' \
-o 'BPNet-OSKN.example_pred.tsv' \
--singularity 
# check the results
head BPNet-OSKN.example_pred.tsv

Schema

Inputs

Single numpy array

Name: None

    Shape: (1000, 4) 

    Doc: One-hot encoded DNA sequence.


Targets

Dictionary of numpy arrays

Name: Oct4

    Shape: (1000, 2) 

    Doc: Strand-specific ChIP-nexus data for Oct4.

Name: Sox2

    Shape: (1000, 2) 

    Doc: Strand-specific ChIP-nexus data for Sox2.

Name: Nanog

    Shape: (1000, 2) 

    Doc: Strand-specific ChIP-nexus data for Nanog.

Name: Klf4

    Shape: (1000, 2) 

    Doc: Strand-specific ChIP-nexus data for Klf4.


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.

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


Model dependencies
conda:
  • python=3.7
  • bioconda::pybedtools>=0.7.10
  • bioconda::bedtools>=2.27.1
  • bioconda::pybigwig>=0.3.10
  • bioconda::pysam>=0.14.0
  • bioconda::genomelake==0.1.4
  • pytorch::pytorch=1.4.0
  • cython=0.29.22
  • h5py=2.10.0
  • numpy=1.19.2
  • pandas=1.1.5
  • fastparquet=0.5.0
  • python-snappy=0.6.0
  • pip=21.0.1
  • nb_conda=2.2.1
  • tensorflow=1.14
  • keras=2.2.4

pip:
  • git+https://github.com/kundajelab/DeepExplain.git
  • git+https://github.com/kundajelab/bpnet.git@0cb7277b736260f8b4084c9b0c5bd62b9edb5266
  • protobuf==3.20

Dataloader dependencies
conda:
  • bioconda::pybedtools
  • bioconda::pyfaidx
  • bioconda::pyranges
  • numpy
  • pandas

pip:
  • kipoiseq