BPNet_Dmel_OreR_2to3hr_ZDTBCG

Authors: Melanie Weilert and Kaelan Brennan

License: MIT

Contributed by: Melanie Weilert and Kaelan Brennan

Cite as: https://doi.org/10.1101/2022.12.20.520743

Type: None

Postprocessing: None

Trained on: 2-3hr OreR D.mel embryos (validation chrom=chr2L, test chrom=chrX)

Source files

BPNet model predicting the ChIP-nexus profiles of Zelda, Dorsal, Twist, GAGA-factor, Caudal and Bicoid in 2-3hr OreR D.mel embryos.

Create a new conda environment with all dependencies installed
kipoi env create BPNet_Dmel_OreR_2to3hr_ZDTBCG
source activate kipoi-BPNet_Dmel_OreR_2to3hr_ZDTBCG
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Test the model
kipoi test BPNet_Dmel_OreR_2to3hr_ZDTBCG --source=kipoi
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Make a prediction
kipoi get-example BPNet_Dmel_OreR_2to3hr_ZDTBCG -o example
kipoi predict BPNet_Dmel_OreR_2to3hr_ZDTBCG \
  --dataloader_args='{"intervals_file": "example/intervals_file", "fasta_file": "example/fasta_file"}' \
  -o '/tmp/BPNet_Dmel_OreR_2to3hr_ZDTBCG.example_pred.tsv'
# check the results
head '/tmp/BPNet_Dmel_OreR_2to3hr_ZDTBCG.example_pred.tsv'
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Create a new conda environment with all dependencies installed
kipoi env create BPNet_Dmel_OreR_2to3hr_ZDTBCG
source activate kipoi-BPNet_Dmel_OreR_2to3hr_ZDTBCG
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Get the model
import kipoi
model = kipoi.get_model('BPNet_Dmel_OreR_2to3hr_ZDTBCG')
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Make a prediction for example files
pred = model.pipeline.predict_example(batch_size=4)
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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'])
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Make predictions for custom files directly
pred = model.pipeline.predict(dl_kwargs, batch_size=4)
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Get the model
library(reticulate)
kipoi <- import('kipoi')
model <- kipoi$get_model('BPNet_Dmel_OreR_2to3hr_ZDTBCG')
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Make a prediction for example files
predictions <- model$pipeline$predict_example()
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Use dataloader and model separately
# Download example dataloader kwargs
dl_kwargs <- model$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$inputs)
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Make predictions for custom files directly
pred <- model$pipeline$predict(dl_kwargs, batch_size=4)
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Get the docker image
Not available yet
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Get the full sized docker image
Not available yet
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Get the activated conda environment inside the container
Not available yet
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Test the model
Not available yet
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Make prediction for custom files directly
Not available yet
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Install apptainer
https://apptainer.org/docs/user/main/quick_start.html#quick-installation-steps
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Make prediction for custom files directly
Not available yet
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Schema

Inputs

Single numpy array

Name: None

    Shape: (1000, 4) 

    Doc: One-hot encoded DNA sequence.

Targets

Dictionary of numpy arrays

Name: Bcd

    Shape: (1000, 2) 

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

Name: Cad

    Shape: (1000, 2) 

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

Name: Dl

    Shape: (1000, 2) 

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

Name: GAF

    Shape: (1000, 2) 

    Doc: Strand-specific ChIP-nexus data for GAGA-factor.

Name: Twi

    Shape: (1000, 2) 

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

Name: Zld

    Shape: (1000, 2) 

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

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