DeepCpG_DNA/Smallwood2014_serum_dna

Authors: Christof Angermueller

License: MIT

Contributed by: Roman Kreuzhuber

Cite as: https://doi.org/10.1186/s13059-017-1189-z
https://doi.org/10.5281/zenodo.1094823

Postprocessing: None

Trained on: scBS-seq and scRRBS-seq datasets, https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1189-z#Sec7

Source files

This is the extraction of the DNA-part of the a pretrained model by Christof Angermueller. The DeepCpG models are trained on: scBS-seq-profiled cells contained 18 serum and 12 2i mESCs, which were pre-processed as described in Smallwood et al. (2014), with reads mapped to the GRCm38 mouse genome. Two serum cells (RSC27_4, RSC27_7) were excluded since their methylation pattern deviated strongly from the remaining serum cells. scRRBS-seq-profiled cells were downloaded from the Gene Expression Omnibus (GEO; GSE65364) and contained 25 human HCCs, six human heptoplastoma-derived cells (HepG2) and six mESCs. Following Hou et al. (2013), one HCC was excluded (Ca26) and the analysis was restricted to CpG sites that were covered by at least four reads. For HCCs and HepG2 cells, the position of CpG sites was lifted from GRCh37 to GRCh38, and for mESC cells from NCBIM37 to GRCm38, using the liftOver tool from the UCSC Genome Browser.

Create a new conda environment with all dependencies installed

kipoi env create DeepCpG_DNA/Smallwood2014_serum_dna
source activate kipoi-DeepCpG_DNA__Smallwood2014_serum_dna

Test the model

kipoi test DeepCpG_DNA/Smallwood2014_serum_dna --source=kipoi

Make a prediction

kipoi get-example DeepCpG_DNA/Smallwood2014_serum_dna -o example
kipoi predict DeepCpG_DNA/Smallwood2014_serum_dna \
  --dataloader_args='{"fasta_file": "example/fasta_file", "intervals_file": "example/intervals_file"}' \
  -o '/tmp/DeepCpG_DNA|Smallwood2014_serum_dna.example_pred.tsv'
# check the results
head '/tmp/DeepCpG_DNA|Smallwood2014_serum_dna.example_pred.tsv'

Create a new conda environment with all dependencies installed

kipoi env create DeepCpG_DNA/Smallwood2014_serum_dna
source activate kipoi-DeepCpG_DNA__Smallwood2014_serum_dna

Get the model

import kipoi
model = kipoi.get_model('DeepCpG_DNA/Smallwood2014_serum_dna')

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('DeepCpG_DNA/Smallwood2014_serum_dna')

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:sharedpy3keras1.2-slim

Get the full sized docker image

docker pull kipoi/kipoi-docker:sharedpy3keras1.2

Get the activated conda environment inside the container

docker run -it kipoi/kipoi-docker:sharedpy3keras1.2-slim

Test the model

docker run kipoi/kipoi-docker:sharedpy3keras1.2-slim kipoi test DeepCpG_DNA/Smallwood2014_serum_dna --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:sharedpy3keras1.2-slim \
kipoi get-example DeepCpG_DNA/Smallwood2014_serum_dna -o /app/example 
docker run -v $PWD/kipoi-example:/app/ kipoi/kipoi-docker:sharedpy3keras1.2-slim \
kipoi predict DeepCpG_DNA/Smallwood2014_serum_dna \
--dataloader_args='{'fasta_file': '/app/example/fasta_file', 'intervals_file': '/app/example/intervals_file'}' \
-o '/app/DeepCpG_DNA_Smallwood2014_serum_dna.example_pred.tsv' 
# check the results
head $PWD/kipoi-example/DeepCpG_DNA_Smallwood2014_serum_dna.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 DeepCpG_DNA/Smallwood2014_serum_dna -o example
kipoi predict DeepCpG_DNA/Smallwood2014_serum_dna \
--dataloader_args='{"fasta_file": "example/fasta_file", "intervals_file": "example/intervals_file"}' \
-o 'DeepCpG_DNA_Smallwood2014_serum_dna.example_pred.tsv' \
--singularity 
# check the results
head DeepCpG_DNA_Smallwood2014_serum_dna.example_pred.tsv

Schema

Inputs

Dictionary of numpy arrays

Name: dna

Shape: (1001, 4)

Doc: DNA sequence

Targets

List of numpy arrays

Name: cpg/BS27_1_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS27_1_SER

Name: cpg/BS27_3_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS27_3_SER

Name: cpg/BS27_5_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS27_5_SER

Name: cpg/BS27_6_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS27_6_SER

Name: cpg/BS27_8_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS27_8_SER

Name: cpg/BS28_10_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS28_10_SER

Name: cpg/BS28_1_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS28_1_SER

Name: cpg/BS28_2_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS28_2_SER

Name: cpg/BS28_3_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS28_3_SER

Name: cpg/BS28_4_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS28_4_SER

Name: cpg/BS28_6_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS28_6_SER

Name: cpg/BS29_1_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS29_1_SER

Name: cpg/BS29_4_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS29_4_SER

Name: cpg/BS29_5_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS29_5_SER

Name: cpg/BS29_6_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS29_6_SER

Name: cpg/BS29_7_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS29_7_SER

Name: cpg/BS29_8_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS29_8_SER

Name: cpg/BS29_9_SER

Shape: (None, 1)

Doc: Methylation probability for cpg/BS29_9_SER

Dataloader

Defined as: .

Doc: Dataloader for the DeepCpG.

Authors: Ziga Avsec , Roman Kreuzhuber

Type: Dataset

License: MIT

Arguments

fasta_file : Reference genome sequence

intervals_file : bed3 file with `chrom start end id score strand`

Model dependencies

conda:

python=3.7
h5py=2.10.0
pip=20.2.4

pip:

tensorflow==1.13.1
keras==1.2.2
protobuf==3.20

Dataloader dependencies

conda:

bioconda::genomelake=0.1.4
bioconda::pybedtools=0.8.1
python=3.7
numpy=1.19.2
pandas=1.1.3

pip: