CpGenie/HCT116_ENCSR000DFS

Authors: Haoyang Zeng

License: Apache License v2

Contributed by: Roman Kreuzhuber

Cite as: https://doi.org/10.1093/nar/gkx177

Postprocessing: None

Trained on: RRBS (restricted representation bisulfite sequencing) data from ENCODE (https://www.encodeproject.org/)

Abstract: DNA methylation plays a crucial role in the establishment of tissue-specific gene expression and the regulation of key biological processes. However, our present inability to predict the effect of genome sequence variation on DNA methylation precludes a comprehensive assessment of the consequences of non-coding variation. We introduce CpGenie, a sequence-based framework that learns a regulatory code of DNA methylation using a deep convolutional neural network and uses this network to predict the impact of sequence variation on proximal CpG site DNA methylation. CpGenie produces allele-specific DNA methylation prediction with single-nucleotide sensitivity that enables accurate prediction of methylation quantitative trait loci (meQTL). We demonstrate that CpGenie prioritizes validated GWAS SNPs, and contributes to the prediction of functional non-coding variants, including expression quantitative trait loci (eQTL) and disease-associated mutations. CpGenie is publicly available to assist in identifying and interpreting regulatory non-coding variants.

Create a new conda environment with all dependencies installed

kipoi env create CpGenie
source activate kipoi-CpGenie

Test the model

kipoi test CpGenie/HCT116_ENCSR000DFS --source=kipoi

Make a prediction

kipoi get-example CpGenie/HCT116_ENCSR000DFS -o example
kipoi predict CpGenie/HCT116_ENCSR000DFS \
  --dataloader_args='{"intervals_file": "example/intervals_file", "fasta_file": "example/fasta_file"}' \
  -o '/tmp/CpGenie|HCT116_ENCSR000DFS.example_pred.tsv'
# check the results
head '/tmp/CpGenie|HCT116_ENCSR000DFS.example_pred.tsv'

Create a new conda environment with all dependencies installed

kipoi env create CpGenie
source activate kipoi-CpGenie

Get the model

import kipoi
model = kipoi.get_model('CpGenie/HCT116_ENCSR000DFS')

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('CpGenie/HCT116_ENCSR000DFS')

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 CpGenie/HCT116_ENCSR000DFS --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 CpGenie/HCT116_ENCSR000DFS -o /app/example 
docker run -v $PWD/kipoi-example:/app/ kipoi/kipoi-docker:sharedpy3keras1.2-slim \
kipoi predict CpGenie/HCT116_ENCSR000DFS \
--dataloader_args='{'intervals_file': '/app/example/intervals_file', 'fasta_file': '/app/example/fasta_file'}' \
-o '/app/CpGenie_HCT116_ENCSR000DFS.example_pred.tsv' 
# check the results
head $PWD/kipoi-example/CpGenie_HCT116_ENCSR000DFS.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 CpGenie/HCT116_ENCSR000DFS -o example
kipoi predict CpGenie/HCT116_ENCSR000DFS \
--dataloader_args='{"intervals_file": "example/intervals_file", "fasta_file": "example/fasta_file"}' \
-o 'CpGenie_HCT116_ENCSR000DFS.example_pred.tsv' \
--singularity 
# check the results
head CpGenie_HCT116_ENCSR000DFS.example_pred.tsv

Schema

Inputs

Single numpy array

Name: seq

Shape: (4, 1, 1001)

Doc: DNA sequence

Targets

Single numpy array

Name: methylation_prob

Shape: (2,)

Doc: Methylated and Unmethylated probabilities

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

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

Model dependencies

conda:

python=3.7
pysam=0.15.3
pip=20.2.4

pip:

h5py==2.10.0
tensorflow==1.15
keras==1.2.2
protobuf==3.20

Dataloader dependencies

conda:

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

pip:

kipoiseq