Basset

Authors: David R. Kelley

License: MIT

Contributed by: Roman Kreuzhuber

Cite as: https://doi.org/10.1101/gr.200535.115

Trained on: From 2,071,886 total sites, 71,886 randomly reserved for testing and 70,000 for validation, leaving 1,930,000 for training.

Source files

This is the Basset model published by David Kelley converted to pytorch by Roman Kreuzhuber. It categorically predicts probabilities of accesible genomic regions in 164 cell types (ENCODE project and Roadmap Epigenomics Consortium). Data was generated using DNAse-seq. The sequence length the model uses as input is 600bp. The input of the tensor has to be (N, 4, 600, 1) for N samples, 600bp window size and 4 nucleotides. Per sample, 164 probabilities of accessible chromatin will be predicted.

Create a new conda environment with all dependencies installed

kipoi env create Basset
source activate kipoi-Basset

Test the model

kipoi test Basset --source=kipoi

Make a prediction

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

Create a new conda environment with all dependencies installed

kipoi env create Basset
source activate kipoi-Basset

Get the model

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

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('Basset')

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:sharedpy3keras2tf2-slim

Get the full sized docker image

docker pull kipoi/kipoi-docker:sharedpy3keras2tf2

Get the activated conda environment inside the container

docker run -it kipoi/kipoi-docker:sharedpy3keras2tf2-slim

Test the model

docker run kipoi/kipoi-docker:sharedpy3keras2tf2-slim kipoi test Basset --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:sharedpy3keras2tf2-slim \
kipoi get-example Basset -o /app/example 
docker run -v $PWD/kipoi-example:/app/ kipoi/kipoi-docker:sharedpy3keras2tf2-slim \
kipoi predict Basset \
--dataloader_args='{'intervals_file': '/app/example/intervals_file', 'fasta_file': '/app/example/fasta_file'}' \
-o '/app/Basset.example_pred.tsv' 
# check the results
head $PWD/kipoi-example/Basset.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 Basset -o example
kipoi predict Basset \
--dataloader_args='{"intervals_file": "example/intervals_file", "fasta_file": "example/fasta_file"}' \
-o 'Basset.example_pred.tsv' \
--singularity 
# check the results
head Basset.example_pred.tsv

Schema

Inputs

Single numpy array

Name: seq

Shape: (4, 600, 1)

Doc: DNA sequence

Targets

Single numpy array

Name: DHS_probs

Shape: (164,)

Doc: Probability of accessible chromatin in 164 cell types

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=3.8
h5py
pytorch::pytorch
pip=22.0.4
bioconda::pysam=0.17
cython

pip:

kipoi
kipoiseq

Dataloader dependencies

conda:

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

pip:

kipoiseq