Basset

Authors: David R. Kelley , Jasper Snoek , John L. Rinn

Version: 0.1.0

License: MIT

Contributed by: Roman Kreuzhuber

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

Trained on:

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. 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

Install model dependencies into current environment

kipoi env install Basset

Test the model

kipoi test Basset --source=kipoi

Make a prediction

cd ~/.kipoi/models/Basset
kipoi predict Basset \
  --dataloader_args='{'intervals_file': 'example_files/intervals.bed', 'fasta_file': 'example_files/hg38_chr22.fa'}' \
  -o '/tmp/Basset.example_pred.tsv'
# check the results
head '/tmp/Basset.example_pred.tsv'

Get the model

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

Make a prediction for example files

pred = model.pipeline.predict_example()

Use dataloader and model separately

# setup the example dataloader kwargs
dl_kwargs = {'intervals_file': 'example_files/intervals.bed', 'fasta_file': 'example_files/hg38_chr22.fa'}
import os; os.chdir(os.path.expanduser('~/.kipoi/models/Basset'))
# Get the dataloader and instantiate it
dl = model.default_dataloader(**dl_kwargs)
# get a batch iterator
it = dl.batch_iter(batch_size=4)
# predict for a batch
batch = 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 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

# Get the dataloader
setwd('~/.kipoi/models/Basset')
dl <- model$default_dataloader(intervals_file='example_files/intervals.bed', fasta_file='example_files/hg38_chr22.fa')
# get a batch iterator
it <- dl$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)

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

Relative path: .

Version: 0.1

Doc: Dataloader for the Basset model.

Authors: Lara Urban , Ziga Avsec , Roman Kreuzhuber

Type: Dataset

License: MIT

Arguments

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

fasta_file : Reference genome sequence

target_file (optional): path to the targets (.tsv) file

use_linecache (optional): if True, use linecache https://docs.python.org/3/library/linecache.html to access bed file rows

Model dependencies

conda:

python=3.5
h5py
pytorch::pytorch-cpu>=0.2.0

pip:

Dataloader dependencies

conda:

bioconda::genomelake
bioconda::pybedtools
python=3.5
numpy
pandas
cython

pip: