pwm_HOCOMOCO/human/JUN

Authors: Ivan V. Kulakovskiy

License: MIT

Contributed by: Ziga Avsec

Cite as: https://doi.org/10.1093/nar/gkv1249

Trained on: Data from multiple sources including ENCODE ChIPseq, HT-SELEX datasets, etc. 204 to 1000 called peaks used for training.

Source files

'''Simple PWM-scanning model PWM database: HOCOMOCO URL: http://hocomoco.autosome.ru/ Paper: Kulakovskiy et al 2015, HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models: doi:10.1093/nar/gkv1249 '''

Create a new conda environment with all dependencies installed

kipoi env create pwm_HOCOMOCO
source activate kipoi-pwm_HOCOMOCO

Test the model

kipoi test pwm_HOCOMOCO/human/JUN --source=kipoi

Make a prediction

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

Create a new conda environment with all dependencies installed

kipoi env create pwm_HOCOMOCO
source activate kipoi-pwm_HOCOMOCO

Get the model

import kipoi
model = kipoi.get_model('pwm_HOCOMOCO/human/JUN')

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('pwm_HOCOMOCO/human/JUN')

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

Schema

Inputs

Single numpy array

Name: seq

Shape: (None, 4)

Doc: DNA sequence

Targets

Single numpy array

Name: pwm_match

Shape: (1,)

Doc: Best PWM match log-odds score

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

auto_resize_len (optional): None, required sequence length.

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
pip=22.0.4
tensorflow
keras

pip:

Dataloader dependencies

conda:

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

pip:

kipoiseq