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kundajelab/
encode-chrombpnet-DNASE-kidney-glomerular-epithelial-cell-ENCSR136KEL-ENCSR120AAX

Name: encode-chrombpnet-DNASE-kidney-glomerular-epithelial-cell-ENCSR136KEL-ENCSR120AAX
Availability: InStock

chrombpnet

encode

chromatin-accessibility

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ENCODE ChromBPNet Atlas

As part of the ENCODE 4 Project, we trained ChromBPNet models on 1,512 ENCODE DNAse-seq and ATAC-seq across 408 biosamples. Here, we provide all models for open-source use.

For more information about the models, see:

Main ENCODE 4 Paper
A unified lexicon of predictive DNA sequence motifs from ENCODE transcription factor binding and chromatin accessibility assays (Yun, C. M. et al., Zenodo 2026)
ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants (Pampari, A. et al., bioRxiv 2024)

ChromBPNet model: DNASE in kidney glomerular epithelial cell (ENCSR136KEL)

Model: ChromBPNet
Assay: DNASE-seq
Experiment: ENCSR136KEL
Model annotation: ENCSR120AAX
Biosample: kidney glomerular epithelial cell (Full name: Homo sapiens kidney glomerular epithelial cell male adult (43 years) and male adult (62 years))
Cell slim(s): epithelial-cell
Organ slim(s): epithelium,kidney
Developmental slim(s): mesoderm
System slim(s): excretory-system
Assembly: hg38

Directory structure

fold_0: Model of 5-fold cross-validation: Fold 0
- model.chrombpnet.fold_0.encid.h5: full chrombpnet model that combines both bias and corrected model in .h5 format
- model.chrombpnet_nobias.fold_0.encid.h5: bias-corrected accessibility model in .h5 format (Use for all biological discovery)
- model.bias_scaled.fold_0.encid.h5: bias model in .h5 format
- model.chrombpnet.fold_0.encid.tar: full chrombpnet model that combines both bias and corrected model in SavedModel format. After being untarred, it results in a directory named "chrombpnet".
- model.chrombpnet_nobias.fold_0.encid.tar: bias-corrected accessibility model in SavedModel format (Use for all biological discovery). After being untarred, it results in a directory named "chrombpnet_wo_bias".
- model.bias_scaled.fold_0.encid.tar: bias model in SavedModel format. After being untarred, it results in a directory named "bias_model_scaled".
- logs.models.fold_0.encid: folder containing log files for training models
fold_1: Model of 5-fold coss-validation: Fold 1
fold_2: Model of 5-fold cross-validation: Fold 2
fold_3: Model of 5-fold cross-validation: Fold 3
fold_4: Model of 5-fold cross-validation: Fold 4

Instructions

1. Pseudocode for loading models in .h5 format

(1) Use the code in python after appropriately defining model_in_h5_format and inputs.
(2) inputs is a one hot encoded sequence of shape (N,2114,4). Here N corresponds to the number of tested sequences, 2114 is the input sequence length and 4 corresponds to [A,C,G,T].

python

import tensorflow as tf
from tensorflow.keras.utils import get_custom_objects
from tensorflow.keras.models import load_model

custom_objects={"tf": tf}
get_custom_objects().update(custom_objects)

model=load_model(model_in_h5_format,compile=False)
outputs = model(inputs)

The list outputs consists of two elements. The first element has a shape of (N, 1000) and contains logit predictions for a 1000-base-pair output. The second element, with a shape of (N, 1), contains logcount predictions. To transform these predictions into per-base signals, follow the provided pseudo code lines below.

python

import numpy as np

def softmax(x, temp=1):
    norm_x = x - np.mean(x,axis=1, keepdims=True)
    return np.exp(temp*norm_x)/np.sum(np.exp(temp*norm_x), axis=1, keepdims=True)
    
predictions = softmax(outputs[0]) * (np.exp(outputs[1])-1)

2. Pseudocode for loading models in .tar format

(1) First untar the directory as follows tar -xvf model.tar.
(2) Use the code below in python after appropriately defining model_dir_untared and inputs.
(3) inputs is a one hot encoded sequence of shape (N,2114,4). Here N corresponds to the number of tested sequences, 2114 is the input sequence length and 4 corresponds to ACGT.

Reference: https://www.tensorflow.org/api_docs/python/tf/saved_model/load

python

import tensorflow as tf

model = tf.saved_model.load('model_dir_untared')
outputs = model.signatures['serving_default'](**{'sequence':inputs.astype('float32')})

The variable outputs represents a dictionary containing two key-value pairs. The first key is logits_profile_predictions, holding a value with a shape of (N, 1000). This value corresponds to logit predictions for a 1000-base-pair output. The second key, named `logcount_predictions``, is associated with a value of shape (N, 1), representing logcount predictions. To transform these predictions into per-base signals, utilize the provided pseudo code lines mentioned below.

python

import numpy as np
def softmax(x, temp=1):
    norm_x = x - np.mean(x,axis=1, keepdims=True)
    return np.exp(temp*norm_x)/np.sum(np.exp(temp*norm_x), axis=1, keepdims=True)
    
predictions = softmax(outputs["logits_profile_predictions"]) * (np.exp(outputs["logcount_predictions"])-1)

Docker image to load and use the models

https://hub.docker.com/r/kundajelab/chrombpnet-atlas/ (tag:v1)

Code for ChromBPNet

https://github.com/kundajelab/chrombpnet/

License & citation

External data users may freely download, analyze and publish results based on any ENCODE data without restrictions.

Released under the ENCODE data-use policy. Please cite the ENCODE Project Consortium and the model software: ChromBPNet (Pampari et al., bioRxiv 2024).

kundajelab/encode-chrombpnet-DNASE-kidney-glomerular-epithelial-cell-ENCSR136KEL-ENCSR120AAX

ENCODE ChromBPNet Atlas

ChromBPNet model: DNASE in kidney glomerular epithelial cell (ENCSR136KEL)

Directory structure

Instructions

1. Pseudocode for loading models in .h5 format

2. Pseudocode for loading models in .tar format

Docker image to load and use the models

Code for ChromBPNet

License & citation

kundajelab/
encode-chrombpnet-DNASE-kidney-glomerular-epithelial-cell-ENCSR136KEL-ENCSR120AAX