Aller au contenu
ModelVérifiéExterne
Télécharger

Encode Chrombpnet DNASE ENCSR173YSO ENCSR142NSU

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.

Sourcehuggingface.co/kundajelab/encode-chrombpnet-DNASE-dorsolateral-prefrontal-cortex-ENCSR173YSO-ENCSR142NSU/tree/e668d83817caa027a9af669d686ab0a93afb8820
TypeOtherVersionRevision e668d83Éditeur@kundajelabCClasse

Source du modèle

Type
Other
Taille du fichier
2,56–25,2 MB
Version
vRevision e668d83
Mis à jour
3 oct. 2026
Éditeur
@kundajelab
Téléchargements
0
Source
Hugging Face

Description de la source

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:

ChromBPNet model: DNASE in dorsolateral prefrontal cortex (ENCSR173YSO)

  • Model: ChromBPNet
  • Assay: DNASE-seq
  • Experiment: ENCSR173YSO
  • Model annotation: ENCSR142NSU
  • Biosample: dorsolateral prefrontal cortex (Full name: Homo sapiens dorsolateral prefrontal cortex tissue female adult (79 years))
  • Cell slim(s): None
  • Organ slim(s): brain
  • Developmental slim(s): ectoderm
  • System slim(s): central-nervous-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].

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.

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

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.

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

Code for 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).

Sources

1 source
Hugging FacePrincipalVersion e668d83817caa027a9af669d686ab0a93afb8820
huggingface.co/kundajelab/encode-chrombpnet-DNASE-dorsolateral-prefrontal-cortex-ENCSR173YSO-ENCSR142NSU/tree/e668d83817caa027a9af669d686ab0a93afb8820

Vérifié 3 oct.

Artefacts du modèle

15 artefacts

fold_0/model.bias_scaled.fold_0.ENCSR173YSO.h5

h5 · 2,56 MB · SHA-256 8309212f9bde…f31a · Hugging Face

Télécharger

fold_0/model.chrombpnet_nobias.fold_0.ENCSR173YSO.h5

h5 · 24,4 MB · SHA-256 e4c4667f51d8…2dd8 · Hugging Face

Télécharger

fold_0/model.chrombpnet.fold_0.ENCSR173YSO.h5

h5 · 25,2 MB · SHA-256 4de340b91da3…dc49 · Hugging Face

Télécharger

fold_1/model.bias_scaled.fold_1.ENCSR173YSO.h5

h5 · 2,56 MB · SHA-256 6e8d4d0dc1d8…6ea8 · Hugging Face

Télécharger

fold_1/model.chrombpnet_nobias.fold_1.ENCSR173YSO.h5

h5 · 24,4 MB · SHA-256 206c09968c6f…1f29 · Hugging Face

Télécharger

fold_1/model.chrombpnet.fold_1.ENCSR173YSO.h5

h5 · 25,2 MB · SHA-256 257685e5f646…1558 · Hugging Face

Télécharger

fold_2/model.bias_scaled.fold_2.ENCSR173YSO.h5

h5 · 2,56 MB · SHA-256 7de1c6a01278…72a0 · Hugging Face

Télécharger

fold_2/model.chrombpnet_nobias.fold_2.ENCSR173YSO.h5

h5 · 24,4 MB · SHA-256 0c9b388deb9d…f7dd · Hugging Face

Télécharger

fold_2/model.chrombpnet.fold_2.ENCSR173YSO.h5

h5 · 25,2 MB · SHA-256 a6f12f20ba2c…8b9a · Hugging Face

Télécharger

fold_3/model.bias_scaled.fold_3.ENCSR173YSO.h5

h5 · 2,56 MB · SHA-256 bc9fbabb6e56…e863 · Hugging Face

Télécharger

fold_3/model.chrombpnet_nobias.fold_3.ENCSR173YSO.h5

h5 · 24,4 MB · SHA-256 746749f612df…ffb3 · Hugging Face

Télécharger

fold_3/model.chrombpnet.fold_3.ENCSR173YSO.h5

h5 · 25,2 MB · SHA-256 d95cfc2c6da4…8286 · Hugging Face

Télécharger

fold_4/model.bias_scaled.fold_4.ENCSR173YSO.h5

h5 · 2,56 MB · SHA-256 42f6e4120fbd…c119 · Hugging Face

Télécharger

fold_4/model.chrombpnet_nobias.fold_4.ENCSR173YSO.h5

h5 · 24,4 MB · SHA-256 ed4b3f9fc3b3…8f22 · Hugging Face

Télécharger

fold_4/model.chrombpnet.fold_4.ENCSR173YSO.h5

h5 · 25,2 MB · SHA-256 693357733bb8…c3e5 · Hugging Face

Télécharger

Extraits de sources

3 extraits
Readme3 oct.huggingface.co/kundajelab/encode-chrombpnet-DNASE-dorsolateral-prefrontal-cortex-ENCSR173YSO-ENCSR142NSU

--- license: mit library_name: chrombpnet tags: - encode - chrombpnet - chromatin-accessibility - DNASE - prefrontal - hg38 --- # 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](https://doi.org/10.5281/zenodo.17123347) (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](https://doi.org/10.1101/2024.12.25.630221) (Pampari, A. et al., bioRxiv 2024) ## ChromBPNet model: DNASE in dorsolateral prefrontal cortex (ENCSR173YSO) - Model: ChromBPNet - Assay: DNASE-seq - Experiment: [ENCSR173YSO](https://www.encodeproject.org/experiments/ENCSR173YSO/) - Model annotation: [ENCSR142NSU](https://www.encodeproject.org/annotations/ENCSR142NSU/) - Biosample: dorsolateral prefrontal cortex (Full name: Homo sapiens dorsolateral prefrontal cortex tissue female adult (79 years)) - Cell slim(s): None - Organ slim(s): brain - Developmental slim(s): ectoderm - System slim(s): central-nervous-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.mode...

Readme11 juil.huggingface.co/kundajelab/encode-chrombpnet-DNASE-ENCSR173YSO-ENCSR142NSU

--- license: mit library_name: chrombpnet tags: - encode - chrombpnet - chromatin-accessibility - DNASE - prefrontal - hg38 --- # 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](https://doi.org/10.5281/zenodo.17123347) (Deshpande et al., Zenodo 2025) - [ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants](https://doi.org/10.1101/2024.12.25.630221) (Pampari et al., bioRxiv 2024) ## ChromBPNet model: DNASE in dorsolateral prefrontal cortex (ENCSR173YSO) - Model: ChromBPNet - Assay: DNASE-seq - Experiment: [ENCSR173YSO](https://www.encodeproject.org/experiments/ENCSR173YSO/) - Model annotation: [ENCSR142NSU](https://www.encodeproject.org/annotations/ENCSR142NSU/) - Biosample: dorsolateral prefrontal cortex (Full name: Homo sapiens dorsolateral prefrontal cortex tissue female adult (79 years)) - Cell slim(s): None - Organ slim(s): brain - Developmental slim(s): ectoderm - System slim(s): central-nervous-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.fo...

Note3 oct.huggingface.co/kundajelab/encode-chrombpnet-DNASE-dorsolateral-prefrontal-cortex-ENCSR173YSO-ENCSR142NSU

Source context: 0 downloads · 0 likes · Library chrombpnet · Repo kundajelab/encode-chrombpnet-DNASE-dorsolateral-prefrontal-cortex-ENCSR173YSO-ENCSR142NSU