| --- |
| license: cc-by-4.0 |
| library_name: bpnet |
| tags: |
| - bpnet |
| - dna |
| - genomics |
| - transcription-factor-binding |
| - encode |
| - ChIP-seq |
| - hg38 |
| - qc-passed |
| - CTCF |
| --- |
| |
| # ENCODE BPNet -- CTCF ChIP-seq in LNCaP clone FGC (ENCSR000DME) |
|
|
| Trained BPNet model (ChIP-seq) from the ENCODE project. |
|
|
| - Experiment: [ENCSR000DME](https://www.encodeproject.org/experiments/ENCSR000DME/) |
| - Model annotation: [ENCSR124ANL](https://www.encodeproject.org/annotations/ENCSR124ANL/) |
| - Assembly: hg38 · Target: CTCF · Biosample: LNCaP clone FGC |
|
|
| ## QC |
| - Status: **passed** |
| - Notes: Found direct motif (counts, profile); |
|
|
| ## Files |
| 5-fold cross-validation. Each `fold_*/` holds the trained model in two forms: |
| - `model.h5` — Keras weights (needs the `bpnet` custom layer to load) |
| - `saved_model/` — TensorFlow SavedModel (portable; loads with no extra deps) |
|
|
| ## Load |
| ```python |
| from huggingface_hub import snapshot_download |
| import tensorflow as tf |
| d = snapshot_download("kundajelab/encode-bpnet-CTCF-ChIP-seq-LNCaP-clone-FGC-ENCSR000DME-ENCSR124ANL") |
| model = tf.saved_model.load(f"{d}/fold_0/saved_model") # portable |
| # Keras .h5 (needs the bpnet package): |
| # from bpnet.model.custommodel import CustomModel |
| # m = tf.keras.models.load_model(f"{d}/fold_0/model.h5", |
| # custom_objects={'CustomModel': CustomModel}) |
| ``` |
|
|
| ## Inference inputs |
| The `serving_default` signature takes **three** inputs (not sequence alone): |
| - `sequence` — one-hot DNA, shape `(N, 2114, 4)` |
| - `profile_bias_input_0` — control (bias) profile track, shape `(N, 1000, 2)` |
| - `counts_bias_input_0` — control log-count(s), shape `(N, 2)` |
|
|
| The bias inputs are the experiment's matched control signal (the model file's `derived_from` control bigWigs on the ENCODE portal). Outputs: `profile_predictions` `(N, 1000, 2)` and `logcounts_predictions` `(N, 1)`. Reverse-complement averaging is the production default. |
|
|
| ## License & citation |
| Released under CC-BY-4.0, matching the [ENCODE data-use policy](https://www.encodeproject.org/about/data-use-policy/). Please cite the ENCODE Project Consortium and the model software: [BPNet](https://github.com/kundajelab/bpnet) (Avsec et al., Nat Genet 2021). |
|
|
