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Jiecong Lin edited this page Jul 15, 2026 · 8 revisions

EPInformer — pipeline

A clean, self-contained pipeline for EPInformer, built around EPInformer/models.py (EPInformer_v2 + the 256 bp enhancer_predictor_256bp). It trains two models, in order, end-to-end from raw ENCODE data for K562 and GM12878, with supported prebuilt inputs for the additional cell lines (H1, HepG2, HUVEC, and NHEK):

  1. Enhancer-activity encoder — predicts 256 bp enhancer activity (H3K27ac·DNase) from sequence.
  2. Gene-expression model (EPInformer_v2) — predicts RNA / CAGE from a gene's promoter plus its ABC-nominated enhancers, reusing the frozen encoder as the sequence backbone.

Do Part 1 (encoder) first, then Part 2 (expression) — the expression model loads the encoder.

Results (12-fold leave-chromosome-out, pooled out-of-fold Pearson R)

Part 1 — enhancer encoder (log2 activity):

H1 HepG2 K562 HUVEC NHEK GM12878
R 0.820 0.743 0.740 0.742 0.677 0.617

Encoder headline values use forward-plus-reverse-complement averaging. For the exact pinned K562 narrowPeak, the corresponding single-reverse result is approximately 0.734; keep reverse-only and fwd+RC predictions in separate output directories.

Part 2 — gene expression (shipped f3: frozen encoder + 3 enhancer features + promoter signal):

K562 GM12878 HepG2 HUVEC NHEK H1
RNA 0.856 0.860 0.845 0.839 0.828 0.781
CAGE 0.867 0.890

CAGE labels exist only for K562/GM12878 (the other four are RNA-only). The H1/HepG2/HUVEC/NHEK expression numbers are new — upstream EPInformer reports expression for K562/GM12878 only.

RNA-seq gene expression prediction

Contents

Pretrained checkpoints

Download pretrained checkpoints from JiecongLin/EPInformer-pipeline: 12-fold enhancer encoders and validated RNA gene-expression checkpoints for all six supported cell lines, plus CAGE checkpoints for K562 and GM12878. f1, f2, and f3 feature configurations are available under expression_models/{cell}/{RNA,CAGE}/{f1,f2,f3}/.

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