Welcome to CREME: Cis-Regulatory Element Model Explanations

CREME is a powerful toolkit for interpreting deep neural networks (DNNs) trained on genomic data. It helps researchers understand how these models predict gene expression and identify important regulatory elements in DNA sequences.

Key Features

• Identify cis-regulatory elements (CREs) that directly enhance or silence target genes

• Map CRE distance from transcription start sites and gene expression

• Analyze the intricate complexity of higher-order CRE interactions

• Treat trained DNNs as surrogates for experimental assays, enabling in silico “measurements” for any sequence

Quick Start

Install CREME using pip:

pip install creme-nn

Then, import and use CREME in your Python scripts:

from creme import context_dependence_test

# Example usage
results = context_dependence_test(model, sequence, tile_pos=[100, 200], num_shuffle=10)

Contents

User Guide

• Usage
  • Context dependence test
  • Context swap test
  • Necessity test
  • Sufficiency test
  • Distance Test
  • Higher order interaction test
  • Multiplicity test
  • Fine-tile search
  • Extra functions
• Tutorials
  • Adding a custom model to use with CREME
  • Context dependence test
  • Context swap test
  • Necessity test
  • Sufficiency test
  • Fine-tile search
  • Distance test
  • Higher-order interaction test
  • Multiplicity test

CREME Functions

• Usage
  • Context dependence test
  • Context swap test
  • Necessity test
  • Sufficiency test
  • Distance Test
  • Higher order interaction test
  • Multiplicity test
  • Fine-tile search
  • Extra functions

Tutorials

• Adding a custom model to use with CREME
• Context dependence test
• Context swap test
• Necessity test
• Sufficiency test
• Fine-tile search
• Distance test
• Higher-order interaction test
• Multiplicity test

About CREME

CREME works by performing various in silico perturbation experiments on DNA sequences and analyzing how these changes affect the DNN’s predictions. This approach mimics wet-lab experiments but allows for faster, more comprehensive testing of hypotheses about gene regulation.

Who is it for?

CREME is designed for computational biologists, bioinformaticians, and machine learning researchers working on genomics problems. It’s particularly useful for those studying gene regulation, enhancer-promoter interactions, and the effects of genetic variations.

Citing CREME

If you use CREME in your research, please cite our paper:

Toneyan S, Koo PK. Interpreting cis-regulatory interactions from large-scale deep neural networks for genomics. bioRxiv. 2023.

Get Involved

• GitHub Repository

• Report Issues

• Contribute

Indices and tables

• Index

• Module Index

• Search Page