DARDN (DnAResDualNet): Identifying transcription factors inducing cancer-specific CTCF binding using multi-CNNs and DeepLIFT

Getting started

1. Clone the repository using the following command:

git@github.com:berkuva/DARDN.git

2. Download the "union_binding_occupancy_score_GT3.csv" file using the following wget command:

wget https://zanglab.github.io/data/cancerCTCF/data/union_binding.bed

For other chromosomes, you can use generate_onehot.py to one-hot encoded generate T-ALL gained CTCF sites.

3. Download T-ALL gained and constitutive CTCF sites for chromosome 1 chr1_len5045.npz from Google Drive.

This file contains 26009 CTCF-centered sites, each with length 10090.

4. Run DARDN on the sample data and pre-set parameters:

python run_model.py

Otherwise, modify load_data.py as needed to load the desired input data to train DARDN. To modify subsequence selection from gained CTCF sites, modify parameters in extract_subsequences.py.

DARDN (DnAResDualNet)

DARDN is a deep learning framework for identifying transcription factors bound near cancer-specific CTCF sites. DARDN is a product of an extension of a previous study.

Results

We ran DARDN on T-cell acute lymphoblastic leukemia (T-ALL) data and identified RBPJ as one of the most enriched motifs under various perturbations to show the robustness of our pipeline.

DARDN is cancer-type agnostic and can easily be adapted to other cancer types. We show the adaptability by running our pipeline on 5 additional cancer types: acute myeloid leukemia (AML), breast cancer (BRCA), colorectal cancer (CRC), lung adenocarcinoma (LUAD), and prostate cancer (PRAD). Please find detailed tables and figures in our manuscript.

Pipeline

1. Prepare CTCF-centered DNA sequences.
2. Perform data augmentation if necessary.
3. Train and evaluate DARDN, which contains dual-CNN networks and residual connections.
4. Apply DeepLIFT to gained sites to assign scores to subsequences.
5. Select subsequences with high DeepLIFT scores (above a cutoff score or a fixed number of subsequences) and input them into a motif analysis tool such as HOMER.

1. Prepare CTCF-centered DNA sequences

If you don't have your own CTCF-centered data and would like to use the data used in our work, you can download cancer-specific CTCF gained sites here. This link includes CTCF gained sites for T-ALL, AML, BRCA, CRC, LUAD, and PRAD as well as constitutive CTCF sites. Additionally, you can generate CTCF-centered data by using the provided generate_onehot.py

2. Data Augmentation

Augmenting DNA sequences can include shifting and reverse complementation of the original sequences. utils.py contains functions for data augmentation. Please modify them as necessary.

3. Train and evaluate DARDN

You can run run_model.py to train and evaluate DARDN. We also provide pre-trained weights in pretrained_weights.pth

4. Apply DeepLIFT

extract_subsequences.py contains two methods to select subsequences after DARDN is trained: 1. Select a fixed number (say 10) of subsequences from each gained sequence. 2. Select a fixed number (say 10) of subsequences from each gained sequence. We also provided attributions.pt which contains DeepLIFT calculations for each gained site for T-ALL.

5. Run motif analysis software such as HOMER

Sample HOMER command: findMotifsGenome.pl /path/to/subsequences.txt hg38 /path/to/save_dir/ -size 200

Summary

• Run generate_onehot.py to generate sequences.
• Run run_model.py for model training and DeepLIFT calculations. Or, use provided pre-trained parameters (pretrained_weights.pth) and DeepLIFT scores for each gained site in T-ALL (attributions.pt).
• Run extract_subsequences.py to filter subsequences with the most positive DeepLIFT scores. This creates a txt file with the genomic coordinates of the selected subsequences. - Run motif analysis.

Citation

If you use any data from this website, please cite:

Cho HJ, Wang Z, Cong Y, Bekiranov S, Zhang A, Zang C. DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery. Genes. 2024; 15(2):144. https://doi.org/10.3390/genes15020144