Exploiting sequence-based features for predicting enhancer–promoter interactions

By Yang Yang, Ruochi Zhang, Shashank Singh, Jian Ma. 2017

100 citations recorded by the source corpus.

Motivation: A large number of distal enhancers and proximal promoters form enhancer‐promoter interactions to regulate target genes in the human genome. Although recent high‐throughput genome‐wide mapping approaches have allowed us to more comprehensively recognize potential enhancer‐promoter interactions, it is still largely unknown whether sequence‐based features alone are sufficient to predict such interactions. Results: Here, we develop a new computational method (named PEP) to predict enhancer‐promoter interactions based on sequence‐based features only, when the locations of putative enhancers and promoters in a particular cell type are given. The two modules in PEP (PEP‐Motif and PEP‐Word) use different but complementary feature extraction strategies to exploit sequence‐based information. The results across six different cell types demonstrate that our method is effective in predicting enhancer‐promoter interactions as compared to the state‐of‐the‐art methods that use functional genomic signals. Our work demonstrates that sequence‐based features alone can reliably predict enhancer‐promoter interactions genome‐wide, which could potentially facilitate the discovery of important sequence determinants for long‐range gene regulation. Availability and Implementation: The source code of PEP is available at: https://github.com/ma‐compbio/PEP. Contact: jianma@cs.cmu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology, Representation & Self-Supervised Learning, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Chris Alberti
2. Amr Ahmed
3. Shashank Singh
4. Barnab'as P'oczos
5. Manzil Zaheer
6. Jian Ma
7. Yang Yang

In Representation & Self-Supervised Learning

1. Amr Ahmed
2. Barnab'as P'oczos
3. Manzil Zaheer
4. Philip Pham

In Computational Biology & Bioinformatics

1. Shashank Singh
2. Barnab'as P'oczos
3. Manzil Zaheer
4. Jian Ma
5. Yang Yang

Citations in the corpus

• Big Bird: Transformers for Longer Sequences (2020, methodology): “Multiple methods have been proposed to identify the promoter regions in a given DNA sequence [100, 59, 11, 99, 71], as it is an important first step in understanding gene regulation.”
• Predicting enhancer-promoter interaction from genomic sequence with deep neural networks (2016, methodology): “We utilized the EPI datasets previously used in TargetFinder [12], which was also used in PEP [15], for our model training and evaluation so that we can also directly compare with TargetFinder and PEP.”