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High-performance method for identification of super enhancers from ChIP-Seq data with configurable cloud virtual
Natalia N Orlova1, Olga V Bogatova1, Alexey V Orlov1,2
1Moscow Institute of Physics and Technology (State University), Dolgoprudny, Moscow Region, Russia.
Methodsx
|March 5, 2021
Summary
A new method rapidly identifies super-enhancers using cloud virtual machines and the ROSE algorithm. This approach works on low-performance computers, enabling faster analysis of ChIP-seq data for super-enhancer discovery.
Area of Science:
- Genomics
- Computational Biology
- Epigenetics
Background:
- Super-enhancers are critical regulatory elements controlling gene expression.
- Identifying super-enhancers is essential for understanding cellular function and disease.
- Existing methods for super-enhancer identification can be time-consuming and computationally intensive.
Purpose of the Study:
- To develop a universal and rapid method for identifying super-enhancers.
- To leverage cloud virtual machines (cVMs) and the Rank-Ordering of Super-Enhancers (ROSE) algorithm for efficient analysis.
- To enable super-enhancer identification on low-performance client machines.
Main Methods:
- Utilized configurable cloud virtual machines (cVMs) for data analysis.
- Employed the Rank-Ordering of Super-Enhancers (ROSE) algorithm.
- Processed ChIP-seq data for active enhancer marks (e.g., H3K27ac) to identify super-enhancer domains.
Main Results:
- The method successfully identified super-enhancers across diverse datasets (human cell line, mouse and human tissues).
- Analysis cycle time for raw ChIP-seq data ranged from 15 to 48 minutes.
- The cVM approach allows for scalable, parallel processing and can be accessed from low-performance computers.
Conclusions:
- The proposed cVM-based method offers a rapid and accessible solution for super-enhancer identification.
- This approach significantly reduces analysis time and computational barriers.
- It facilitates high-throughput processing and broad application in genomic research.
