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STARRPeaker: uniform processing and accurate identification of STARR-seq active regions
Donghoon Lee1,2,3,4, Manman Shi5,6, Jennifer Moran5,6
1Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Genome Biology
|December 9, 2020
Summary
STARRPeaker is a new framework that uniformly processes STARR-seq data, addressing issues like non-uniform coverage and biases. This method enables comprehensive and unbiased enhancer identification in human cell lines.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- STARR-seq (Stimulation, Assay, Reporting, and Sequencing) is a powerful technology for identifying regulatory elements.
- Increased library complexity and sequencing depth in STARR-seq can lead to non-uniform coverage and biases (e.g., GC content).
- RNA secondary structure and thermodynamic stability can also confound STARR-seq results.
Purpose of the Study:
- To develop a robust computational framework for processing STARR-seq data.
- To address and mitigate confounding factors in STARR-seq experiments.
- To enable comprehensive and unbiased enhancer calling.
Main Methods:
- Development of STARRPeaker, a negative binomial regression framework.
- Generation of whole-genome STARR-seq data from HepG2 and K562 human cell lines.
- Application of STARRPeaker for enhancer identification.
Main Results:
- STARRPeaker provides a uniform approach to process STARR-seq data.
- The framework effectively accounts for non-uniform coverage and sequencing biases.
- Comprehensive and unbiased enhancer calling was achieved in human cell lines.
Conclusions:
- STARRPeaker offers a significant advancement in STARR-seq data analysis.
- The developed method improves the accuracy and reliability of enhancer identification.
- This framework facilitates a deeper understanding of gene regulation.