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STARRPeaker: uniform processing and accurate identification of STARR-seq active regions.

ABSTRACT: STARR-seq technology has employed progressively more complex genomic libraries and increased sequencing depths. An issue with the increased complexity and depth is that the coverage in STARR-seq experiments is non-uniform, overdispersed, and often confounded by sequencing biases, such as GC content. Furthermore, STARR-seq readout is confounded by RNA secondary structure and thermodynamic stability. To address these potential confounders, we developed a negative binomial regression framework for uniformly processing STARR-seq data, called STARRPeaker. Moreover, to aid our effort, we generated whole-genome STARR-seq data from the HepG2 and K562 human cell lines and applied STARRPeaker to comprehensively and unbiasedly call enhancers in them.

SUBMITTER: Lee D

PROVIDER: S-EPMC7722316 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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STARRPeaker: uniform processing and accurate identification of STARR-seq active regions.

Lee Donghoon D Shi Manman M Moran Jennifer J Wall Martha M Zhang Jing J Liu Jason J Fitzgerald Dominic D Kyono Yasuhiro Y Ma Lijia L White Kevin P KP Gerstein Mark M

Genome biology 20201208 1

STARR-seq technology has employed progressively more complex genomic libraries and increased sequencing depths. An issue with the increased complexity and depth is that the coverage in STARR-seq experiments is non-uniform, overdispersed, and often confounded by sequencing biases, such as GC content. Furthermore, STARR-seq readout is confounded by RNA secondary structure and thermodynamic stability. To address these potential confounders, we developed a negative binomial regression framework for ...[more]

PMID: 33292397

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