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Challenges and considerations for reproducibility of STARR-seq assays
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Abstract
High-throughput methods such as RNA-seq, ChIP-seq and ATAC-seq have well-established guidelines, commercial kits, and analysis pipelines that enable consistency and wider adoption for understanding genome function and regulation. STARR-seq, a popular assay for directly quantifying activity of thousands of enhancer sequences simultaneously, has seen limited standardization across studies. The assay is long with >250 steps, and frequent customization of the protocol and variations in bioinformatics methods raise concerns for reproducibility of STARR-seq studies. Here, we assess each step of the protocol and analysis pipelines from published sources and in-house assays, and identify critical steps and QC checkpoints necessary for reproducibility of the assay. We also provide guidelines for experimental design, protocol scaling, customization, and analysis pipelines for better adoption of the assay. These resources will allow better optimization of STARR-seq for specific research needs, enable comparisons and integration across studies, and improve reproducibility of results.
Title: Challenges and considerations for reproducibility of STARR-seq assays
Description:
Abstract
High-throughput methods such as RNA-seq, ChIP-seq and ATAC-seq have well-established guidelines, commercial kits, and analysis pipelines that enable consistency and wider adoption for understanding genome function and regulation.
STARR-seq, a popular assay for directly quantifying activity of thousands of enhancer sequences simultaneously, has seen limited standardization across studies.
The assay is long with >250 steps, and frequent customization of the protocol and variations in bioinformatics methods raise concerns for reproducibility of STARR-seq studies.
Here, we assess each step of the protocol and analysis pipelines from published sources and in-house assays, and identify critical steps and QC checkpoints necessary for reproducibility of the assay.
We also provide guidelines for experimental design, protocol scaling, customization, and analysis pipelines for better adoption of the assay.
These resources will allow better optimization of STARR-seq for specific research needs, enable comparisons and integration across studies, and improve reproducibility of results.
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