Sequence-based correction of barcode bias in massively parallel reporter assays

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Massively parallel reporter assays (MPRAs) are essential for high-throughput screening of candidate cis-regulatory elements (CREs).
• MPRAs rely on cloning CREs with unique DNA tags upstream or downstream of a reporter gene.
• Tag sequences can introduce biases, potentially affecting the accuracy of measured cis-regulatory activity.

Purpose of the Study:

• To develop a sequence-based method for correcting tag-sequence-specific effects in MPRAs.
• To reduce variation and improve the identification of functional regulatory variants using MPRAs.
• To enhance the design and reliability of MPRA protocols.

Main Methods:

• Development of a novel sequence-based computational method.
• Application of the method to correct for tag sequence biases in MPRA data.
• Analysis of sequence features associated with post-transcriptional regulation.

Main Results:

• The proposed method significantly reduces tag-sequence-specific variation in MPRA data.
• Improved identification of functional regulatory variants compared to uncorrected data.
• The model successfully captures sequence features linked to post-transcriptional mRNA regulation.

Conclusions:

• The developed method offers a robust approach to mitigate bias in MPRA experiments.
• This advancement improves the accuracy and efficiency of identifying functional CREs.
• The findings contribute to better MPRA experimental design and data interpretation.