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Updated: Jul 4, 2026

Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library

Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library

Published on: January 10, 2025

Protocol for identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome

Mingyi Yang¹, Gege Liu², Magnar Bjørås³

¹Department of Microbiology, Oslo University Hospital and University of Oslo, 0372 Oslo, Norway; Department of Medical Biochemistry, Oslo University Hospital and University of Oslo, 0372 Oslo, Norway.

|July 2, 2026

Summary

This summary is machine-generated.

BayesPI-BAR version 3 (bpb3) predicts functional mutation blocks (FMBs) impacting gene expression. This tool integrates DNA sequence and gene expression data to identify regulatory elements disrupted by genetic variations, aiding in understanding transcriptional regulation.

Keywords:

Bioinformatics Cancer Computer sciences Genetics Genomics Molecular Biology Sequence analysis

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Identifying functional mutation blocks (FMBs) influencing genome-wide transcriptional regulation is complex.
Existing methods struggle to effectively integrate diverse genomic and transcriptomic data.

Purpose of the Study:

To introduce BayesPI-BAR version 3 (bpb3), a novel Python-based tool for predicting FMBs.
To demonstrate bpb3's capability in analyzing the impact of single-nucleotide polymorphisms (SNPs) on transcription factors (TFs) and gene expression.

Main Methods:

bpb3 integrates DNA sequence data with gene expression data.
The tool was applied to rank TFs affected by known SNPs in regulatory regions.
It also integrated SNP genomic distribution with differential gene expression to identify FMBs disrupting TF-DNA binding and gene expression.

Main Results:

bpb3 successfully ranked TFs based on SNP impact.
The tool identified FMBs associated with disrupted TF-DNA binding and gene expression in lymphoma.
Four examples demonstrated the tool's utility in analyzing regulatory elements.

Conclusions:

bpb3 is an effective tool for predicting FMBs by integrating sequence and expression data.
The tool aids in understanding how genetic variations affect transcriptional regulation and TF activity.
bpb3 provides a valuable approach for identifying disease-associated regulatory elements, particularly in cancer genomics.