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Related Experiment Video

Updated: Jul 20, 2025

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

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Published on: January 10, 2025

Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data.

Mingyi Yang^1,2, Omer Ali^3,4, Magnar Bjørås^1,5

¹Department of Microbiology, Oslo University Hospital and University of Oslo, Oslo, Norway.

|July 31, 2023

Summary

This summary is machine-generated.

Identifying functional single nucleotide variants (SNVs) linked to diseases is difficult. Our tool, BayesPI-BAR version 3 (bpb3), finds functional mutation blocks (FMBs) using genomic and transcriptomic data to reveal disease mechanisms.

Keywords:

Biocomputational method Bioinformatics Biological sciences Biological sciences tools Omics

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Millions of single nucleotide variants (SNVs) exist in the human genome.
Identifying disease-associated functional SNVs remains a significant challenge in genetic research.

Purpose of the Study:

To introduce BayesPI-BAR version 3 (bpb3), a novel tool for analyzing non-encoding SNVs.
To identify functional mutation blocks (FMBs) by integrating genome sequencing and transcriptome data.

Main Methods:

Implemented a two-level Bayesian approach with a biophysical model for protein-DNA interactions.
Computed transcription factor (TF)-DNA binding affinity changes using clustered position weight matrices (PWMs) from over 1700 TF-motifs.
Integrated epigenetic data, such as DNA methylome, for enhanced FMB scanning.

Main Results:

Identified FMBs characterized by high-frequency SNVs and significant changes in TF binding affinity near regulatory regions of differentially expressed genes.
Demonstrated robust and automatic FMB identification using datasets from follicular lymphoma and melanoma.
bpb3 successfully identified FMBs in tested datasets.

Conclusions:

bpb3 provides valuable insights into disease patho-mechanisms by analyzing genomic and transcriptomic data.
The tool aids in identifying potential therapeutic targets for diseases.
bpb3 offers a robust method for functional SNV analysis.