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

Mingyi Yang; Omer Ali; Magnar Bjørås; Junbai Wang

doi:10.1016/j.isci.2023.107266

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

iScience. 2023 Jul 3;26(8):107266. doi: 10.1016/j.isci.2023.107266. eCollection 2023 Aug 18.

Authors

Mingyi Yang^{1

2}, Omer Ali^{3

4}, Magnar Bjørås^{1

5}, Junbai Wang⁶

Affiliations

¹ Department of Microbiology, Oslo University Hospital and University of Oslo, Oslo, Norway.
² Department of Medical Biochemistry, Oslo University Hospital and University of Oslo, Oslo, Norway.
³ Department of Pathology, Oslo University Hospital - Norwegian Radium Hospital, Oslo, Norway.
⁴ Faculty of Medicine, University of Oslo, Oslo, Norway.
⁵ Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
⁶ Department of Clinical Molecular Biology (EpiGen), Akershus University Hospital and University of Oslo, Lørenskog, Norway.

Abstract

Millions of single nucleotide variants (SNVs) exist in the human genome; however, it remains challenging to identify functional SNVs associated with diseases. We propose a non-encoding SNVs analysis tool bpb3, BayesPI-BAR version 3, aiming to identify the functional mutation blocks (FMBs) by integrating genome sequencing and transcriptome data. The identified FMBs display high frequency SNVs, significant changes in transcription factors (TFs) binding affinity and are nearby the regulatory regions of differentially expressed genes. A two-level Bayesian approach with a biophysical model for protein-DNA interactions is implemented, to compute TF-DNA binding affinity changes based on clustered position weight matrices (PWMs) from over 1700 TF-motifs. The epigenetic data, such as the DNA methylome can also be integrated to scan FMBs. By testing the datasets from follicular lymphoma and melanoma, bpb3 automatically and robustly identifies FMBs, demonstrating that bpb3 can provide insight into patho-mechanisms, and therapeutic targets from transcriptomic and genomic data.

Keywords: Biocomputational method; Bioinformatics; Biological sciences; Biological sciences tools; Omics.