Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data
Summary: 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) b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004223013433 |
| _version_ | 1797780684812582912 |
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| author | Mingyi Yang Omer Ali Magnar Bjørås Junbai Wang |
| author_facet | Mingyi Yang Omer Ali Magnar Bjørås Junbai Wang |
| author_sort | Mingyi Yang |
| collection | DOAJ |
| description | Summary: 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. |
| first_indexed | 2024-03-12T23:47:28Z |
| format | Article |
| id | doaj.art-7bb525c2c7384d05a80674ee9aec1a2b |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-12T23:47:28Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-7bb525c2c7384d05a80674ee9aec1a2b2023-07-14T04:28:18ZengElsevieriScience2589-00422023-08-01268107266Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome dataMingyi Yang0Omer Ali1Magnar Bjørås2Junbai Wang3Department of Microbiology, Oslo University Hospital and University of Oslo, Oslo, Norway; Department of Medical Biochemistry, Oslo University Hospital and University of Oslo, Oslo, NorwayDepartment of Pathology, Oslo University Hospital - Norwegian Radium Hospital, Oslo, Norway; Faculty of Medicine, University of Oslo, Oslo, NorwayDepartment of Microbiology, Oslo University Hospital and University of Oslo, Oslo, Norway; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Clinical Molecular Biology (EpiGen), Akershus University Hospital and University of Oslo, Lørenskog, Norway; Corresponding authorSummary: 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.http://www.sciencedirect.com/science/article/pii/S2589004223013433Biological sciencesBioinformaticsBiocomputational methodOmicsBiological sciences tools |
| spellingShingle | Mingyi Yang Omer Ali Magnar Bjørås Junbai Wang Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data iScience Biological sciences Bioinformatics Biocomputational method Omics Biological sciences tools |
| title | Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data |
| title_full | Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data |
| title_fullStr | Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data |
| title_full_unstemmed | Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data |
| title_short | Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data |
| title_sort | identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data |
| topic | Biological sciences Bioinformatics Biocomputational method Omics Biological sciences tools |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223013433 |
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