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Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data
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 integra...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10371843/ https://www.ncbi.nlm.nih.gov/pubmed/37520692 http://dx.doi.org/10.1016/j.isci.2023.107266 |
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author | Yang, Mingyi Ali, Omer Bjørås, Magnar Wang, Junbai |
author_facet | Yang, Mingyi Ali, Omer Bjørås, Magnar Wang, Junbai |
author_sort | Yang, Mingyi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10371843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-103718432023-07-28 Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data Yang, Mingyi Ali, Omer Bjørås, Magnar Wang, Junbai iScience Article 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. Elsevier 2023-07-03 /pmc/articles/PMC10371843/ /pubmed/37520692 http://dx.doi.org/10.1016/j.isci.2023.107266 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yang, Mingyi Ali, Omer Bjørås, Magnar Wang, Junbai Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data |
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 | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10371843/ https://www.ncbi.nlm.nih.gov/pubmed/37520692 http://dx.doi.org/10.1016/j.isci.2023.107266 |
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