Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Yang, Mingyi, Ali, Omer, Bjørås, Magnar, Wang, Junbai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
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
_version_ 1785078238864736256
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
work_keys_str_mv AT yangmingyi identifyingfunctionalregulatorymutationblocksbyintegratinggenomesequencingandtranscriptomedata
AT aliomer identifyingfunctionalregulatorymutationblocksbyintegratinggenomesequencingandtranscriptomedata
AT bjørasmagnar identifyingfunctionalregulatorymutationblocksbyintegratinggenomesequencingandtranscriptomedata
AT wangjunbai identifyingfunctionalregulatorymutationblocksbyintegratinggenomesequencingandtranscriptomedata