Cargando…
Identification of transcription factor high accumulation DNA zones
BACKGROUND: Transcription factors (TF) play a crucial role in the regulation of gene transcription; alterations of their activity and binding to DNA areas are strongly involved in cancer and other disease onset and development. For proper biomedical investigation, it is hence essential to correctly...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590011/ https://www.ncbi.nlm.nih.gov/pubmed/37864168 http://dx.doi.org/10.1186/s12859-023-05528-1 |
_version_ | 1785123908206198784 |
---|---|
author | Cascianelli, Silvia Ceddia, Gaia Marchesi, Alberto Masseroli, Marco |
author_facet | Cascianelli, Silvia Ceddia, Gaia Marchesi, Alberto Masseroli, Marco |
author_sort | Cascianelli, Silvia |
collection | PubMed |
description | BACKGROUND: Transcription factors (TF) play a crucial role in the regulation of gene transcription; alterations of their activity and binding to DNA areas are strongly involved in cancer and other disease onset and development. For proper biomedical investigation, it is hence essential to correctly trace TF dense DNA areas, having multiple bindings of distinct factors, and select DNA high occupancy target (HOT) zones, showing the highest accumulation of such bindings. Indeed, systematic and replicable analysis of HOT zones in a large variety of cells and tissues would allow further understanding of their characteristics and could clarify their functional role. RESULTS: Here, we propose, thoroughly explain and discuss a full computational procedure to study in-depth DNA dense areas of transcription factor accumulation and identify HOT zones. This methodology, developed as a computationally efficient parametric algorithm implemented in an R/Bioconductor package, uses a systematic approach with two alternative methods to examine transcription factor bindings and provide comparative and fully-reproducible assessments. It offers different resolutions by introducing three distinct types of accumulation, which can analyze DNA from single-base to region-oriented levels, and a moving window, which can estimate the influence of the neighborhood for each DNA base under exam. CONCLUSIONS: We quantitatively assessed the full procedure by using our implemented software package, named TFHAZ, in two example applications of biological interest, proving its full reliability and relevance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05528-1. |
format | Online Article Text |
id | pubmed-10590011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105900112023-10-22 Identification of transcription factor high accumulation DNA zones Cascianelli, Silvia Ceddia, Gaia Marchesi, Alberto Masseroli, Marco BMC Bioinformatics Software BACKGROUND: Transcription factors (TF) play a crucial role in the regulation of gene transcription; alterations of their activity and binding to DNA areas are strongly involved in cancer and other disease onset and development. For proper biomedical investigation, it is hence essential to correctly trace TF dense DNA areas, having multiple bindings of distinct factors, and select DNA high occupancy target (HOT) zones, showing the highest accumulation of such bindings. Indeed, systematic and replicable analysis of HOT zones in a large variety of cells and tissues would allow further understanding of their characteristics and could clarify their functional role. RESULTS: Here, we propose, thoroughly explain and discuss a full computational procedure to study in-depth DNA dense areas of transcription factor accumulation and identify HOT zones. This methodology, developed as a computationally efficient parametric algorithm implemented in an R/Bioconductor package, uses a systematic approach with two alternative methods to examine transcription factor bindings and provide comparative and fully-reproducible assessments. It offers different resolutions by introducing three distinct types of accumulation, which can analyze DNA from single-base to region-oriented levels, and a moving window, which can estimate the influence of the neighborhood for each DNA base under exam. CONCLUSIONS: We quantitatively assessed the full procedure by using our implemented software package, named TFHAZ, in two example applications of biological interest, proving its full reliability and relevance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05528-1. BioMed Central 2023-10-20 /pmc/articles/PMC10590011/ /pubmed/37864168 http://dx.doi.org/10.1186/s12859-023-05528-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Cascianelli, Silvia Ceddia, Gaia Marchesi, Alberto Masseroli, Marco Identification of transcription factor high accumulation DNA zones |
title | Identification of transcription factor high accumulation DNA zones |
title_full | Identification of transcription factor high accumulation DNA zones |
title_fullStr | Identification of transcription factor high accumulation DNA zones |
title_full_unstemmed | Identification of transcription factor high accumulation DNA zones |
title_short | Identification of transcription factor high accumulation DNA zones |
title_sort | identification of transcription factor high accumulation dna zones |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590011/ https://www.ncbi.nlm.nih.gov/pubmed/37864168 http://dx.doi.org/10.1186/s12859-023-05528-1 |
work_keys_str_mv | AT cascianellisilvia identificationoftranscriptionfactorhighaccumulationdnazones AT ceddiagaia identificationoftranscriptionfactorhighaccumulationdnazones AT marchesialberto identificationoftranscriptionfactorhighaccumulationdnazones AT masserolimarco identificationoftranscriptionfactorhighaccumulationdnazones |