دورية أكاديمية
Regulatory landscape enrichment analysis (RLEA): a computational toolkit for non-coding variant enrichment and cell type prioritization
| العنوان: | Regulatory landscape enrichment analysis (RLEA): a computational toolkit for non-coding variant enrichment and cell type prioritization |
|---|---|
| المؤلفون: | Samuel Rosean, Eric A. Sosa, Dónal O’Shea, Srilakshmi M. Raj, Cathal Seoighe, John M. Greally |
| المصدر: | BMC Bioinformatics, Vol 25, Iss 1, Pp 1-18 (2024) |
| بيانات النشر: | BMC, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Biology (General) |
| مصطلحات موضوعية: | Cell-type prioritization, Non-coding enrichment, Chromatin architecture, GWAS, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5 |
| الوصف: | Abstract Background As genomic studies continue to implicate non-coding sequences in disease, testing the roles of these variants requires insights into the cell type(s) in which they are likely to be mediating their effects. Prior methods for associating non-coding variants with cell types have involved approaches using linkage disequilibrium or ontological associations, incurring significant processing requirements. GaiaAssociation is a freely available, open-source software that enables thousands of genomic loci implicated in a phenotype to be tested for enrichment at regulatory loci of multiple cell types in minutes, permitting insights into the cell type(s) mediating the studied phenotype. Results In this work, we present Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation and demonstrate its capability to test the enrichment of 12,133 variants across the cis-regulatory regions of 44 cell types. This analysis was completed in 134.0 ± 2.3 s, highlighting the efficient processing provided by GaiaAssociation. The intuitive interface requires only four inputs, offers a collection of customizable functions, and visualizes variant enrichment in cell-type regulatory regions through a heatmap matrix. GaiaAssociation is available on PyPi for download as a command line tool or Python package and the source code can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociationTest . Conclusions GaiaAssociation is a novel package that provides an intuitive and efficient resource to understand the enrichment of non-coding variants across the cis-regulatory regions of different cells, empowering studies seeking to identify disease-mediating cell types. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1471-2105 |
| العلاقة: | https://doaj.org/toc/1471-2105Test |
| DOI: | 10.1186/s12859-024-05794-7 |
| الوصول الحر: | https://doaj.org/article/4060e01f1a0740f48e1cf9011cdeebb1Test |
| رقم الانضمام: | edsdoj.4060e01f1a0740f48e1cf9011cdeebb1 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14712105 |
|---|---|
| DOI: | 10.1186/s12859-024-05794-7 |