Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Epigenome-Wide Association Study (EWAS) Baseada em Rede× | Análise de Expressão Diferencial de RNA-seq× | |
|---|---|---|
| Área | Bioinformática | Bioinformática |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 2010s, consolidating 2012–2018 | 2008–2010 (RNA-seq DE methodology established) |
| Autor original≠ | Adapted from EWAS (Rakyan et al., 2011) and network-based genomic methods (e.g., Ideker & Sharan, 2008) | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Tipo≠ | Integrative epigenomic analysis | Quantitative genomics pipeline |
| Fonte seminal≠ | Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541. link ↗ | Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. DOI ↗ |
| Outros nomes | network EWAS, network-integrated EWAS, graph-based EWAS, network-based DNA methylation analysis | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Relacionados | 6 | 6 |
| Resumo≠ | Network-based EWAS extends conventional epigenome-wide association studies by overlaying differentially methylated positions or regions onto biological interaction networks — such as protein-protein interaction, co-expression, or gene regulatory networks — to identify functionally coherent epigenetic modules rather than isolated CpG hits. This integration increases statistical power for detecting weak signals and reveals coordinated epigenetic dysregulation across pathways. | RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values. |
| ScholarGateConjunto de dados ↗ |
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