Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Филогенетический анализ× | Анализ дифференциальной экспрессии РНК-сек (DE)× | |
|---|---|---|
| Область | Биоинформатика | Биоинформатика |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1960s-1981 (distance trees ~1967; ML framework formalised 1981) | 2008–2010 (RNA-seq DE methodology established) |
| Автор метода≠ | Joseph Felsenstein (maximum likelihood framework); Walter Fitch and Emanuel Margoliash (distance methods) | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Тип≠ | Computational inference method | Quantitative genomics pipeline |
| Основополагающий источник≠ | Felsenstein, J. (2004). Inferring Phylogenies. Sinauer Associates. ISBN: 978-0878931774 | 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 ↗ |
| Другие названия | molecular phylogenetics, phylogenetic inference, evolutionary tree reconstruction, phylogenomics | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Связанные≠ | 5 | 6 |
| Сводка≠ | Phylogenetic analysis reconstructs the evolutionary history of organisms, genes, or proteins by comparing molecular sequence data and estimating the branching tree that best explains observed similarities and differences. Rooted in the work of Felsenstein and colleagues from the 1960s onward, it is a cornerstone technique in evolutionary biology, microbiology, epidemiology, and comparative genomics, supporting tasks from tracing viral outbreak origins to classifying novel species. | 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. |
| ScholarGateНабор данных ↗ |
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