Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Nätverksbaserad variantidentifiering× | RNA-seq Differential Expression× | |
|---|---|---|
| Ämnesområde | Bioinformatik | Bioinformatik |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 2017–2018 | 2008–2010 (RNA-seq DE methodology established) |
| Upphovsperson≠ | Erik Garrison, Paten lab (UCSC); Hannes Eggertsson, deCODE Genetics | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Typ≠ | Computational genomics pipeline | Quantitative genomics pipeline |
| Ursprungskälla≠ | Garrison, E., Sirén, J., Novak, A. M., Hickey, G., Eizenga, J. M., Dawson, E. T., Jones, W., Garg, S., Markello, C., Lin, M. F., Paten, B., & Durbin, R. (2018). Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nature Biotechnology, 36(9), 875–879. DOI ↗ | 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 ↗ |
| Alias | graph-genome variant calling, variation graph genotyping, vg-based variant calling, pangenome variant calling | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Närliggande | 6 | 6 |
| Sammanfattning≠ | Network-based (graph-genome) variant calling replaces the conventional single linear reference genome with a variation graph — a network in which nodes represent sequence segments and edges represent known alternative paths through the genome. Reads are mapped onto this graph, enabling detection of SNPs, indels, and structural variants with substantially lower reference bias than linear-reference pipelines. Key tools include the Variation Graph Toolkit (vg) and Graphtyper. | 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. |
| ScholarGateDatamängd ↗ |
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