Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Выравнивание последовательностей× | Анализ одноклеточной РНК-секвенирования (scRNA-seq)× | |
|---|---|---|
| Область | Биоинформатика | Биоинформатика |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1970 (global alignment); 1981 (local alignment) | 2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016 |
| Автор метода≠ | Saul B. Needleman & Christian D. Wunsch (global); Temple F. Smith & Michael S. Waterman (local) | Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015) |
| Тип≠ | Computational sequence analysis technique | High-throughput single-cell transcriptomic profiling pipeline |
| Основополагающий источник≠ | Needleman, S. B., & Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48(3), 443–453. DOI ↗ | Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5), 495–502. DOI ↗ |
| Другие названия | pairwise alignment, multiple sequence alignment, MSA, sequence comparison | scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Sequence alignment is a foundational bioinformatics technique that arranges two or more DNA, RNA, or protein sequences to reveal regions of similarity, infer evolutionary relationships, identify functional domains, and map sequencing reads to reference genomes. It underpins virtually every downstream genomic analysis, from variant calling and gene expression quantification to phylogenetics and structural annotation. | Single-cell RNA sequencing (scRNA-seq) analysis characterises gene expression at the resolution of individual cells, enabling discovery of cell types, states, and transitions that are invisible in bulk transcriptomics. Starting from raw sequencing reads, the workflow produces a cell-by-gene count matrix and proceeds through quality control, normalisation, dimensionality reduction, unsupervised clustering, cell-type annotation, and a range of downstream analyses such as trajectory inference and differential expression between cell populations. |
| ScholarGateНабор данных ↗ |
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