Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Де ново сборка транскриптома× | Поиск по профилю HMMER× | |
|---|---|---|
| Область | Биоинформатика | Биоинформатика |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2011 | 1994 |
| Автор метода≠ | Aviv Regev | Sean Eddy |
| Тип≠ | Sequence assembly pipeline | Probabilistic sequence search pipeline |
| Основополагающий источник≠ | Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D. A., Amit, I., ... & Regev, A. (2011). Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology, 29(7), 644-652. DOI ↗ | Krogh, A., Brown, M., Mian, I. S., Sjölander, K., & Haussler, D. (1994). Hidden Markov models in computational biology: applications to protein modeling. Journal of Molecular Biology, 235(5), 1501-1531. DOI ↗ |
| Другие названия | transcriptome assembly, de novo assembly, RNA-Seq assembly | profile-hidden Markov model, HMM profile search, HMMER |
| Связанные | 3 | 3 |
| Сводка≠ | De novo transcriptome assembly reconstructs full-length messenger RNA sequences directly from sequencing reads without requiring a reference genome. Pioneered by Regev, Haas, and colleagues, this pipeline enables transcript discovery in non-model organisms and detection of novel isoforms, fusion genes, and splice variants. | HMMER profile search identifies distant protein sequence homologs using probabilistic models of protein families, known as profile Hidden Markov Models (HMMs). Developed by Eddy and colleagues, this method captures sequence variation patterns within protein families and detects homologs with far greater sensitivity than position-weight matrices or pairwise alignment. |
| ScholarGateНабор данных ↗ |
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