Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mkusanyiko wa Transkriptomu wa De Novo× | Utafutaji wa Wasifu wa HMMER× | |
|---|---|---|
| Nyanja | Bioinformatiki | Bioinformatiki |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2011 | 1994 |
| Mwanzilishi≠ | Aviv Regev | Sean Eddy |
| Aina≠ | Sequence assembly pipeline | Probabilistic sequence search pipeline |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | transcriptome assembly, de novo assembly, RNA-Seq assembly | profile-hidden Markov model, HMM profile search, HMMER |
| Zinazohusiana | 3 | 3 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
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