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| Analyse d'écran CRISPR× | Recherche de profils HMMER× | |
|---|---|---|
| Domaine | Bio-informatique | Bio-informatique |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 2013 | 1994 |
| Auteur d'origine≠ | Feng Zhang | Sean Eddy |
| Type≠ | High-throughput genetic screen pipeline | Probabilistic sequence search pipeline |
| Source fondatrice≠ | Shalem, O., Sanjana, N. E., Hartenian, E., Shi, X., Scott, D. A., Mikkelsen, T. S., ... & Zhang, F. (2014). Genome-scale CRISPR-Cas9 knockout screening in human cells. Science, 343(6166), 84-87. 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 ↗ |
| Alias≠ | CRISPR pooled screen, genetic screen analysis | profile-hidden Markov model, HMM profile search, HMMER |
| Apparentées | 3 | 3 |
| Résumé≠ | CRISPR screen analysis processes data from pooled genetic screens using CRISPR-Cas9 to identify genes required for cell growth, survival, or phenotype in specific conditions. Developed by Zhang, Sanjana, and others, this computational pipeline transforms sequencing readouts of guide RNA abundances into ranked lists of functional genes. | 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. |
| ScholarGateJeu de données ↗ |
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