Comparer des méthodes

	Recherche de profils HMMER	Binning métagénomique
Domaine	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline
Année d'origine	1994	2011
Auteur d'origine	Sean Eddy	Jillian Banfield
Type	Probabilistic sequence search pipeline	Sequence assembly and clustering pipeline
Source fondatrice	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.	Kang, D. D., Froula, J., Egan, R., & Wang, Z. (2015). MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ, 3, e1165.
Alias	profile-hidden Markov model, HMM profile search, HMMER	metagenomic assembly, genome binning, MAG recovery
Apparentées	3	3
Résumé	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.	Metagenomic binning partitions assembled contigs from complex microbial communities into distinct genome bins, each representing an individual organism or strain. Pioneered by Banfield and colleagues, this pipeline isolates single-organism genomes (metagenome-assembled genomes or MAGs) from environmental samples without requiring cultivated isolates.