Profile hidden Markov models

By Sean R. Eddy. 1998

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, methodology): “Efficient sequence search methods have been the computational foundation for extracting biological information from sequences (Altschul et al., 1990; Altschul & Koonin, 1998; Eddy, 1998; Remmert et al., 2011).”
Evaluating Protein Transfer Learning with TAPE (2019, methodology): “The key technique for modeling sequence relationships in computational biology is alignment [13, 16, 21, 22].”
Direct-coupling analysis of residue coevolution captures native contacts across many protein families (2011, methodology): “…a multiple sequence alignment (MSA) of a large number of sequences of a given protein domain, extracted using Pfam’s Hidden Markov Models (HMMs) (21, 22), the basic quantities in this context are the frequency count The raw statistical correlation obtained above suffers from a sampling bias,…”
Learning generative models for protein fold families (2011, methodology): “The y-axis shows the decrease in the per-position imputation error when moving from a proﬁle-HMM model to the corresponding MRF, while the x-axis bins this improvement according to the number of edges in the MRF at that position.”
The HHpred interactive server for protein homology detection and structure prediction (2005, methodology): “In a recent study (28), in which we bench-marked HHsearch, the method for HMM–HMM comparison employed by our server, together with PSI-BLAST, HMMER, PROF_SIM and COMPASS (3,6,7,29), HHsearch was found to possesses the highest sensitivity and alignment accuracy.”
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “If the alignment returned by HHBlits had fewer than 1000 sequences we used JackHMMER with an e-value threshold of 10 (Remmert et al., 2012; Eddy, 1998).”
Dissecting the Specificity of Protein-Protein Interaction in Bacterial Two-Component Signaling: Orphans and Crosstalks (2011, methodology): “gests that our approach should already be applicable for less amplified systems. Methods Data extraction 769 bacterial genomes were scanned using HMMER 2 with the Pfam 22.0 hidden Markov models (HMM, [35]) for the following Sensor Kinase (SK) domains: “HisKA” (PF00512), “HWE_HK” (PF07536), “HisKA_2””
HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. (2000, background): “In contrast to the more familiar familyspeci®c HMMs (Sonnhammer et al., 1997, Eddy, 1998), which have shown considerable power in remote homolog detection, HMMSTR models features common to protein sequences and structures generally.”
The human ATP-binding cassette (ABC) transporter superfamily. (2001, unknown): “This hypothesis is supported by studies showing that CF homozygotes fail to secrete chloride ions in response to a variety of stimulants, and a study in mice in which heterozygous null animals showed reduced intestinal fluid secretion in response to cholera toxin (Gabriel et al. 1993).”
Intraspecific ITS Variability in the Kingdom Fungi as Expressed in the International Sequence Databases and Its Implications for Molecular Species Identification (2008, methodology): “Hidden Markov Models (HMMs) of the fl anking nuclear small sub-unit (nSSU), 5.8S, and nuclear large sub-unit (nLSU) were constructed from the large-scope fungal alignments of Tehler et al. (2003); Larsson et al. (2004); Binder et al. (2005); and James et al. (2006) using HMMER 2.3.2 (Eddy, 1998).”
Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments (2010, background): “In particular, so-called profile hidden Markov models [1] of protein families and domains have been highly successful in identifying sequences that have similar function and fold into a common structure, making them among the most important tools in functional genomics, see e.g. [2].”