Hidden Markov Models
Bioinformatics & Genomics
Companies, Publishers, & Books
Compendiums & Lists of Resource Links
Compounds & Enzymes
Educational & Information Resources
Genomics & DNA Sequence Analysis
Hidden Markov Models
Major Sites & Organizations
Metabolic Pathway Databases & Related
Molecular Modeling & Visualization
Motif, Domain, Profile, Pattern, & Repeat Searches
Multiple Alignment & Phylogeny
Protein & Nucleic Acid Search Servers
Protein Analysis from Sequence
Sites with Multiple or Integrated Tools
Software Catalogues, Lists, & Downloads
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Dirichlet Mixtures and other Regularizers
[University of California – Santa Cruz] This site contains pointers to some of the Dirichlet mixtures and other regularizers (methods for estimating probabilities of amino acids given small samples).
[Analytical Biostatistics Section – NIH] “FORESST is a database (with search engine) of hidden Markov models that characterize secondary structure representations of protein structural domains.”
Hidden Markov Models and Protein Sequence Analysis
[University of California – Santa Cruz] (Rachel Karchin) An on-line paper providing an introduction to the sophisticated and flexible statistical model called the hidden Markov model (HMM) and its use as a tool in the study of protein molecules.
[Washington Univ., St. Louis] A protein sequence analysis tool that profiles hidden Markov models (profile HMMs) that can be used to do sensitive database searching using statistical descriptions of a sequence family’s consensus.
[Net-ID, Inc.] A general purpose hidden Markov model (HMM) simulator for biological sequence analysis.
[Institute of Enzymology] Predicts transmembrane helices and topology of proteins.
Making the most of your hidden Markov models
[University of Califoria, Santa Cruz] (ISMB99 Tutorial Material) This tutorial is intended for people who know what HMMs are but want to know how to use them most effectively. It details the tricks
used in the SAM-T98 method (in 1998 the best method for remote homology detection in proteins). Full text articles are also provided.
[SWBIC] Protein Classification through the Assessment of Predicted Secondary Structure (PCAPSS) is a method for identifying remote protein homologs in the Protein Data Bank that share a similar secondary
structure to the query protein. PCAPSS was designed to be used with orphan or hypothetical proteins, i.e.,
those for which amino acid sequence searches fail to find similar sequences with functional annotation. Its purpose
is to generate hypotheses for structure and function for such hypothetical or putative protein sequences based on
[Washington University, St. Louis] Pfam is a large collection of multiple sequence alignments and hidden Markov models covering many common protein domains based on the Swissprot 38 and SP-TrEMBL 11 protein sequence databases.
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Sequence Alignment and Modeling System
[University of California – Santa Cruz] (SAM) SAM is a collection of flexible software tools for creating, refining, and using linear hidden Markov models for biological sequence analysis.
[Medical Research Council Laboratory of Molecular Biology, Cambridge] The purpose of this server is to provide structural (and hence implied functional) assignments to protein sequences at the Structural Classification of Proteins (SCOP)
superfamily level. SCOP provides a classification of all proteins in the Protein Data Bank (PDB). Whole genome assignment results are also provided.
[CBS] Predicts transmembrane helices in proteins.
[UCSC] A hidden Markov Model protein structure prediction server. A library of hidden Markov Models, one per PDB structure and containing approximately 2500 HMMs is on this server.