Table of Contents
Motifs, profiles and hidden Markov models
The objects of our study
The statistics of biological sequences can be global or local
Motifs - Sites - Signals - Domains
Specific searches / predictions
Motifs and models
PPT Slide
Prosite patterns
Consensus in sequences
Example: C2H2 zinc finger DNA binding domain
Searching with regular expressions
Regular expressions can be limiting
Cys-Cys-His-His profile: sequence logo form
Calculation of a position-specific scoring matrix (PSSM) from counts
Derivation of PSSM entries
Use of a PSSM to find sites
Representation of motifs: the next steps
Profiles
Derivation of a profile for Ig domains
PPT Slide
Hidden Markov models
HMMs: generalities
PPT Slide
Hidden Markov models:extensions
PPT Slide
PPT Slide
A very short profile HMM
How profile HMMs work: in brief
Pfam domain-HMMs
PPT Slide
PPT Slide
Designing the HMM, I
Designing the HMM, II
Designing the HMM, III
HMM: decoding
CC-PROBABILITY PROFILE
References
HMM-type software available
References
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