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SIM - Alignment Tool for protein
AMAS - Analyse Multiply Aligned Sequences
ToPLign: Toolbox for Protein Alignment
MSA
DIALIGN
AllAll
MaxHom
MRA
GeneBee
Match-Box
Protein Structure Prediction
Sequence Alignment and Modeling System
THE MEME SYSTEM - Multiple EM for Motif Elicitation
Meta-MEME motif-based hidden Markov models
SIM - Alignment Tool for protein
SIM is a program which finds a user-defined number of best non-intersecting
alignments between two protein sequences or within a sequence.
Once the alignment is computed, you can view it using LALNVIEW, a graphical viewer program for pairwise alignments
AMAS - Analyse Multiply Aligned Sequences
Visualization of physico-chemical properties of the columns of an alignment
ToPLign: Toolbox for Protein Alignment
Computing, analysis and visualization of pairwise, multiple, threading,
and parametric alignments.
MSA
(Close-to-) Optimal Alignments using the Carrillo-Lipman bound
DIALIGN
While standard alignment methods of alignment rely on comparing single
residues and imposing gap penalties, DIALIGN constructs alignments by
comparing whole segments of the sequences. No gap penalty is employed.
This point of view is especially adequate if sequences are not globally
related but share only local similarities as is the case in genomic DNA
sequences and in many protein families.
AllAll
Calculate Phylogenetic Trees, Alignments, dSplits, Probabilistic
ancestral sequence, {Kabat-Wu, probability, maximum likelihood}
variation index, prediction of Surface/Interior/Active site, prediction
of parse regions.
MaxHom
MaxHom Alignments (offered together w/ structure prediction)
MRA
MRA, Alignment for Many Sequences by Comparison of Dot-matrices.
GeneBee
GeneBee Multiple alignment: pairwise motifs to multiple motifs to "supermotifs"
to construction of multiple alignment.
Match-Box
The Match-Box multiple sequence alignment method circumvents the gap
penalty requirement: in the Match-Box method the gaps are the result of
the alignment and not a governing parameter of the matching procedure.
The method produces reliable results, as assessed by the tests performed on protein families of known structures and of low sequence similarity.
A reliability score is computed in relation with a threshold of similarity progressively raised to extend the aligned regions to their maximal length. The score obtained at each position of the final alignment is printed below the sequences and allows a discriminant reading of each aligned region.
Several additional outputs present pairwise similarity analyses in order to allow delineation of relevant subsets of related sequences and to avoid alignment of unrelated sequences.
Protein Structure Prediction
This is a hidden Markov model (HMM) protein structure prediction server.
The server has used UCSC's SAM-T98 method to create a library of HMMs, one per PDB structure (about 2500 HMMs total). You can search this database of HMMs with a protein sequence.
Sequence Alignment and Modeling System
The Sequence Alignment and Modeling system (SAM) is a collection of
flexible software tools for creating, refining, and using linear
hidden Markov models for biological sequence analysis. The model
states can be viewed as representing the sequence of columns in a
multiple sequence alignment, with provisions for arbitrary
position-dependent insertions and deletions in each sequence. The
models are trained on a family of protein or nucleic acid sequences
using an expectation-maximization algorithm and a variety of
algorithmic heuristics. A trained model can then be used to both
generate multiple alignments and search databases for new members of
the family. SAM is written in the C programming language for Unix
machines and MasPar parallel computers, and includes extensive
documentation.
THE MEME SYSTEM - Multiple EM for Motif Elicitation
MEME is a tool for discovering motifs in a group of related DNA or
protein sequences.
A motif is a sequence pattern that occurs repeatedly in a group of related protein or DNA sequences. MEME represents motifs as position-dependent letter-probability matrices which describe the probability of each possible letter at each position in the pattern. Individual MEME motifs do not contain gaps. Patterns with variable-length gaps are split by MEME into two or more separate motifs.
MEME takes as input a group of DNA or protein sequences (the training set) and outputs as many motifs as requested. MEME uses statistical modeling techniques to automatically choose the best width and description for each motif.
Meta-MEME motif-based hidden Markov models
Meta-MEME is a software toolkit for building and using motif-based hidden
Markov models of DNA and proteins. The input to Meta-MEME is a set of similar
protein sequences, as well as a set of motif models discovered by MEME.
Meta-MEME combines these models into a single, motif-based hidden Markov
model and uses this model to produce a multiple alignment of the original set of
sequences and to search a sequence database for homologs.
Any Comments, Questions? Support@hgmp.mrc.ac.uk