We are pleased to announce release of UCSC's
SAM Version 3.2 with SAM-T99
http://www.cse.ucsc.edu/research/compbio/sam.html
The Sequence Alignment and Modeling system (SAM) is a collection of
flexible software tools for creating, refining, and using linear
(profile) 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.
Version 3.0 introduced scripts for the SAM-T99 method of remote
homology detection. SAM-T99 is an iterative HMM search method for
creating an HMM from a single protein sequence or seed alignment using
iterative search of a protein database. The method is currently the
most sensitive purely-sequence-based remote homology detection
algorithm [Park et al, JMB 284(4):1201-1210,1998]. SAM-T99 is based
on successful methods created for the CASP2 and CASP3 protein
structure prediction experiments [Karplus et al, Proteins,
Sup.1:134-139,1997 and Sup.3:121-125,1999].
Other additions include the use of reverse-sequence null models
[Barrett et al, Bioinformatics 14(10):846-856,1998], the calculation
of E-values, and constrained alignment and training.
Version 3.2 improves SAM-T99, and includes multi-track (e.g., primary
sequence and predicted secondary structure) HMMs, far faster sequence
I/O, secondary structure alphabets, posterior-decoded alignments,
greater access to posteriors, and a 10% faster inner loop.
SAM is available in binary distribution for pc linux, alpha osf, sun
solaris, and sgi irix platforms.
Papers, documentation, and more information on SAM can be found at:
http://www.cse.ucsc.edu/research/compbio/sam.html
Servers for performing SAM-T99 structure prediction using our library
of HMMs is available at:
http://www.cse.ucsc.edu/research/compbio/HMM-apps/HMM-applications.html
Kevin Karplus
Mark Diekhans
Richard Hughey
saminfo at cse.ucsc.edu
Department of Computer Engineering
University of California
Santa Cruz, CA 95064
