We are pleased to announce release of UCSC's
SAM Version 3.0 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.
For the first time, SAM includes 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.
SAM is available in binary distribution for unix, linux, and freebsd
platforms.
SAM is FREE for academic and non-profit use, but requires a signed
hardcopy license agreement if you do not already have one. The
license agreement is available from the WWW site.
The license agreement, papers, documentation, and more information on
SAM can be found at:
http://www.cse.ucsc.edu/research/compbio/sam.html
Servers for performing SAM-T98 structure prediction (SAM-T99 to
available soon) using our library of HMMs is available at:
http://www.cse.ucsc.edu/research/compbio/HMM-apps/HMM-applications.html
The server will soon be accelerated with the UCSC Kestrel parallel processor:
http://www.cse.ucsc.edu/research/kestrel
Kevin Karplus
Mark Diekhans
Richard Hughey
saminfo at cse.ucsc.edu
Department of Computer Engineering
University of California
Santa Cruz, CA 95064
