File	Date	Author	Commit
app	2013-02-26	Andre Yoshiaki Kashiwabara	[88a3e5] Updating README file
doc	2013-02-26	Andre Yoshiaki Kashiwabara	[33e859] Manual updating
examples	2013-02-26	Andre Yoshiaki Kashiwabara	[d9868d] Blosum Pair-HMM
lang	2013-02-22	Andre Yoshiaki Kashiwabara	[7b8482] Merging together the modfications
src	2013-02-26	Andre Yoshiaki Kashiwabara	[88a3e5] Updating README file
tops-R-package	2011-05-11	Andre Yoshiaki Kashiwabara	[664b5f] Merge branch 'master' of /home/yoshiaki/git/tops
.gitignore	2013-02-22	Andre Yoshiaki Kashiwabara	[7b8482] Merging together the modfications
.gitmodules	2013-02-22	Andre Yoshiaki Kashiwabara	[7b8482] Merging together the modfications
CMakeLists.txt	2013-02-26	Andre Yoshiaki Kashiwabara	[88a3e5] Updating README file
FindPerlLibs.cmake	2010-07-29	Andre Yoshiaki Kashiwabara	[6d62cb] ToPS: an object oriented framework of probabili...
FindRmath.cmake	2010-07-29	Andre Yoshiaki Kashiwabara	[6d62cb] ToPS: an object oriented framework of probabili...
LICENSE.txt	2011-02-14	Andre Yoshiaki Kashiwabara	[cdf119] Merge branch 'HEAD', remote branch 'igor/master...
README	2013-02-26	Andre Yoshiaki Kashiwabara	[88a3e5] Updating README file
update_version.pl	2011-05-11	Andre Yoshiaki Kashiwabara	[664b5f] Merge branch 'master' of /home/yoshiaki/git/tops

Read Me

Overview
=========

ToPS is an objected-oriented framework implemented using C++ that facilitates the integration of probabilistic models for sequences over a user defined alphabet. ToPS contains the implementation of five distinct models to analyze discrete sequences:

1. Independent and identically distributed model
2. Variable-Length Markov Chain (VLMC)
3. Inhomogeneous Markov Chain
4. Hidden Markov Model
5. Pair Hidden Markov Model
6. Profile Hidden Markov Model
7. Similarity Based Sequence Weighting
8. Generalized Hidden Markov Model (GHMM)

The user can implement models either by manual description of the probability values in a configuration file, or by using training algorithms provided by the system. The ToPS framework also includes a set of programs that implement bayesian classifiers, sequence samplers, and sequence decoders. Finally, ToPS is an extensible and portable system that facilitates the implementation of other probabilistic models, and the development of new programs.

Example of usage is in "example" folder.

Please feel free to contact me if you have any question: André Yoshiaki Kashiwabara <akashiwabara@usp.br>

Documentation
=============

http://tops.sourceforge.net/tutorial.pdf
http://tops.sourceforge.net/api.html


Git Repository
==============

You can download the development version of ToPS by executing the command below:

git clone git://tops.git.sourceforge.net/gitroot/tops/tops

Platforms
=========

ToPS was designed to run on Unix/Linux operating systems. Tested platforms include: MacOS X, and Ubuntu linux.

Software Requirement
====================

ToPS was written in C++. It was compiled using the g++ version 4.2.1 and it requires

- Boost C++ Libraries version 1.52
- CMake
- Git

Installing ToPS
===============

   1. Gunzip and untar the package

      git clone git://tops.git.sourceforge.net/gitroot/tops/tops 
      git submodule update --init

      This will create a directory named tops

   2. Go to the tops_v1 directory:

       cd tops

   3. Run the configuration script:

      cmake .

   4. Run make

       make

   5. Run make install

      sudo make install

   6. If you are using linux run ldconfig

      sudo ldconfig