The clearcut from sheneman

$Id: README,v 1.3 2006/09/01 04:55:39 sheneman Exp $

******************************************************************************

                       Clearcut :: Relaxed Neighbor Joining

                             (Version 1.0.9, Feb. 2009)

******************************************************************************

INTRODUCTION:
-------------

Clearcut is the reference implementation for the Relaxed Neighbor Joining (RNJ)
algorithm by J. Evans, L. Sheneman, and J. Foster from the Initiative
for Bioinformatics and Evolutionary Studies (IBEST) at the University of 
Idaho.

Details of RNJ are published here:

  Evans, J., L. Sheneman, and J.A. Foster (2006) Relaxed Neighbor-Joining: 
  A Fast Distance-Based Phylogenetic Tree Construction Method, 
  J. Mol. Evol., 62, 785-792

Relaxed Neighbor-Joining (RNJ) is a fast approximation to the 
Neighbor Joining algorithm originally described in:

  Saitou, N. and M. Nei (1987), The Neighbor-Joining method: A new method
  for reconstructing phylogenetic trees., Mol. Biol. Evol.  4:406-425

and revised in:

  Studier, J. and K. Keppler (1988), A note on the neighbor-joining
  algorithm of Saitou and Nei., Mol. Biol. Evol.  5:729-731


Whereas traditional Neighbor-Joining has a cubic time complexity with
respect to the number of input sequences,  Relaxed Neighbor-Joining has 
a drastically reduced, sub-cubic time complexity for the average case.  
In addition to being significantly (and asymptotically) 
more efficient, Relaxed Neighbor-Joining shares some nice theoretical
properties with Traditional Neighbor-Joining.  In particular, if distances
are truly additive (self-consistent), RNJ will reconstruct the true tree
that is consistent with those additive distances.  

For non-additive distances, RNJ will sample the space of similar trees 
more thoroughly than traditional NJ by greedily joining nodes which
represent a locally (as opposed to globally) minimum distance between 
nodes. This results in sampling more trees than it is possible to explore 
through NJ alone.  Additionally, it has been empirically shown that 
for non-additive distances, RNJ is capable of reconstructing trees which 
are nearly qualitatively indistinct from trees built via the conventional 
Neighbor-Joining algorithm.

******************************************************************************


INSTALLING CLEARCUT:
--------------------

Clearcut was developed and tested primarily under Redhat Linux 7.2 and 
Redhat Enterprise Linux 3 on the Pentium 3 and Pentium 4 architectures.
However, Clearcut should compile and run easily on other UNIX and
UNIX-like operating systems.  For example, clearcut builds cleanly on 
the Sun Solaris 9 and Apple OS X platforms.

Good compiler optimization can result in an approximate 2X overall speedup 
for Clearcut.  Compiler optimizations for GCC under Linux on the 
Pentium 4 architecture have been thoroughly explored.  The Makefile
included in this distribution has several possible sets of compiler 
flags available.  Uncomment the appropriate set of compiler flags
for your particular architecture and compiler combination.  The
default CFLAGS is set to basic level 3 optimization with gcc, but significant
additional compiler optimizations are most likely available/desirable.


To build Clearcut:

  + Unzip and extract the distribution.
  + Edit "Makefile" and select the appropriate optimization flags
  + Type "make" to compile and link clearcut
  + Type "make install" to install clearcut on your system


******************************************************************************


RUNNING CLEARCUT:
-----------------
	
Clearcut has a variety of possible command-line arguments.  To see the 
available arguments, type:  

 $> clearcut --help

Clearcut is capable of reading either a pre-computed distance matrix in
approximate PHYLIP format, or it can input an alignment in FASTA format.
In addition to fully symmetric distance matrices, clearcut can parse 
upper and lower diagonal half-matrices.

Clearcut can build a distance matrix from a Multiple Sequence Alignment (MSA)
of either DNA or Protein sequences.  When building a distance matrix, 
percent pairwise differences can be corrected for multiple hits using either
the Jukes-Cantor or Kimura correction methods as described in:

    Kimura, M. (1980), A simple method for estimating evolutionary
    rates of base substitutions through comparative studies of
    nucleotide sequences.  J. Mol. Evol., 16, 111-120

    Kimura, M. (1983), The Neutral Theory of Molecular Evolution.
    p. 75., Cambridge University Press, Cambridge, England

    Jukes, T.H. (1969), Evolution of protein molecules.  In H.N. Munro (Ed.),
    Mammalian Protein Metabolism, Volume III, Chapter 24, pp. 21-132. 
    New York: Academic Press
	

By default, Clearcut will perform joins in a random fashion in order to 
minimize systematic algorithmic bias.  This option can be disabled using
the --norandom runtime option.

By default, Clearcut uses Relaxed Neighbor-Joining, although Traditional
Neighbor-Joining can be invoked with the --neighbor runtime option.
	

******************************************************************************


AUTHOR/MAINTAINER:
------------------


Clearcut is maintained by:  Luke Sheneman
                            [email protected]
                            https://github.com/sheneman/clearcut


Please contact the maintainer with questions, bug reports, and feedback!!



******************************************************************************

LICENSE:
--------

Clearcut is distributed under the BSD license, which is described in 
the LICENSE file that is bundled with this program.

******************************************************************************
sheneman / clearcut Goto Github PK

clearcut's Introduction

clearcut's People

Contributors

Stargazers

Watchers

Forkers

clearcut's Issues

Please tag your releases

Recommend Projects

React

Vue.js

Typescript

TensorFlow

Django

Laravel

D3

Recommend Topics

javascript

web

server

Machine learning

Visualization

Game

Recommend Org

Facebook

Microsoft

Google

Alibaba

D3

Tencent
