MotEvo: Integrated Bayesian probabilistic methods for  inferring
regulatory sites and motifs on multiple alignments of DNA sequences 
********************************************************************

CONTENTS:

0. Compiling MotEvo
1. Input files and their format
   1.1 Multiple sequence alignments.
       1.1.1 How do I obtain multiple alignments? 
   1.2 Species tree.
       1.2.1 Where do I obtain a species tree?
   1.3 Positional weight matrices
2. Running modes and their parameters
   2.0 General options and parameters
   2.1 TFBS: Predicting transcription factor binding sites
       2.1.1 Parameters for TFBS prediction
   2.2 WMREF: Weight matrix refinement
       2.2.1 Parameters for WM refinement.
   2.3 ENH: Predicting enhancers
       2.3.1 Parameters for predicting enhancers
3. Output files
   3.1 Predicted transcription factor binding sites (TFBSs).
   3.2 Priors for all weight matrices.
   3.3 Refined weight matrices.
   3.4 Enhancer prediction results.
4. Creating UFE model files.
5. Contact and credits.

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


0. Compiling

Under Linux, you can use the provided Makefile to compile MotEvo and the 
UFE model. Simply type 'make'.

If you prefer to do it manually, you first have to create the UFE model by
g++ -O3 -c evomodel.cpp  
g++ -O3 -c runUFE.cpp
g++ -O3 runUFE.o evomodel.o -o runUFE

To compile MotEvo, simply type:
g++ -O3 motevo.c -o motevo

Note, this should also work under MACOS. Second note: we use the GNU
C++ compiler g++. You can of course use another C++ compiler. Under
Windows you will need to know how to compile C++ source code
yourself. 

This compiling will create two executable binaries called 'motevo' and 'runUFE'. 
The code uses only standard libraries so in principle this should compile 
without problems under most operating systems. If you experience problems
compiling, please contact us (see contact information below).

1. INPUT FILES AND THEIR FORMAT

MotEvo needs three input files to run: 
       - A file that contains (multiply-aligned) sequences.
       - A parameter file.
       - A file that contains a set of positional weight matrices
       (note: this file can in principle be an empty file).

1.1 Multiple sequence alignments.

One input file contains the sequences that MotEvo runs on. In general
this file contains multiple sequence alignments in the following
FASTA-like format. 

Example of the multiple alignment format:
>>hg18_chr7_1241423_1241946_+
CCAAGTAC---GGCTCCCC-AGGCC-CTTCCGG-ACG-AGAAGC-ACCCATA-T---------TT-CCCTTCA
>rheMac2_chr6_73853854_73854374_-
CGCAGATCACTGTATTAACCTCTCC-GCTCTG------------------------TCAACCG-T-GGCTTTA
>mm9_chr5_139799730_139800231_+
CCAAGTTC---GGCTTCAC-AGGCC-CTTCTTG-AGGAAGAAGT-AACCAGA-T---------TT-CCCTTCA

>>hg18_chr7_100263950_100264352_+
AGC--TGG-------G-CATGGTGGCG-CG----------CGCC-TGTAATCCTAGCT-----ACTCG-GGAG
>rheMac2_chr3_48125114_48125516_+
------------------------------CTGGGCACG-GT--------GGCGCGCGCCTGTAATCCT--AG
>mm9_chr5_137579005_137579392_-
GGGTATGGAGAGCTTCCCAACAGACCTCCG----TTCCTCAAGC-AATAATCCCAGCTTGTCTATCTCCGGAG
>canFam2_chr6_11928830_11929223_-
--------------------CGGGCCT-CC----------AGGC-AGCGACCCTAGCTTAGATGATCCTGGAG

In this example there are two multiply aligned regions with
orthologous sequences. Like in the FASTA format, each sequence has two
lines associated with it. One line, which starts with either ">" or
">>" has the name of the sequence, and the line immediately following
this has the sequence and gap pattern. The start of each new multiple
alignment of orthologous sequences is indicated by giving the first
sequence a ">>" mark instead of ">". 
IMPORTANT: The evolutionary model employed by MotEvo explicitly uses
the phylogenetic tree relating the species and MotEvo thus needs to
know which sequence in each multiple alignment came from which
species in the phylogenetic tree. Therefore, on the name line of each
species there should be one and only one occurrence of a 'species
name' that also appears in the phylogenetic tree (which is specified
in the parameter file). In the two multiple alignments above these
species names are, hg18, rheMac2, and mm9, indicating that the
sequences come from the human genome (version 18), the rhesus macaque
genome (version 2), and the mouse genome (version 9). The other parts
of the name indicate the regions in the genome from which the
sequences stem but these can be anything the user chooses.
Note: For clarity we put an empty line between the two
alignments. This is not necessary for input files used by MotEvo.
Note: Each multiple alignment MUST have a sequence from the reference
species. In general it is most intuitive to start each multiple
alignment with the reference species (in this case hg18).


For input files with sequences from a single species the format is as follows:

>>hg18_chr7_1241423_1241946_+
CCAAGTACGCTCCCCAGGCCCTTCCGGACGAGAAGCACCCATATTTCCCTTCA

>>hg18_chr7_100263950_100264352_+
AGCTGGGCATGGTGGCGCGGCCTGTAATCCTAGCTACTCGGGAG


Note: There are no gaps when using sequences from a single species and
each sequence starts with ">>". 
Note: MotEvo in principle allows for non-standard base letters such as N
(any base), S (C or G), W (A or T), and so on.

1.1.1 How do I obtain multiple alignments? 

We assume you are already in possession of the sequences from the
reference species on which you want to run MotEvo. What to do if your
sequences are from an unknown genome (like meta-genomic sequences)
falls outside the scope of this manual (finding orthologous sequences
is highly nontrivial in that case).

Obtaining orthologous sequences:
To obtain the orthologous regions of your sequences in other genomes
we suggest you use the pair-wise genome alignments that are provided
by a number of resources on the web. These include:
UCSC database (http://hgdownload.cse.ucsc.edu/downloads.html).
ENSEMBL database (http://www.ensembl.org/info/docs/compara/analyses.html).

These provide pairwise alignments for most available multi-cellular
organisms.

Obtaining alignments:
Once you have obtained orthologous regions for all your sequences you
need to multiply align them. To this end we typically use the T-Coffee
software that can be obtained from: http://tcoffee.crg.cat/.
We typically use the following simple command line:
t_coffee infile -type=dna -outfile=outfile -output fasta_aln

where infile has the set of orthologous input sequences. The
additional options we use to indicate the sequences are DNA sequences
(as opposed to amino acid sequences) and that we want to output in
FASTA-alignment format. The output multiple-alignments can then
directly be used with MotEvo. The only thing that needs to be done is
to add a second '>' to the identifiers of the reference species for
each multiple alignment.

1.2 Phylogenetic species tree

The phylogenetic tree of all species that occur in at least one of the
multiple alignments should be given in the parameter file. The format
of the phylogenetic tree is the standard Newick format
(http://en.wikipedia.org/wiki/Newick_format). The distances on the
branches of the tree are defined as the number of expected
substitutions per neutrally evolving site.

For example, here is reasonable tree for a set of mammalian genomes:
((((hg18:0.048,rheMac2:0.048):0.143,mm9:0.489):0.030,((canFam2:0.224,equCab1:0.149):0.011,bosTau3:0.246):0.047):0.365,monDom4:0.481);

1.2.1 Where do I obtain a tree for my species?

For sets of mammalian genomes, Drosophila genomes, Saccharomyces
genomes, and a number of bacterial clades, reasonable phylogenetic
trees are provided in the directory 'trees'.

These species trees were generally obtained using the procedures described in:
Molina and van Nimwegen, Genome Res. 2008 Jan;18(1):148-60. Epub 2007
Nov 21. PMID:18032729 

MotEvo's results are not highly sensitive to the tree. If you need a
tree for another set of species, and you would not know how to obtain
one yourself, then we suggest to look at some web-sites and data-bases
dedicated to the genomes of these species. These often have trees
available. You might also want to play with rescaling the length of
the branches. Keep in mind that distances are often given in terms of
number of substitutions at the amino acid level as opposed to number
of substitutions at neutrally evolving sites (which is what MotEvo
uses). 

1.3 Positional weight matrices

The position-specific weight matrices that MotEvo uses as input (and
produces as output when doing WM refinement) are provided in a
TRANSFAC-like format. Below is an example of a single WM:
//
NA name_of_wm
PW 7
PO      A       C       G       T
1       0       0       0       10
2       0       0       0       10
3       0       0       0       10
4       0       4       6       0
5       0       2       8       0
//

This WM has length 5. The counts on each line indicate the number of
known sites for the motif that have an A, C, G, and T at this position
respectively. MotEvo will use the counts, automatically add
pseudo-counts, and normalize, to obtain probabilities (that is you do
NOT need to add pseudo-counts yourself). In this example 4 of the 10
known sites have a C at position 4, and the other 6 have a G at this
position.

Note: The input WM file can have an arbitrary number of WMs in it.
Note: To control the prior weight assigned to each WM you can specify
a prior weight by adding a line:
PW weight
For example, in the above example a weight of 7 is assigned to this
WM. These weights are transformed into prior probabilities as
follows. The settings of the parameter file determine the total prior
probability that is assigned to ALL WMs combined in the input WM
file. By default this prior probability is divided equally among all
WMs. However, when PW values are specified for each WM the prior
probability is distributed across the WMs in proportion to the PW
value. In this way the user can control the precise prior
probabilities of all WMs.

2. RUNNING MODES

Given input files with multiple sequence alignments and a set of
weight matrices (WMs) representing the binding specificities of a set
of TFs, MotEvo can essentially be run in 3 different modes:

     1. Predicting transcription factor binding sites (TFBSs). Here
     MotEvo predicts where in the input sequences TFBSs occur for each
     of the input WMs.

     2. WM refinement. Here MotEvo uses the input sequences (which are
     assumed to contain TFBSs for the input WMs) to improve the input
     WMs into versions that better explain the observed data.

     3. Enhancer prediction. Here MotEvo predicts where in the input
     sequences significant clusters of TFBSs for the input WMs occur
     (these may correspond to cis-regulatory modules/enhancers).


2.0 GENERAL PARAMETERS 

There are a number of parameters that are used independently of the
running mode used. We describe these in turn. The general form of the
parameter file is that each line starts with the name of a parameter,
followed by a space, followed by the value of the
parameter. Parameters that have to be specified for every MotEvo run
are indicated with the word 'REQUIRED'.
     
     -refspecies (REQUIRED): The name of the species that is
     considered the reference. That is, MotEvo will make predictions
     for TFBSs/WMs/enhancers with respect to this reference
     species. The name should match exactly one of the species names
     in the tree.

     -TREE (REQUIRED): The phylogenetic tree (in Newick format)
     relating the species from which the sequences in the input
     alignments stem. All species occurring in any of the input
     multiple alignments should occur in this tree. The branch lengths
     correspond to the expected number of substitutions per neutrally
     evolving site.

     -restrictparses: zero or one. When set to one, MotEvo will not
      consider binding site configurations where a site occurs at a
      position that has higher score for the background than for the
      WM. By default this variable is set to zero and one would rarely
      need to use this variable (it is essentially only used for
      certain testing purposes).


     BACKGROUND RELATED PARAMETERS
 
     -markovorderBG (REQUIRED): The order of the background model,
     i.e. the number of upstream nucleotides that the current
     nucleotide depends on in the background model. In the simplest
     case this parameter is set to '0' (zeroth order) and the
     probabilities for an A, C, G, or T to occur are independent of
     the preceding nucleotides.

     -bg A (and bg C, bg G, and bg T): These are used when the simple
     bg model with order zero is used. They give the probabilities for
     A, C, G, and T to occur. If these parameters are not provided
     MotEvo will estimate the background probabilities from the input
     sequences (assuming all input is background).

     -mybgfile: When this parameter is specified MotEvo obtains the
     background model parameters from the file as opposed to
     estimating them from the input sequences. The file should consist
     of lines of the form:
     X1X2...XkY value
     where X1, X2, X3, ..., Xk, and Y are all nucleotides and 'value'
     is a probability. This value corresponds to the probability of
     observing nucleotide Y given that the previous k nucleotides are
     X1 through Xk, i.e. P(Y|X1,X2,..,Xk). The nucleotides and the
     values should be separated by a tab. Example
     ATCG 0.1
     means that there is probability 0.1 for a G to occur given that
     the previous nucleotides are ATC. 
     

     PRIOR RELATED PARAMETERS 

     -bgprior: The a priori probability for background columns. MotEvo
     assigns prior probabilities to binding site configurations which
     are proportional to the product of, for each WM type, the prior
     of the WM to the power of the number of sites in the
     configuration. This parameter gives the prior for the
     background. It controls the a priori density of TFBSs that is
     expected in the following way. When the bgprior is set to p,
     MotEvo will divide the remaining probability (1-p) among the UFE
     and all input WMs.

     -UFEwmprior: The prior for the UFE model relative to the other
     (non-bg) WMs. Given a bgprior of p, the remaining probability
     (1-p) is divided among the other WMs and the UFE. This parameter
     controls how much weight the UFE gets relative to the other
     WMs. That is, if this parameter is set to 100 it implies that a
     site for the UFE is a priori 100 times as likely as a site for a
     given known WM. To give an example, for human there are about
     2000 TFs and we may estimate anywhere from 400-1000 individual
     motifs. Given roughly 200 known motifs, this implies there are
     800 unknown motifs. This would imply the UFE has to be roughly
     set to 200-800. Note: To turn off the UFE model altogether,
     simply set this parameter to zero. Note: The weights of the
     individual WMs can be controlled through the input WM file. By
     adding, for each WM, a field 'PW' that specifies the prior weight
     of the WM.

     -EMprior: either 0 (off) or 1 (on). When set to 1 MotEvo will use
     expectation maximization to estimate all prior probabilities
     (including those for the UFE and background model) that maximize
     the probability of the input data. Note: In all models except for
     enhancer finding a single set of priors for the entire input data
     is estimated. In the enhancer finding mode a set of priors is
     estimated separately for each window.  
     -priordiff: A convergence criterium for fitting the priors. When this 
     quantitity is set to 0.01 it means that priors are considered 
     converged when they change by less than 1% from one iteration to 
     the next. 
     Note: During iteration, those priors that become so small that they
     correspond to less than 1 expected site in the entire input are
     set to be stricly zero.

     priordiff: Typically a small real number (e.g. 0.01). This
     parameter sets the cut-off for the expectation-maximization
     iteration of the priors. That is, when this parameter is set to
     0.01 the prior updating is considered converged when the priors
     change by less than 1% from one iteration to the next.


     UFE RELATED PARAMETERS

     UFEwmfile: A file containing the pre-calculated values for the
     probability of each possible alignment column under the UFE
     model. IMPORTANT: These probabilities depend on both the
     phylogenetic tree and the background probabilities b_A, b_C, b_G,
     and b_T. It is important that this UFE file was generated using
     the exact same phylogenetic tree and background probabilities as
     used in the current run. Note: The UFEwmfile can be generated
     using the provided executable runUFE. The user needs to specify a
     tree in Newick format as well as the base frequencies for A, C,
     G, and T (see 4. for a description).

     UFEwmlen: The length of UFE sites. This is typically set to a
     value corresponding to the typical length of a TFBS, e.g. 10 for
     eukaryotes. Note: When running with a single WM one typically
     sets the UFE length to the same as the WM length.

     UFEprint: zero or one. When set to one the UFE sites are printed
     in the site file (default). There are typically many UFE sites
     and when the user is only interested in sites for the given WMs,
     setting this value zero avoids printing the UFE sites in the site
     file. 

     UFEwmproffile: Name of an output file reporting UFE site
     probabilities at each position. When this parameter is provided
     MotEvo will create a fil that reports, for every position in the
     input sequences, the probability that an UFE site occurs at this
     position. Note: When using this parameter together with an empty
     input WM file, MotEvo produces profiles across the sequences that
     quantify the conservation evidence that conserved regulatory
     sites appear at each position (i.e. similar to phastcons
     profiles). 


2.1 TFBS: Predicting transcription factor binding sites 

To run MotEvo to predict TFBSs on the input data, the parameter 'Mode'
in the parameter file needs to be set to TFBS, i.e.  
Mode: TFBS 

      2.1.1 Parameters for TFBS prediction
      When predicting TFBSs the only additional parameters that needs
      to be specified beyond the general parameters are related to the
      output that MotEvo produces.
      
      OUTPUT RELATED PARAMETERS

      sitefile: Name of the output file containing the predicted
      binding sites. NOTE: when this parameter is not specified,
      MotEvo will simply not report invidual predicted TFBSs.
      minposterior: Minimal posterior probability for sites to be
      reported, i.e. predicted TFBSs with posteriors less than this
      cut-off will not be reported in the output file, e.g. 0.01 when
      run in a very sensitive setting, and 0.2 when run in a more
      specific setting.

      priorfile: Name of the output file containing the final priors
      and site densities. Note: When EMprior=0 this will give the
      observed posterior densities of sites for the various WMs using
      the fixed input priors. When EMprior=1 this file will contain
      the final estimated priors and site densities.

      minposterior: A real number between zero and one. The minimal
      posterior probability for TFBSs to be reported, i.e. sites with
      posterior less than this cut-off are not reported in the sites
      file.

      printsiteals: zero or one. When set to zero MotEvo will not
      print out the sequence alignments of each site, but will only
      report position, WM, and posterior of each site.


2.2 WMREF: Weight matrix refinement
To run MotEvo to optimize the WMs using the input sequences, the
parameter 'Mode' in the parameter file needs to be set to WMREF, i.e
Mode: WMREF

Note: The refined WMs will be reported in the file wms.updated.

Note: When the sitefile and/or priorfile are defined then MotEvo will
also report the final predicted TFBSs and site densities with the
refined WMs.

	2.2.1 Parameters for WM refinement.

	minposteriorWM: Minimal posterior of the TFBSs for inclusion
	in WM refinement. That is, when constructing a new WM from the
	predicted TFBSs only sites with a posterior over this cut-off
	are used. This can help to ensure that the updated WM is not
	`soiled' by the large number of putative sites with low
	posterior probability, e.g. setting this parameter to 0.2 or
	0.5 when only high scoring sites are desired.

	wmdiff: A convergence criterium for fitting the WMs. When this
        quantitity is set ot 0.01 it means that WMs are considered
        converged when their entries change by less than 1% from one
        iteration to the next. 
	

2.3 ENH: Predicting enhancers.

To run MotEvo to predict the location of enhancers in the input
sequences, the parameter 'Mode' in the parameter file needs to be set
to ENH, i.e.  
Mode: ENH

      2.3.1 Parameters for predicting enhancers

      winlen: Length of the window that is slid across the sequences of
      the reference species. This length should roughly correspond to
      the expected length of enhancers (cis-regulatory modules),
      e.g. 500.

      steplen: The number of base pairs by which the window is moved
      at each step. When this parameter is set to 1 a putative
      enhancer window will be considered at every base pair in the
      input sequences. However, the running time can be considerably
      reduced without much loss of accuracy by setting this parameter
      to a higher value, e.g. 100. 

      OUTPUT RELATED PARAMETERS

      CRMfile: Name of the output file containing the results of the
      enhancer predictions. 


3. Output files
Here we discuss the formats of the output files.

   3.1 Predicted transcription factor binding sites (TFBSs).

   Each predicted site starts with a line that gives the location,
   posterior, and WM binding the site. The next lines then explicitly
   show the sequence alignment at the location of the site, including
   WM-scores for all species that have an orthologous copy of the
   site. Here is an example:
   8-43 + 0.261798 ZEB1 hg18_chr7_100302640_100303065_+
   CACGTG 4.18809 hg18_chr7_100302640_100303065_+
   CACGTG 4.18809 rheMac2_chr3_48157384_48157809_+
   CACGTG 4.18809 mm9_chr5_137543847_137544273_-
   CACGTG 4.18809 canFam2_chr6_11902557_11902937_-
   CACCTT 6.72233 monDom4_chr2_278815013_278815460_+
   
   The first line shows that this site occurs from position 8-43, on
   the positive strand, in the reference species of the alignment with
   name hg18_chr7_100302640_100303065_+. This is a site for the WM
   with name ZEB1, and its posterior probability is 0.261798.
   That is, this line has the general format:
   start end strand posterior WM alignment

   The next lines show the local alignment. In this case, besides the
   reference species (hg18 = human) orthologous sites occur in rheMac2
   (rhesus macaque), mm9 (mouse), canFam2 (dog), and monDom4
   (opossum). The second column on each line shows the WM-score for
   the corresponding sequence. Except for opossum, which has a WM
   score of 6.72233, all others have a WM score of 4.18809. The
   general format of these lines is thus:
   sequence WM-score sequence_name
   Note:The WM score is the log-likelihood ratio of the probabilities
   of the sequence under WM and background model.

   3.2 Priors for all weight matrices.
   This file shows, for each WM (including UFE and background model),
   the prior, and overall density of sites for this WM.
   Example:
   ZEB1			0.000175027     2.06264         0.000866717
   background           0.9764          11506.6         0.80584
   UFEwm                0.0233931       275.681         0.193067
   
   The format of each line is:
   WM-name    prior   number_of_sites	density
   Thus, for ZEB1 the prior is 0.000175027. The total expected number
   of sites for ZEB1 in all input sequences is 2.06264, and the
   fraction of all sequence covered by ZEB1 sites is 0.000866717.
   Note: The prior and density are not the same. For example, the
   prior for the background in this case is 0.9764 whereas bg
   nucleotides cover only 0.80584 of the sequences. This is because the
   other WMs are typically much wider than a single nucleotide so that
   each occurrence of a site for another WM covers multiple letters in
   the sequences.

   3.3 Refined weight matrices.
   After WM refinement MotEvo creates a file with the refined WMs for
   each of the input WMs. Example:
   //
   NA ctcf.drosophila
   P0 A		C	G	T	cons	inf
   01 24.587	121.703	38.049	147.730	Y	0.277
   02 64.221	62.073	149.789	55.985	N	0.102
   03 77.192	23.695	183.638	47.543	G	0.329	
   04 21.189	279.442	17.580	13.858	C	1.086
   05 0.675	329.627	1.766	0.000	C	1.893
   06 285.398	3.943	27.511	15.216	A	1.199
   07 4.763	214.712	107.671	4.923	C	0.853
   08 29.167	213.399	12.362	77.141	C	0.580
   09 306.994	7.203	11.862	6.009	A	1.463
   10 1.999	0.000	330.069	0.000	G	1.908
   11 128.695	0.000	203.373	0.000	G	0.998
   12 11.642	0.503	190.135	129.787	G	0.790
   13 0.000	0.000	331.408	0.660	G	1.940
   14 5.307	2.977	307.830	15.954	G	1.496
   15 12.319	296.571	4.303	18.875	C	1.326
   16 176.574	2.438	149.151	3.904	A	0.833
   17 15.803	168.907	136.747	10.611	S	0.573
   18 31.752	144.785	33.030	122.502	Y	0.256
   //
   Note: The counts on each line correspond to the sum over the
   posterior of all predicted sites on the input that have the
   corresponding letter at the corresponding position of the
   sites, e.g. at position 6 295.711 sites have an A, 3.94 have a C,
   26.405 have a G, and 18.641 have a T. The last two columns give the
   consensus sequence and the information content (in bits) at each
   position. 
   Note: this format is of course exactly the same as the input
   WM format, so that output files of refinement can be directly used
   as input for another run.

   3.4 Enhancer prediction results.
   The results file of enhancer prediction provides, for every window,
   an enhancer score. It also provides the estimated prior
   probabilities for all the input WMs, the UFE, and the background
   for each window. Here is an example line from an output file:
   dm3target_9990_10612_20602  9750    10199	 37.833974 0.000786 0.014661	0.019347	0.055090	0.000126 0.000000 0.0119 85 0.741604	0.156402
   
   The first field indicates the name of the alignment that this
   window occurs in. The second field is the starting position of the
   window in the alignment, and the third field the end position. The
   fourth field is the enhancer score. The enhancer score is defined
   as log[P(W|bg,ufe,wms)/P(W|bg,ufe)], i.e the logarithm of the ratios of
   probability of the multiple alignment of the window W using a
   combination of bg, UFE model, and the input weight matrices,
   and the probability when only the UFE model and background are
   used. The enhancer score is followed by the estimated prior
   probabilities of all the WMs, the UFE, and the background, for the
   corresponding window. The order of the WMs is given in the first
   line of the file (which is a header line). 

  
4. Creating UFE model files.

To use the UFE model MotEvo needs an input file that calculates, for
each possible alignment column, the probability under the UFE and
background model. Importantly, these probabilities should be created
using the same phylogenetic tree and background probabilities as for
the MotEvo run. UFE model files can be created using the runUFE code
that we provide in the source directory. 

Example command:
runUFE tree.mammals 0.25 0.25 0.25 0.25 > UFEmodel.mammals

Here the tree.mammals is a phylogenetic tree in Newick format (as can
be found in the 'trees' directory) and the probabilities 0.25 0.25
0.25 0.25 are the base frequencies in the order A C G T.
Note: The running time of runUFE increases exponentially with the
number of species run, i.e running with more than 10 species may
take a long time to run.


5. Contact and credits

MotEvo was developed by Phil Arnold, Ionas Erb, Nacho Molina, and Erik
van Nimwegen. Please report any problems, bugs, comments, or requests
for additional functionality to:
erik.vannimwegen@unibas.ch
Physical address:
Core program Computational and Systems Biology
Biozentrum, University of Basel
Klingelbergstrasse 50-70
4056 Basel, Switzerland



   
