SISSIz - Dinucleotide controlled random alignments and RNA gene prediction
==========================================================================
1. DESCRIPTION
==============
SISSIz randomizes multiple sequence alignments preserving the average
dinucleotide content. It uses a simulation procedure guided by a
phylogenetic tree.
In combination with the RNAalifold algorithm it can be used to detect
functional RNA structures in multiple alignments. To this end, SISSIz
calculates the consensus folding energy of the original data and
compares it to the expected energy distribution of the random
alignments. The significance of the prediction is measures as a
z-score:
(Native energy) - (Mean random energy)
z = --------------------------------------
Standard dev. random energy
Negative z-scores indicate secondary structures that are more
conserved/stable than expected by chance.
1. INSTALLATION
===============
This packages uses the GNU configuration and installation
system and thus compilation and installation should be easy:
cd /somedir/SISSIz-0.1
./configure
make
make install (as root)
This installs the SISSIz binary into /usr/local/bin and additional
data files and examples in /usr/local/share/SISSIz.
If you have no root rights on your system or prefer to install SISSIz
into a self-contained directory run configure for example like this:
./configure --prefix=/opt/programs/SISSIz --datadir=/opt/programs/SISSIz/share
2. USAGE
========
2.1 Input alignment
-------------------
The input alignment must be in CLUSTAL W format or MAF format.
2.2 Program call
----------------
SISSIz [OPTIONS] [FILE]
2.3 Options
-----------
Most options have a long (--long) and a short form (-x).
-s, --simulate
This switchtes to "simulation only" mode, i.e no RNA analysis is
carried out and the alignments is just randomized.
-n, --num-samples
Number of samples that are used for calculating the z-score. In
simulation mode, the number of random alignments to be
produced. Default is 100 for z-scores, and 1 for simulations.
-i --mono
-d --di
Choose between a mononucleotide and dinucleotide background
model. Default is the dinucleotide model.
-v, --verbose
Produce verbose output that gives an overview of the algorithm,
intermediate results and statistics.
-t, --tstv
Use a model that has different rates for transitions and transversions.
-k, --kappa
Set the transition/transversion rate ration parameter for the
model. If not set, it is estimated from the data. If --tstv not set
this option can be ignored.
-p, --precision
Limit the deviation of the mononucleotide content in the simulated
alignments. The value given here is the maximum Euclidian distance
between the mononucleotide frequencies in the original alignment and
the simulated alignment. In other word, it is the square-root of the
sum of the squared differences between the four frequencies of As, Cs,
Ts, and Gs. For example if you set to 0.01 you will get almost exactly
the same monunucleotide content in the simulations as in the original
alignment. This makes it slower but more accurate.
-m, --num-regression
Pairwise alignments of 25 different distances are simulated to
estimate the relationship between observed and genetic distances. For
each of these points the average of X samples is used, where X is the
number given here. Default is 10.
-f, --flanks
In most cases you can ignore this option. In the algorithm additional
sites are used as "buffer sites" to overcome problems with saturation
of mutations that make it impossible to estimate distances. Default is
1.5 x (number of sites in original alignment). If your simulation
fails because of a negative logorithm you can raise this value.
--dna --rna
Use T or U in the output alignments. Default is --dna, i.e Ts are
printed.
--clustal --maf
Output alignment format, either CLUSTAL W format (default) or MAF
format. Only relevant in simulation mode (--simulate).
-h --help
Print short overview of options.
-V --version
Print version information.
2.4 Output
----------
In simulation mode one or more alignmnts in CLUSTAL W or MAF format
are printed.
If --verbose is given lots of additional information is given (these
lines start with a # so that they can be filtered easily).
When calculating z-scores the results are given in one tab separated
line:
1. Model name (either "sissiz-mono" or "sissiz-di")
2. Name of the input file name.
3. Number of sequences in the input alignment
4. Mean pairwise identity (MPI) of the input alignment
5. Average MPI of the sampled alignments.
6. Standard deviation of the MPIs of the sampled alignments.
7. RNAalifold consensus Minimum free energy (MFE) of the original alignment.
8. Average consensus MFE in the sampled alignments
9. Standard deviation of the consensus MFE in the sampled algnments
10. z-score calculated from 7. 8. and 9.
3. EXAMPLES
===========
SISSIz --simulate --tstv -n 1000 test.aln
Simulate 1000 random alignments with the same average dinucleotide
content and a transition/transversion model.
SISSIz --simulate --mono --tstv -n 1000 test.aln
The same as above with mononucleotide model.
SISSIz --verbose --simulate --mono --tstv -n 1000 test.aln
The same as above, but show detailed results.
SISSIz --verbose --tstv --precision 0.05 -n 1000 test.aln
Calculate z-score with a sample size of 1000. Slowest but most
accurate settings.
4. LITERATURE
=============
SISSIz and the new dinucleotide randomization procedure is described in:
Gesell T, Washietl S
Dinucleotide controlled null models for comparative RNA gene
prediction.
BMC Bioinformatics. 2008; 9:248
The basic idea of calculating stability z-score for multiple
alignments is described here:
Washietl S and Hofacker IL
Consensus folding of aligned sequences as a new measure for the
detection of functional RNAs by comparative genomics.
J Mol Biol (2004) 342:19-30.
The theoretical foundations for the simulation Model is described
here:
Gesell T and von Haeseler A
In silico sequence evolution with site-specific interactions along
phylogenetic trees.
Bioinformatics (2006) 22:716-22.
The RNAalifold algorithm is described here:
Hofacker IL, Fekete M, Stadler PF
Secondary structure prediction for aligned RNA sequences.
J Mol Biol (2002) 319:1059-66.
5. CONTACT
==========
Stefan Washietl <wash@tbi.univie.ac.at>
Tanja Gesell <tanja.gesell@univie.ac.at>