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资源说明：RNAz 2.0 extension: A program that classify unknown DNA sequences as either RNA or others using SVM with features that capture thermodynamics, structural conservation, co-transcriptional folding, structural influences on RNA gene selection and higher moments of Boltzmann distribution.
RNAz 2.0 Extended
============
Content
=======
0. Terms of use
1. Introduction
2. Theory
2.1. The structure conservation index
2.2. Thermodynamic stability
2.3. Classification based on both scores
3. Usage
3.1. Installation
3.2. Enviroment variable
3.3. Invocation
3.4. Output
4. Citing RNAz
5. Important notes
6. Contact
0. Terms of use
===============
Please read the file COPYING for licence terms of RNAz.
1. Introduction
===============
RNAz 2.0 Extension is a branch of RNAz 2.0 that adds a new model to
existing RNAz 2.0. The new model captures co-transcriptional folding,
structural influences of RNA gene selection and higher moments of
Boltzmann distribution factors. The model is trained by different
training data set than other models as the original models were no
longer available from the authors.
RNAz detects stable and conserved RNA secondary structures in multiple
sequence alignments. RNAz calculates two independent scores for structural
conservation (the structure conservation index SCI) and for thermodynamical
stability (the z-score). High structural conservation (high SCI) and
thermodynamical stability (negative z-scores) are typical features of
functional RNAs (e.g. non-coding RNAs or cis-acting regulatory
elements). RNAz uses both scores to classify a given alignment as
functional RNA or not. It uses a support vector machine classification
procedure which estimates a RNA-class probability which can be used as
convenient overall-score.
For a detailed coverage of all aspects of RNAz we recommend to read the
manual/tutorial (manual.pdf).
2. Theory
=========
2.1. The structure conservation index
=====================================
RNAz uses programs from the Vienna RNA package to perform minimum free
energy (MFE) RNA secondary structure predictions.
First, it calculates the average MFEs for all single sequences in the
aligment using RNAfold.
Second the complete alignment is folded using RNAalifold. RNAalifold
implements a consensus folding algorithm which uses essentially the same
algorithms and energy parameters as RNAfold. It calculates a consensus MFE
which is composed of an energy term averaging the energy contributions of
the single sequences and a covariance term rewarding compensatory and
consistent mutations.
If the sequences in the alignment can fold into a common structure, the
average MFE and the consensus MFE will be of similar dimension. If there is
no common structure, the consensus MFE will be higher (i.e. less stable)
than the average MFE of the single sequences.
Based on this intuitive rationale, a structure conservation index is
defined:
consensus MFE
SCI= --------------
average MFE
The SCI will be around 0 if RNAalifold does not find a consensus structure,
it will be around 1 if the structure is perfectly conserved. A SCI above 1
indicates a perfectly conserved secondary structure which is even supported
by compensatory and/or consistent mutations.
2.2. Thermodynamic stability
============================
The significance of a predicted MFE as calculated by RNAfold is difficult
to interpret in absolute terms. It depends on the length and the base
composition of the sequences (longer sequence => lower MFE, higher
GC-content => lower MFE). Typically the significane of a MFE is estimated
by comparing to many random sequences of the same length and base
composition. If mu is the mean and sigma the standard deviation of the MFEs
of many random sequences a convenient normalized measure for the
significance of the native sequence with MFE m is a z-score:
m - mu
z = ---------
sigma
RNAz can effeciently calculate z-scores without sampling. Negative z-scores
indicate that the native sequence is more stable than the random
background. The unit of z-scores are standard deviations. Random MFEs can
be roughly approximated by a standard normal distribution which gives an
impression of associated P values (e.g. z=-2 => P=0.98).
2.3. Classification based on both scores
========================================
Both scores represent independent diagnostic features of functional
RNAs. RNAz combines both into a overall score using a support vector
machine regression. Depending on the SCI and z, but also the number of
sequences in the alignment and the mean pairwise identity a RNA
class-probability is calculated. It should be noted that the level of the
SCI depends on the sequence similarity. 100% conservation for example
results in a SCI of 1 per definition but do not hold any information for
our purpose. Thus, the support vector machine was taught to interpret the
significance of the SCI depending on the sequence variation.
The confidence level of this RNA-class probability or "RNAz P-value"
slightly varies depending on the properties of the input alignment. In our
tests, P=0.5 and P=0.9 had specificities of 96% and 99%.
2.4. Co-transcriptional folding, Structural influences of RNA gene selection and Higher moments of Boltzmann distribution
=======================================================================================
Please read following papers:
"Co-transcriptional folding is encoded within RNA genes"
(http://www.biomedcentral.com/1471-2199/5/10/)
written by Irmtraud M Meyer and Istvan Miklos.
"An Analysis of Structural Influences on Selection in RNA Genes"
(http://mbe.oxfordjournals.org/content/26/1/209.full.pdf)
written by Naila K. Mimouni, Rune B. Lyngso, Sam Griffiths-Jones and Jotun Hein.
"Moments of the Boltzmann distribution for RNA secondary structures"
(http://ramet.elte.hu/~miklosi/MiklosMeyerNagy.pdf)
written by Istvan Miklos, Irmtraud M. Meyer and Borbala Nagy.
3. Usage
========
3.1. Installation
=================
See INSTALL for details.
3.3. Invocation
===============
RNAz [options] [filename]
RNAz takes an alignment file in the ClustalW or MAF format. Available
command line options:
-h, --help Print help and exit
-V, --version Print version and exit
-f, --forward Score forward strand
-r, --reverse Score reverse strand
-b, --both-strands Score both strands
-o, --outfile=FILENAME Output filename
-w, --window=START-STOP Score window from START to STOP
-p, --cutoff=FLOAT Probability cutoff
-g, --show-gaps Display alignment with gap (default=off)
-s, --predict-strand Use strand predictor (default=off)
-t, --rna-features Use rna features model (default=off)
You can test RNAz on one of the example alignments (installed
by default in /usr/local/share/RNAz/examples)
cd /usr/local/share/RNAz/examples
You can test RNAz with old model by:
RNAz tRNA.aln
You can test RNAz with normal model:
RNAz -d tRNA.aln
You can test RNAz with new rna features model by:
RNAz -d -t tRNA.aln
Note: -t option always need -d option along together since RNA features model is trained only for dinucleotide model.
3.4. Output
===========
Please refer to the following commented sample output:
Header:
############################ RNAz 2.0 ##############################
Sequences: 4
Columns: 73
Reading direction: forward
Mean pairwise identity: 80.82
Shannon entropy: 0.31118
G+C content: 0.54795
Mean single sequence MFE: -25.88
Consensus MFE: -25.98
Energy contribution: -23.10
Covariance contribution: -2.88
Combinations/Pair: 1.43
Mean z-score: -1.37
Structure conservation index: 1.00
SVM decision value: 1.47
SVM RNA-class probability: 0.997262
Prediction: RNA
Avg CIS(g) and TRANS(g): -0.13870 0.26359
Avg MFE structure log probability: -0.628086
Avg Diff MFE and Boltzmann Expected Free Energy (per base): 0.025282
Avg Boltzmann Variance (per base): 0.000670
######################################################################
Secondary structure predictions:
The next lines summarize the secondary structure predictions in the
following format:
>sequence name
SEQUENCE
STRUCTURE (MFE)
The structure is indicated in dot bracket notation: '.' denotes a unpaired
base, while '(' and ')' denote base pairs.
The last structure is the RNAalifold consensus structure with the consensus
MFE (broken down in energy and covariance contribution)
>AF041468.1
GGGGGUAUAGCUCAGUUGGUAGAGCGCUGCCUUUGCACGGCAGAUGUCAGGGGUUCGAGUCCCCUUACCUCCA
(((((((..((((........)))).(((((.......))))).....(((((.......)))))))))))). ( -31.60)
>X54300
GGGGGUAUAGCUUAGUUGGUAGAGCGCUGCUUUUGCAAGGCAGAUGUCAGCGGUUCGAAUCCGCUUACCUCCA
(((((((..((((.((.(((((....)))))...)).)))).......(((((.......)))))))))))). ( -27.90)
>L00194
GGGGCCAUAGCUCAGUUGGUAGAGCGCCUGCUUUGCAAGCAGGUGUCGUCGGUUCGAAUCCGUCUGGCUCCA
(((((((..((((........))))(((((((.....)))))))...(.(((.......))).)))))))). ( -32.50)
>AY017179
GGGCCGGUAGCUCAGCCUGGGAGAGCGUCGGCUUUGCAAGCCGAAGGCCCCGGGUUCGAAUCCCGGCCGGUCCA
((((((((...((((((((((...((.((((((.....))))))..)))))))))).))......)))))))). ( -40.40)
>consensus
GGGGCUAUAGCUCAGU_UGGUAGAGCGCCGCCUUUGCAAGGCAGAUGUCAGCGGUUCGAAUCCCCUUACCUCCA
(((((((..((((.........)))).((((((.....)))))).....(((((.......)))))))))))). (-30.98 = -27.72 + -3.25)
6. Citing RNAz
==============
If you use RNAz in your work please cite:
Washietl S, Hofacker IL, Stadler PF
Fast and reliable prediction of noncoding RNAs.
Proc Natl Acad Sci U S A. 102(7):2454-9 (2005)
6. Contact
==========
Please feel free to contact the author for bug-reports, feature-requests,
suggestions etc. Of course I also look forward to hearing from you if you
discoverd something biologically interesting using RNAz.
For RNAz: Stefan Washietl
For RNA Features: Katsuya Noguchi
--
$Id: README,v 1.5 2006/10/12 16:40:24 wash Exp $
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