RNAz
- Detecting stable and conserved RNA secondary structures in
multiple sequence alignments.
RNAz [options] [file]
Scores the forward direction, the reverse complement or both. By default only the forward direction as given in the input alignment is scored.
Stores the output in a file. By default the output is printed to the standard output.
Only show results with RNA class probability P > X. (Default: 0.5)
Print the output including gaps. Useful if the alignment wants to be recovered from the RNAz output. (Default: off)
Background model used to calculate z-scores. Default is dinucleotide content. Setting this option to mononucleotide will use the same models used RNAz 1.0 and prior versions.
If a z-score cannot be calculated efficiently because sequence characteristics are out of range (i.e. base composition too biased or sequence too short), RNAz will use a slow empirical shuffling procedure to determine the z-score. This can slow down screens considerably and can be turned off with this option.
Assumes input alignments to be structurally aligned using LocaRNA (experimental feature).
Prints version information and exits.
Prints a brief help message and exits.
RNAz
detects stable and conserved RNA secondary structures in multiple
sequence alignments. It 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. noncoding 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 class-probability which can be used as
convenient overall-score.
RNAz
reads one or more alignments in CLUSTAL W or MAF format from a file
or standard input and prints the results to the standard output.
Please refer to the files README and manual.pdf for full documentation.
Stefan Washietl <wash@mit.edu>
Andreas Gruber <agruber@tbi.univie.ac.at>
Ivo Hofacker <ivo@tbi.univie.ac.at>
Kristin Missal <missal@izbi.uni-leipzig.de>