miranda(l) The miRanda Package miranda(l)
miranda - Finds potential target sites for miRNAs in
miranda file1 file2 [ options ... ]
miranda file1 file2 [-sc score] [-en energy]
[-scale scale] [-loose] [-go X] [-ge Y] [-out fileout]
[-quiet] [-trim T] [-noenergy] [-shuffle] [-s nshuff]
[-w W] [-uniform] [-z zscore]
miRanda is an algorithm for the detection of potential
microRNA target sites in genomic sequences. miRanda reads
RNA sequences (such as microRNAs) from file1 and genomic
DNA/RNA sequences from file2. Both of these files should
be in FASTA format. This is an example of a FASTA format
>embl|AJ550546|DME550546 Drosophila melanogaster microRNA
One or more miRNA sequences from file1 are scanned against
all sequences in file2 and potential target sites are
reported. Potential target sites are identified using a
two-step strategy. First a dynamic programming local
alignment is carried out between the query miRNA sequence
and the reference sequence. This alignment procedure
scores based on sequence complementarity and not on
sequence identity. In other words we look for A:U and G:C
matches instead of A:A, G:G, etc. The G:U wobble bair is
also permitted, but generally scores less than the more
optimal matches. Here is an example alignment:
Query: 3' GTCGAAAGTTTTACTAGAGTG 5' (eg. miRNA)
Ref: 5' TAGTTTTCACAATGATCTCGG 3' (eg. 3'UTR)
The second phase of the algorithm takes high-scoring
alignments (Those above a score threshold, defined by -sc)
detected from phase 1 and estimates the thermodynamic sta
bility of RNA duplexes based on these alignments. This
second phase of the method utilizes folding routines from
the RNAlib library, which is part of the ViennaRNA package
written by Ivo Hofacker. At this stage we generate a con
strained fictional single-stranded RNA composed of the
query sequence, a linker and the reference sequence
(reversed). This structure then folded using RNAlib and
Optionally some rudimentary statistics about each target
site can be generated by performing a number of alignments
using shuffled reference sequences (see -shuffled option).
A distribution is built from these data and statistical
parameters from this distribution are used to produce a Z-
Score for a detected target site.
Displays help, usage information and command-line
--version -v --license
Display version and license information.
Set the score threshold to score. Only alignments
with scores >= score will be used for further anal
Set the energy threshold to energy. Only alignments
with energies <= energy will be used for further
analysis. This value should be negative.
Set the scaling parameter to scale. This scaling is
applied to match / mismatch scores in the critical
10bp region of the 5' end of the microRNA. Many
known examples of miRNA:Target duplexes are highly
complementary in this region. This parameter can be
thought of as a contrast function to more effec
tively detect alignments of this type.
-loose Remove strict alignment heuristics. In normal mode
heuristics are applied to predicted duplexes that
count the number of complementary base-pairs in
different regions of the alignment according to
observations of known miRNA:target duplexes. Align
ments that fail this test are not displayed Turning
this option off is less conservative but may result
in more false-positive detections.
-go X Set the gap-opening penalty to X for alignments.
This value should be negative.
-ge Y Set the gap-extend penalty to Y for alignments.
This value should be negative.
Print results to an output file called fileout.
-quiet Quiet mode, produces the minimum of output.
Trim reference sequences to T nucleotides. Useful
when using noisy predicted 3'UTRs as reference
For each analysis between a microRNA and a refer
ence sequence, also do nshuff alignments between
the microRNA and shuffled reference sequences. This
is used to build a distribution of shuffled align
ment scores that can be used to estimate the relia
bility of a given score. This analysis produces a
Z-Score for each predicted target. It is possible
to filter results using this score with the -z
option. Shuffling statistics are optional and are
not turned on by default. It should also be noted
that this dramatically increase the number of
alignments processed, and will slow the algorithm
Set the total number of random shuffle analyses to
nshuff. Larger numbers of analyses produces a more
-w W For window-based shuffling the window size can be
changed by modifying the W parameter. Window based
shuffling maintains nucleotide composition across
the sequence, shuffling takes place in a small win
dow that moves across the sequence.
Disables windowed shuffling, and shuffles uniformly
across the reference sequence.
Sets a Z-Score threshold for all targets based on
sequence shuffling statistics. Only detected tar
gets with Z-Scores >= zscore will be displayed by
If you use this program for your research then please
include the following citation:
A.J. Enright, B. John, U. Gaul, T. Tuschl, C. Sander, D.S.
MicroRNA targets in Drosophila; Genome Biology 5(1):R1.
I.L. Hofacker, W. Fontana, P.F. Stadler, S. Bonhoeffer, M.
Tacker, P. Schuster (1994) Fast Folding and Comparison of
RNA Secondary Structures. Monatshefte f. Chemie 125:
M. Zuker, P. Stiegler (1981) Optimal computer folding of
large RNA sequences using thermodynamic and auxiliary
information, Nucl Acid Res 9: 133-148
J.S. McCaskill (1990) The equilibrium partition function
and base pair binding probabilities for RNA secondary
Comments and bug-reports should be sent to
Anton Enright 1.0 miranda(l)
Man(1) output converted with