metaRNA finds potential target sites for the microRNAs in genomic sequences. It is built on miRanda, an algorithm for detection and ranking of the targets of microRNA.
from metarna.target_scan import scan, free_energy gene_sequence = ( "ACAAGATGCCATTGTCCCCCGGCCTCCTGCTGCTGCTGCTCTCCGGGGCCACGGCCACCGCTGCCCTGCC" "CCTGGAGGGTGGCCCCACCGGCCGAGACAGCGAGCATATGCAGGAAGCGGCAGGAATAAGGAAAAGCAGC" "CTCCTGACTTTCCTCGCTTGGTGGTTTGAGTGGACCTCCCAGGCCAGTGCCGGGCCCCTCATAGGAGAGG" ) mirna_sequence = "UGGCGAUUUUGGAACUCAAUGGCA" # Get free Energy value: delta_g = free_energy(gene_sequence, mirna_sequence) # Get full targets information: targets = scan(gene_sequence, mirna_sequence) # Specifying Calculation Parameters targets = scan(gene_sequence, mirna_sequence, scale=5.0) # Check the docs for all available options
Latest metaRNA documentation is available on ReadTheDocs.
metaRNA supports Python versions 2.7, 3.3, 3.4, and 3.5. It requires the Vienna RNA package which must be installed before installing metaRNA.
After Intalling Vienna RNA package, metaRNA may be installed simply by executing:
$ pip install metarna
metaRNA is currently tested on Mac OSX and Ubuntu, however other Unix based systems should be supported. It isn’t tested on Windows yet.
virtualenv is assumed and expected.
$ python setup.py develop # Installs Development Version $ python -m unittest
The miRanda algorithm works in two phases. In phase one, the potential target sites are reported based on query microRNA and reference (CDNA) sequence. These targets are scored and the high scoring alignments are then used in second phase, where the folding routines of RNAlib library are utilised to calculate the minimum free energy of the resulting combinations.
Citing in publications
Please cite the original miRanda library, and Vienna RNA library. The citations can be obtained from the links above.