~vejnar/miRmap

#miRmap - Comprehensive prediction of microRNA target repression strength

The miRmap library is a Python library predicting the repression strength of microRNA (miRNA) targets. The model combines:

thermodynamic features: ΔG duplex, ΔG binding, ΔG seed duplex, ΔG seed binding, ΔG open and ΔG total,
evolutionary features: BLS and PhyloP,
probabilistic features: P.over binomial and P.over exact, and
sequence-based features: AU content, UTR position and 3' pairing.

NOTE This is a reimplementation by the same author of the miRmap library published in 2011 with most of the core algorithm unchanged. Please refer to the miRmap1 repository for the old library.

#Online

miRmap is available online.

#Download

See refs page.

#Citation

If you use miRmap for your research, please cite:

Charles E. Vejnar and Evgeny M. Zdobnov
miRmap: Comprehensive prediction of microRNA target repression strength
Nucleic Acids Research 2012 Dec 1;40(22):11673-83. doi: 10.1093/nar/gks901

#Installation

#External dependencies

The Spatt library is necessary for the P.over exact feature.

Download the latest Spatt tarball (Version 2.1 was successfully tested), then do:
```
cd spatt-<version>
mkdir build
cd build
cmake -DWITH_SHARED_LIB=ON ..
make
```
To create the library at libspatt2/libspatt2.so.
PHAST is necessary for the evolutionary features. Compilation instructions of PHAST are available in this PKGBUILD.

#Using `pip`

After installing external dependencies, install miRmap:

pip3 install mirmap

Python dependencies ViennaRNA and dendropy will be installed from PyPI.

#Example

import mirmap.target

utr_seq = "ATAGACTGTACATTATGAAGAATACCCAGGAAGACTTTGTGACTGTCACTTGCTGCTTTTTCTGCGCTTCAGTAACAAGT"
mirna_seq = "UAGCAGCACGUAAAUAUUGGCG".replace("U", "T")

targets = mirmap.target.find_targets_with_seed(utr_seq, mirna_seq)
print(targets[0].report())

This will return:

          36                   57
          |                    |
CAGGAAGACTTTGTGACTGTCACTTGCTGCTTTTTCTGCGCT
                        |||||||
          GCGGTTATAAATGCACGACGAT

Then we can calculate the scores of the miRNA target:

import mirmap.if_lib_spatt
import mirmap.scores

if_spatt = mirmap.if_lib_spatt.Spatt("bin/linux_x86_64/libspatt2.so")

scores = mirmap.scores.calc_scores(
    targets[0],
    if_spatt=if_spatt,
)
print(mirmap.scores.report_scores(scores))

This will return:

 ΔG duplex (kcal/mol)     -13.8
 ΔG binding (kcal/mol)    -11.95
 ΔG open (kcal/mol)       14.03
 ΔG total (kcal/mol)      0.2345
 AU content               0.6574
 UTR position             22.0
 3' pairing               1.0
 TargetScan score         23.66
 Probability (Exact)      0.03813
 Probability (Binomial)   0.006405
 Conservation (BLS)       0.0
 Conservation (PhyloP)    1.0
 miRmap score             -0.3122

#License

The miRmap library is distributed under the GNU GPL v3 (see /LICENSE).

refs

clone