miRmap 2.0.0

Comprehensive prediction of microRNA target repression strength

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

1. 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.

2. 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).

Copyright © 2011-2024 Charles E. Vejnar