miRmap
Comprehensive prediction of microRNA target repression strength
Search and predict miRNA targets with miRmap
Here
Abstract

MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the vast majority of messenger RNAs (mRNAs). Base-pairing of the so-called miRNA “seed” region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets. To address these challenges, predictors may employ thermodynamic, evolutionary, probabilistic, or sequence-based features. We developed an open source software library, miRmap, which for the first time comprehensively covers all four approaches using eleven predictor features, three of which are novel. This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments. Overall, target site accessibility appears to be the most predictive feature. Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category. We combined all the features into an integrated model that almost doubles the predictive power of TargetScan.

If you have any comments or requests, please email Charles E. Vejnar ().

miRNA target predictions
Web interface

User-friendly and feature-rich miRmap web application.

  • Start the tutorial here.
  • Requires Firefox >= 4 or Internet Explorer >= 8 or Chrome.
REST interface

Programmatic way of accessing miRmap online predictions: all the functionnality of the web interface through a REST interface. See the documentation for more details.

Datasets (flat files)
Release name Species Release date Notes
mirmap201301e Human
Chimpanzee
Mouse
Rat
Cow
Chicken
Zebrafish
Opossum
mirmap201301e/ 2013-01-09 miRBase 19
Ensembl 69
Compara 12
miRmap 1.1
mirmap201202e Human
Mouse
mirmap201202e/ 2012-02-01 miRBase 18
Ensembl 67
Compara 12
miRmap 1.0

Note 1: File name format <release_name>_<species>_<content>.csv.xz

_targetsAll predicted target sites with their genomic and 3'-UTR coordinates
_targets_1to1All miRNA-mRNA predicted regulations
_targets_1to1_ptSame as above, as percentiles over the prediction set
_mirnasmiRNA names and sequences
_transcriptsmRNA transcripts names and sequences

Note 2: If you do not have the xz tool currently installed, please use XZ Utils or 7zip to decompress the files.

Source code

miRmap is a software library written in Python, distributed under the GNU GPL (see documentation).

Stable releases
Release date Notes
miRmap-1.1.tar.gz 2013-01-09
  • Adds ΔG seed duplex and ΔG seed binding features
  • Adds interfaces to Vienna RNA, PHAST, and Spatt executables
  • Updates miRmap models
  • Corrects bugs (mirmap_runs.py...)
miRmap-1.0.tar.gz 2012-02-01
  • First public release
Development

Development of miRmap is public and can be followed at Bitbucket http://dev.vejnar.org/mirmap.

If you develop a new feature or find a bug, please submit it through the Bitbucket interface.

Documentation

The documentation for the miRmap library is available here. Installation instructions, along with usage examples, can also be found in these pages.

The documentation for the miRmap web interface is available here. A tutorial describing how to use miRmap web interface is available.

The documentation for the miRmap REST interface is available here.

Mailing-list

To stay informed of new miRmap releases (less than 5 emails per year and we keep your email safe), please send an email to with "Subscribe miRmap mailing-list" in the subject.

How to cite miRmap

If you use miRmap in your research, please cite this publication:

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