MinoTar – Predict microRNA Targets in Coding Sequence

MinoTar

:: DESCRIPTION

MinoTar predicts microRNA coding region targets by searching for highly conserved microRNA seed sites in coding regions. Conservation is judged conditionally on the observed amino acid evolution.

::DEVELOPER

the Perrimon Lab and Bonnie Berger‘s group at MIT.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • WebServer

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Proc Natl Acad Sci U S A. 2010 Sep 7;107(36):15751-6. doi: 10.1073/pnas.1006172107.
Conserved microRNA targeting in Drosophila is as widespread in coding regions as in 3’UTRs.
Schnall-Levin M, Zhao Y, Perrimon N, Berger B.

TAPIR 1.2 – Prediction of Plant microRNA Targets

TAPIR 1.2

:: DESCRIPTION

TAPIR is a web server designed for the prediction of plant microRNA targets. The server offers the possibility to search for plant miRNA targets using a fast and a precise algorithm. The precise option is much slower but guarantees to find less perfectly paired miRNA – target duplexes. Furthermore, the precise option allows the prediction of target mimics, which are characterized by a miRNA – target duplex having a large loop, making them undetectable by traditional tools.

::DEVELOPER

Van de Peer Lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux
  • Perl
  • Bioperl
  • ViennaRNA library + perl modules

:: DOWNLOAD

 TAPIR

:: MORE INFORMATION

Citation

Bonnet, E., He, Y., Billiau, K., Van de Peer, Y. (2010)
TAPIR, a web server for the prediction of plant microRNA targets, including target mimics.
Bioinformatics 26, 1566-1568.

TargetScore 1.24.0 – Infer microRNA Targets using microRNA-overexpression data and Sequence Information

TargetScore 1.24.0

:: DESCRIPTION

TargetScore: a probabilistic approach to explore human miRNA targetome by integrating miRNA-overexpression data and sequence information

::DEVELOPER

Yue Li @ The Zhang Lab, University of Toronto

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Weindows/Linux/MacOsX
  • R package
  • BioConductor

:: DOWNLOAD

 TargetScore

 :: MORE INFORMATION

Citation

Bioinformatics. 2014 Mar 1;30(5):621-8. doi: 10.1093/bioinformatics/btt599. Epub 2013 Oct 17.
A probabilistic approach to explore human miRNA targetome by integrating miRNA-overexpression data and sequence information.
Li Y1, Goldenberg A, Wong KC, Zhang Z.

PACCMIT-CDS / PACCMIT – MicroRNA Target Predictions

PACCMIT-CDS / PACCMIT

:: DESCRIPTION

PACCMIT-CDS is a software that finds potential microRNA targets within coding sequences by searching for conserved motifs that are complementary to the microRNA seed region and also overrepresented in comparison with a background model preserving both codon usage and amino acid sequence.

PACCMIT (Prediction of ACcessible and/or Conserved MIcroRNA Targets) is a flexible algorithm that filters potential miRNA binding sites in 3’UTR regions by their conservation, accessibility, or both, and then ranks the predictions according to the over-representation with respect to a random background based on a Markov model.

::DEVELOPER

LABORATORY OF THEORETICAL PHYSICAL CHEMISTRY, LCPT

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 PACCMIT-CDS

:: MORE INFORMATION

Citation

PACCMIT/PACCMIT-CDS: identifying microRNA targets in 3′ UTRs and coding sequences.
Šulc M, Marín RM, Robins HS, Vaníček J.
Nucleic Acids Res. 2015 May 6. pii: gkv457

Searching the coding region for microRNA targets.
Marín RM, Sulc M, Vanícek J.
RNA. 2013 Apr;19(4):467-74. doi: 10.1261/rna.035634.112.

Optimal use of conservation and accessibility filters in microRNA target prediction.
Marín RM, Vanícek J.
PLoS One. 2012;7(2):e32208. doi: 10.1371/journal.pone.0032208.

SVMicrO 20100315 – microRNA Target Prediction

SVMicrO 20100315

:: DESCRIPTION

SVMmicrO is a microRNA target prediction algorithm that utilizes a comprehensive microRNA binding features.

::DEVELOPER

Dr. Yufei Huang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 SVMicrO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2010 Sep 22;11:476. doi: 10.1186/1471-2105-11-476.
Improving performance of mammalian microRNA target prediction.
Liu H, Yue D, Chen Y, Gao SJ, Huang Y.