siDirect 2.0 – Target Specific siRNA online Design Site

siDirect 2.0

:: DESCRIPTION

siDirect is a web server for providing efficient and target-specific siRNA design for mammalian RNAi. In this new version, the siRNA design algorithm has been extensively updated to eliminate off-target silencing effects by reflecting our recent finding that the capability of siRNA to induce off-target effect is highly correlated to the thermodynamic stability of the ‘seed’ duplex.

::DEVELOPER

Morishita Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation:

siDirect 2.0: updated software for designing functional siRNA with reduced seed-dependent off-target effect.
Naito Y, Yoshimura J, Morishita S, Ui-Tei K.
BMC Bioinformatics. 2009 Nov 30;10:392. doi: 10.1186/1471-2105-10-392.

TSMDA – Target and Symptom-based computational model for miRNA-disease Association Prediction

TSMDA

:: DESCRIPTION

TSMDA is a novel machine learning method that leverages target and symptom information and negative sample selection to predict miRNA-disease association.

::DEVELOPER

Biosig Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Uthayopas K, de Sá AGC, Alavi A, Pires DEV, Ascher DB.
TSMDA: Target and symptom-based computational model for miRNA-disease-association prediction.
Mol Ther Nucleic Acids. 2021 Aug 26;26:536-546. doi: 10.1016/j.omtn.2021.08.016. PMID: 34631283; PMCID: PMC8479276.

TargetProfiler – miRNA Target Prediction tool

TargetProfiler

:: DESCRIPTION

Targetprofiler is a novel miRNA target prediction tool that utilizes a probabilistic learning algorithm in the form of a hidden Markov model trained on experimentally verified miRNA targets.

::DEVELOPER

Ioannis Iliopoulos’ Bioinformatics & Computational Biology Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

RNA Biol. 2012 Sep;9(9):1196-207. doi: 10.4161/rna.21725. Epub 2012 Sep 1.
A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2.
Oulas A1, Karathanasis N, Louloupi A, Iliopoulos I, Kalantidis K, Poirazi P.

MiRTif – MicroRNA:Target Interaction Filter

MiRTif

:: DESCRIPTION

MiRTif is a machine learning algorithm based on SVM (support vector machine) that serves as a post-processing filter for the miRNA:target duplexes predicted by softwares such as miRanda, PicTar and TargetScanS.

::DEVELOPER

the Bioinformatics Institute (BII), Singapore.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

MiRTif: a support vector machine-based microRNA target interaction filter.
Yang Y, Wang YP, Li KB.
BMC Bioinformatics. 2008 Dec 12;9 Suppl 12:S4. doi: 10.1186/1471-2105-9-S12-S4.

CleaveLand 4.5 – Find Cleaved Small RNA Targets

CleaveLand 4.5

:: DESCRIPTION

CleaveLand is a command-line executed pipeline for finding cleaved small RNA targets using degradome data (also known as PARE [parallel analysis of RNA ends] and GMUCT [genome-wide mapping of uncapped transcripts]). Provided with a set of degradome data, a list of small RNA queries, a reference transcriptome/mRNA set, CleaveLand outputs potentially cleaved small RNA targets along with other supporting information.

::DEVELOPER

Axtell Lab @ Penn State

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows / MacOsX
Perl
Targetfinder

:: DOWNLOAD

CleaveLand

:: MORE INFORMATION

Citation:

Addo-Quaye, C., Miller, W., and Axtell, M.J. (2009).
CleaveLand: A pipeline for using degradome data to find cleaved small RNA targets.
Bioinformatics 25: 130-131.

SAROTUP 3.1 – Target-Unrelated Peptides Scanners

SAROTUP 3.1

:: DESCRIPTION

SAROTUP is a suite of web tools that can scan, report and exclude possible target-unrelated peptides from the noisy experiment results of phage display.

::DEVELOPER

HLAB: Huang’s LAB

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows

:: DOWNLOAD

SAROTUP

:: MORE INFORMATION

Citation

J Biomed Biotechnol. 2010;2010:101932. doi: 10.1155/2010/101932. Epub 2010 Mar 21.
SAROTUP: scanner and reporter of target-unrelated peptides.
Huang J1, Ru B, Li S, Lin H, Guo FB.

Brownian Motion Simulator – Estimating Minimum Passage Time and Docking Ratio of Nanoparticles to Specified Target

Brownian Motion Simulator

:: DESCRIPTION

Brownian Motion Simulator estimates the incubation time and the docking ratio of nanoparticles to a target spot. The results are diagrams about the docking ratio and the incubation time.

::DEVELOPER

PIT Bioinformatics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

IEEE Trans Nanobioscience. 2011 Dec;10(4):248-9. doi: 10.1109/TNB.2011.2169331. Epub 2011 Sep 23.
3-d brownian motion simulator for high-sensitivity nanobiotechnological applications.
Toth A1, Banky D, Grolmusz V.

aTRAM 2.4.3 – automated Target Restricted Assembly Method

aTRAM 2.4.3

:: DESCRIPTION

aTRAM performs targeted de novo assembly of loci from paired-end Illumina runs.

::DEVELOPER

aTRAM team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOsX
Perl

aTRAM

:: MORE INFORMATION

Citation

aTRAM – automated target restricted assembly method: a fast method for assembling loci across divergent taxa from next-generation sequencing data.
Allen JM, Huang DI, Cronk QC, Johnson KP.
BMC Bioinformatics. 2015 Mar 25;16(1):98.

CCTop 1.0.0 – CRISPR/Cas9 Target Online Predictor

CCTop 1.0.0

:: DESCRIPTION

CCTop is a CRISPR/Cas9 target online predictor

::DEVELOPER

The Centre for Organismal Studies (COS) Heidelberg

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

CCTop

:: MORE INFORMATION

Citation

CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Tool.
Stemmer M, Thumberger T, Del Sol Keyer M, Wittbrodt J, Mateo JL.
PLoS One. 2015 Apr 24;10(4):e0124633. doi: 10.1371/journal.pone.0124633.

TargetMiner – microRNA Target Prediction

TargetMiner

:: DESCRIPTION

TargetMiner is a software to find information about microRNA target mRNA. The classifier Support Vector Machine (SVM) is used to classify the test data. The SVM is trained with a set of biologically validated positive (miRNA- target pairs) and newly generated negative examples (miRNA- non target pairs). A set of 90 targeting site context specific features is then extracted from the training examples. From this a set of 30 most favorable features with high F-Score is then selected to train the classifier.

::DEVELOPER

TargetMiner Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

TargetMiner

:: MORE INFORMATION

Citation

Sanghamitra Bandyopadhyay and Ramkrishna Mitra,
“Targetminer:MicroRNA Target Prediction with Systematic Identification Of Tissue Specific Negative Examples“,
Bioinformatics, Vol. 25, no. 20, pp. 2625 – 2631, 2009.