Tag: Prediction

miREval 2.0 – microRNA Prediction in Genome Sequences

miREval 2.0

:: DESCRIPTION

miREval is an online tool that can simultaneously search up to 100 sequences for novel microRNAs (miRNAs) in multiple organisms.

::DEVELOPER

Centenary Institute – Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

NO

:: MORE INFORMATION

Citation

Gao D, Middleton R, Rasko JE, Ritchie W.
miREval 2.0: a web tool for simple microRNA prediction in genome sequences.
Bioinformatics. 2013 Dec 15;29(24):3225-6. doi: 10.1093/bioinformatics/btt545. Epub 2013 Sep 18. PMID: 24048357; PMCID: PMC5994938.

Morpheus 2.0 – Prediction of Transcription Factors Binding Sites based on Position Weight Matrix

Morpheus 2.0

:: DESCRIPTION

Morpheus offers a range of tools to analyze transcription factor binding sites (TFBS) on DNA sequences.

::DEVELOPER

BIODEV

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • Python

:: DOWNLOAD

 Morpheus

:: MORE INFORMATION

PhosphoSVM – Non-kinase-specific Phosphorylation site Prediction tool

PhosphoSVM

:: DESCRIPTION

PhosphoSVM is a web server for prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine.

::DEVELOPER

System Biology Laboratory Of Chi Zhang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Amino Acids. 2014 Jun;46(6):1459-69. doi: 10.1007/s00726-014-1711-5. Epub 2014 Mar 13.
PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine.
Dou Y1, Yao B, Zhang C.

SCLpredT – Protein Subcellular Localization Prediction

SCLpredT

:: DESCRIPTION

SCLpredT is an enhanced version of SCLpred (Subcellular Localisation), in that: it incorporates homology information to proteins of known localization ; it is trained on a larger dataset; in has one more output class (“organelle”).

::DEVELOPER

Gianluca Pollastri group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Springerplus. 2013 Oct 3;2:502. doi: 10.1186/2193-1801-2-502. eCollection 2013.
SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks.
Adelfio A1, Volpato V, Pollastri G.

Geptop 2.0 – Gene Essentiality Prediction tool for COMPLETE-GENOME based on Orthology and Phylogeny

Geptop 2.0

:: DESCRIPTION

Geptop is a webserver, which first provides a platform to detect essential gene sets over bacterial species, by comparing orthology and phylogeny of query protein sets with essential gene datasets determined experimentally (from DEG database).

::DEVELOPER

Microbe and Drug Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux
  • Python
  • BioPython

:: DOWNLOAD

 Geptop

:: MORE INFORMATION

Citation

Wen QF, Wei W, Guo FB.
Geptop 2.0: Accurately Select Essential Genes from the List of Protein-Coding Genes in Prokaryotic Genomes.
Methods Mol Biol. 2022;2377:423-430. doi: 10.1007/978-1-0716-1720-5_23. PMID: 34709630.

PLoS One. 2013 Aug 15;8(8):e72343. doi: 10.1371/journal.pone.0072343. eCollection 2013.
Geptop: a gene essentiality prediction tool for sequenced bacterial genomes based on orthology and phylogeny.
Wei W1, Ning LW, Ye YN, Guo FB.

PepSite 2 – Prediction of Peptide Binding Sites on Protein Surfaces

PepSite 2

:: DESCRIPTION

PepSite can predict binding of a given peptide onto a protein structure, enabling users to better understand the details of the interaction of interest.

::DEVELOPER

Russell Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

PepSite: prediction of peptide-binding sites from protein surfaces.
Trabuco LG, Lise S, Petsalaki E, and Russell RB.
Nucleic Acids Res. 2012; 40(Web Server issue):W423-426.

InterPreTS – Interaction Prediction through Tertiary Structure

InterPreTS

:: DESCRIPTION

Given a set of protein sequences (in fasta format), InterPreTS will use BLAST to find homologues of known structure for all pairs (i.e. templates that can model each pair of sequences based on homology) and then evaluate the suitability of those templates for modelling the interaction

::DEVELOPER

the Cell Networks Protein Evolution group based at the University of Heidelberg.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl

:: DOWNLOAD

 InterPreTS

:: MORE INFORMATION

Citation

Aloy P, Russell RB.
InterPreTS: protein interaction prediction through tertiary structure.
Bioinformatics. 2003 Jan;19(1):161-2.

CABS-fold – Server for de novo and Consensus-based prediction of Protein Structure

CABS-fold

:: DESCRIPTION

CABS-fold is a server that provides tools for protein structure prediction from sequence only (de novo modeling) and also using alternative templates (consensus modeling).

::DEVELOPER

Laboratory of Theory of Biopolymers,  University of Warsaw

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2013 Jul;41(Web Server issue):W406-11. doi: 10.1093/nar/gkt462. Epub 2013 Jun 8.
CABS-fold: Server for the de novo and consensus-based prediction of protein structure.
Blaszczyk M1, Jamroz M, Kmiecik S, Kolinski A.

Terminus 0.4.1 – N-Terminal PTM prediction

Terminus 0.4.1

:: DESCRIPTION

Terminus predicts the initial methionine cleavage and the N-terminal acetylation.

::DEVELOPER

Terminus team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2014 May 2. [Epub ahead of print]
Motifs tree: a new method for predicting post-translational modifications.
Charpilloz C1, Veuthey AL, Chopard B, Falcone JL.

RNAstructure 6.3 – Prediction & Analysis of RNA Secondary Structure

RNAstructure 6.3

:: DESCRIPTION

RNAstructure is a complete package for RNA and DNA secondary structure prediction and analysis. It includes algorithms for secondary structure prediction, including facility to predict base pairing probabilities. It also can be used to predict bimolecular structures and can predict the equilibrium binding affinity of an oligonucleotide to a structured RNA target. This is useful for siRNA design. It can also predict secondary structures common to two, unaligned sequences, which is much more accurate than single sequence secondary structure prediction. Finally, RNAstructure can take a number of different types of experiment mapping data to constrain or restrain structure prediction. These include chemical mapping, enzymatic mapping, NMR, and SHAPE data.

::DEVELOPER

Mathews Lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Mac / Windows
  • Java

:: DOWNLOAD

RNAstructure

:: MORE INFORMATION

Citation

AccessFold: Predicting RNA-RNA Interactions with Consideration for Competing Self-Structure.
DiChiacchio L, Sloma MF, Mathews DH.
Bioinformatics. 2015 Nov 20. pii: btv682.

Reuter, J. S., & Mathews, D. H. (2010).
RNAstructure: software for RNA secondary structure prediction and analysis.
BMC Bioinformatics. 11,129.