HHMMiR 1.2 – Prediction of microRNAs using Hierarchical Hidden Markov models

HHMMiR 1.2

:: DESCRIPTION

HHMMiR is a novel approach for de novo miRNA hairpin prediction in the absence of evolutionary conservation. HHMMiR implements a Hierarchical Hidden Markov Model (HHMM) that utilizes region-based structural as well as sequence information of miRNA precursors.

:: DEVELOPER

Benos Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 HHMMiR

:: MORE INFORMATION

Citation:

S. Kadri, V. Hinman, P.V. Benos,
HHMMiR: Efficient de novo Prediction of MicroRNAs using Hierarchical Hidden Markov Models“,
BMC Bioinformatics (Proc APBC 2009) (2009) 10 (Suppl 1):S35.

SOLart 1.0 – Protein Solubility Prediction

SOLart 1.0

:: DESCRIPTION

SOLart is a fast and accurate method for predicting the protein solubility of a target protein whose experimental or modeled structure is available. It yields a scaled solubility score with values close to zero indicating aggregate-prone proteins, while values close to 130 designate soluble proteins.

::DEVELOPER

Service de Biomodélisation, Bioinformatique et Bioprocédés (3BIO)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics, 36 (5), 1445-1452 2020 Mar 1
SOLart: A Structure-Based Method to Predict Protein Solubility and Aggregation
Qingzhen Hou , Jean Marc Kwasigroch, Marianne Rooman, Fabrizio Pucci

SCooP 1.0 – Prediction of the Stability Curve of Proteins

SCooP 1.0

:: DESCRIPTION

SCooP is a fast and accurate method for predicting the Gibbs-Helmholtz equation associated to the folding transition of a target protein of known (or modeled) structure. In addition, SCooP yields an estimation of the thermodynamic quantities that characterize the folding process, in particular the change in enthalpy and in heat capacity upon folding, the melting temperature and the folding free energy at room temperature.

::DEVELOPER

Service de Biomodélisation, Bioinformatique et Bioprocédés (3BIO)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

F. Pucci, J.M. Kwasigroch, M. Rooman (2017),
SCooP : an accurate and fast predictor of protein stability curves as a function of the temperature,
Bioinformatics 33, 3415-3422.

ArchPred – Knwoledge-based Loop Structure Prediction method

ArchPred

:: DESCRIPTION

ARCH_Pred is a web application that interface a knowledge-based loop structure prediction method. Given a query loop of unknown structure, ARCH_Pred identifies the most suitable loops from a library of structures of protein loops.

::DEVELOPER

Bioinformatics Lab :: IBERS :: Aberystwyth University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Server

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

ArchPRED: a template based loop structure prediction server.
Fernandez-Fuentes N, Zhai J, Fiser A.
Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W173-6.

Phos3D – Prediction of Phosphorylation Sites (P-sites) in Proteins

Phos3D

:: DESCRIPTION

Phos3D is a web server for the prediction of phosphorylation sites (P-sites) in proteins. The approach is based on Support Vector Machines trained on sequence profiles enhanced by information from the spatial context of experimentally identified P-sites.

::DEVELOPER

Max Planck Institute for Molecular Plant Physiology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2009 Apr 21;10:117.
Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction in proteins.
Durek P, Schudoma C, Weckwerth W, Selbig J, Walther D.

CPSP Tools 4.8.0 – Constraint-based Protein Structure Prediction

CPSP Tools 4.8.0

:: DESCRIPTION

CPSP-tools package provides programs to solve exactly and completely the problems typical of studies using 3D lattice protein models. Among the tasks addressed are the prediction of globally optimal and/or suboptimal structures as well as sequence design and neutral network exploration.

::DEVELOPER

Bioinformatics Group
Albert-Ludwigs-University Freiburg

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 CPSP Tools

:: MORE INFORMATION

Citation

Martin Mann, Sebastian Will, and Rolf Backofen.
CPSP-tools – Exact and Complete Algorithms for High-throughput 3D Lattice Protein Studies.
BMC Bioinformatics, 9, 230, 2008.

MoDPepInt 4.8.0 – Prediction of Modular Domain-peptide Interactions

MoDPepInt 4.8.0

:: DESCRIPTION

MoDPepInt (Modular Domain Peptide Interaction) is a new, easy-to-use webserver for the prediction of binding partners for modular protein domains. The server comprises three different tools, i.e. SH2PepInt, SH3PepInt and PDZPepInt, for predicting the binding partners of three different modular protein domains, i.e. SH2, SH3 and PDZ domains, respectively.

::DEVELOPER

Chair for Bioinformatics Freiburg

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Server

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bioinformatics. 2014 May 28. pii: btu350. [Epub ahead of print]
MoDPepInt: An interactive webserver for prediction of modular domain-peptide interactions.
Kundu K1, Mann M1, Costa F1, Backofen R2.

CopraRNA 2.1.3 – sRNA Target Prediction utilizing Homology

CopraRNA 2.1.3

:: DESCRIPTION

CopraRNA (Comparative Prediction Algorithm for sRNA Targets) is a tool for sRNA target prediction. It computes whole genome predictions by combination of distinct whole genome IntaRNA predictions. As input, CopraRNA requires at least 3 homologous sRNA sequences from 3 distinct organisms in FASTA format.

::DEVELOPER

Chair for Bioinformatics Freiburg

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl

:: DOWNLOAD

 CopraRNA 

:: MORE INFORMATION

Citation

Proc Natl Acad Sci U S A. 2013 Sep 10;110(37):E3487-96. doi: 10.1073/pnas.1303248110. Epub 2013 Aug 26.
Comparative genomics boosts target prediction for bacterial small RNAs.
Wright PR1, Richter AS, Papenfort K, Mann M, Vogel J, Hess WR, Backofen R, Georg J.

MultiTF-PPI – Competitive Transcription Factor Binding Prediction

MultiTF-PPI

:: DESCRIPTION

MultiTF-PPI is a probabilistic protein-protein interaction guided method for competitive transcription factor binding prediction.

::DEVELOPER

Computational systems biology group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX / Windows
  • Matlab

:: DOWNLOAD

MultiTF-PPI

:: MORE INFORMATION

EasyGene 1.2c – Prediction of Genes in Prokaryotes

EasyGene 1.2c

:: DESCRIPTION

EasyGene estimates the statistical significance of a predicted gene. The gene finder is based on a hidden Markov model (HMM) that is automatically estimated for a new genome. Using extensions of similarities in Swiss-Prot, a high quality training set of genes is automatically extracted from the genome and used to estimate the HMM. Putative genes are then scored with the HMM, and based on score and length of an ORF, the statistical significance is calculated. The measure of statistical significance for an ORF is the expected number of ORFs in one megabase of random sequence at the same significance level or better, where the random sequence has the same statistics as the genome in the sense of a third order Markov chain.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

EasyGene

:: MORE INFORMATION

Citation

Large-scale prokaryotic gene prediction and comparison to genome annotation.
P. Nielsen and A. Krogh.
Bioinformatics: 21:4322-4329, 2005.

EasyGene – a prokaryotic gene finder that ranks ORFs by statistical significance.
Thomas Schou Larsen and Anders Krogh.
BMC Bioinformatics: 4:21, 2003