Tag: Interactions

Selfish – Discovery of Differential Chromatin Interactions via a Self-Similarity Measure

Selfish

:: DESCRIPTION

SELFISH is a tool for finding differential chromatin interactions between two Hi-C contact maps. It uses self-similarity to model interactions in a robust way.

::DEVELOPER

Lonardi Bioinformatics Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • Matlab

:: DOWNLOAD

SELFISH

:: MORE INFORMATION

Citation

Ardakany AR, Ay F, Lonardi S.
Selfish: discovery of differential chromatin interactions via a self-similarity measure.
Bioinformatics. 2019 Jul 15;35(14):i145-i153. doi: 10.1093/bioinformatics/btz362. PMID: 31510653; PMCID: PMC6612869.

FpClass – Interactions and Properties of Human Proteins

FpClass

:: DESCRIPTION

FpClass is a data mining-based method for proteome-wide PPI prediction.

::DEVELOPER

Jurisica Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl

:: DOWNLOAD

 FpClass

:: MORE INFORMATION

Citation

In silico prediction of physical protein interactions and characterization of interactome orphans.
Kotlyar M, Pastrello C, Pivetta F, Lo Sardo A, Cumbaa C, Li H, Naranian T, Niu Y, Ding Z, Vafaee F, Broackes-Carter F, Petschnigg J, Mills GB, Jurisicova A, Stagljar I, Maestro R, Jurisica I.
Nat Methods. 2015 Jan;12(1):79-84. doi: 10.1038/nmeth.3178.

Gremlin – Genome Rearrangement Explorer with Multi-Scale, Linked Interactions

Gremlin

:: DESCRIPTION

Gremlin is an interactive visualization model for the comparative analysis of structural variation in human and cancer genomes.

::DEVELOPER

Raphael Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  Gremlin

:: MORE INFORMATION

Citation

T.M. O’Brien, A. Ritz, B.J. Raphael, and D.H. Laidlaw. (2010)
Gremlin: An Interactive Visualization Model for Analyzing Genomic Rearrangements.
IEEE Transactions on Visualization and Computer Graphics. vol.16, no.6, pp.918-926.

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.

SNPAssociation – Detecting Two-locus Associations allowing for Interactions in GWAS

SNPAssociation

:: DESCRIPTION

SNPAssociation is a code for testing associations allowing for interactions in genome-wide association studies.

::DEVELOPER

Laboratory for Bioinformatics and Computational Biology, HKUST

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 SNPAssociation

:: MORE INFORMATION

Citation:

Bioinformatics. 2010 Oct 15;26(20):2517-25. doi: 10.1093/bioinformatics/btq486. Epub 2010 Aug 24.
Detecting two-locus associations allowing for interactions in genome-wide association studies.
Wan X1, Yang C, Yang Q, Xue H, Tang NL, Yu W.

CHpredict – Predict C-H…O and C-H…PI Interactions

CHpredict

:: DESCRIPTION

The CHpredict server predict two types of interactions: C-H…O and C-H…PI interactions. For C-H…O interaction, the server predicts the residues whose backbone Calpha atoms are involved in interaction with backbone oxygen atoms and for C-H…PI interactions, it predicts the residues whose backbone Calpha atoms are involved in interaction with PI ring system of side chain aromatic moieties.

::DEVELOPER

CHpredict Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Kaur, H. and Raghava, G.P.S. (2006)
Prediction of Cα-H…O and Cα-H…π interactions in proteins using recurrent neural network. 
In-Silico Biology 6, 0011