bgmm 1.8.3 – Belief-based Gaussian Mixture Modeling

bgmm 1.8.3

:: DESCRIPTION

bgmm is an R package for knowledge-based mixture modeling. It implements mixture modeling variants, which differ with respect to the amount of incorporated knowledge, and spread the entire range from unsupervised to supervised modeling.

:: DEVELOPER

Vingron lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • R

:: DOWNLOAD

 bgmm

:: MORE INFORMATION

Citation

Introducing knowledge into differential expression analysis.
Szczurek E, Biecek P, Tiuryn J, Vingron M.
J Comput Biol. 2010 Aug;17(8):953-67. doi: 10.1089/cmb.2010.0034.

IMOD 4.9.12 – Tomographic 3D Reconstruction & Modeling

IMOD 4.9.12

:: DESCRIPTION

IMOD is a set of image processing, modeling and display programs used for tomographic reconstruction and for 3D reconstruction of EM serial sections and optical sections. The package contains tools for assembling and aligning data within multiple types and sizes of image stacks, viewing 3-D data from any orientation, and modeling and display of the image files.

::DEVELOPER

The Boulder Lab for 3D Electron Microscopy

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

IMOD ; source code

:: MORE INFORMATION

The citation is: Kremer J.R., D.N. Mastronarde and J.R. McIntosh (1996) Computer visualization of three-dimensional image data using IMOD. J. Struct. Biol. 116:71-76. For tomographic reconstruction, see also: Mastronarde, D. N. (1997) Dual-axis tomography: an approach with alignment methods that preserve resolution. J. Struct. Biol. 120:343-352.

PDEGEM 20140325 – Modeling non-uniform Read Distribution in RNA-seq data

PDEGEM 20140325

:: DESCRIPTION

PDEGEM (Positional Dependent Energy Guided Expression Model ) is a robust nonlinear regression model to estimate the abundance of transcripts

::DEVELOPER

Minghua Deng

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • R

:: DOWNLOAD

 PDEGEM

:: MORE INFORMATION

Citation

BMC Med Genomics. 2015 May 29;8 Suppl 2:S14. doi: 10.1186/1755-8794-8-S2-S14. Epub 2015 May 29.
PDEGEM: Modeling non-uniform read distribution in RNA-Seq data.
Xia Y, Wang F, Qian M, Qin Z, Deng M.

MAìSTAS – Modeling and Assessment of ISoforms Through Automated Server

MAìSTAS

:: DESCRIPTION

Maìstas is a fully automatic pipeline aimed at building and assessing three-dimensional models for alternative splicing isoforms. The server builds, when possible, comparative structural models for all the splicing isoforms of a submitted gene or set of genes. The models are then analysed in terms of their suitability to exist in the monomeric state, i.e. when a warning appears in the model assessment, it cannot be excluded the possibility that other multimeric state may stabilize the structure.

::DEVELOPER

MAìSTAS team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Jun 15;27(12):1625-9. doi: 10.1093/bioinformatics/btr198. Epub 2011 Apr 15.
MAISTAS: a tool for automatic structural evaluation of alternative splicing products.
Floris M1, Raimondo D, Leoni G, Orsini M, Marcatili P, Tramontano A.

BMix – Probabilistic Modeling of Occurring Substitutions in PAR-CLIP data

BMix

:: DESCRIPTION

BMix is a toolbox for analysing PAR-CLIP data and detecting T-to-C substitutions induced following RNA-protein cross-linking.

::DEVELOPER

the Computational Biology Group (CBG)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 BMix

:: MORE INFORMATION

Citation:

BMix: Probabilistic modeling of occurring substitutions in PAR-CLIP data.
Golumbeanu M, Mohammadi P, Beerenwinkel N.
Bioinformatics. 2015 Sep 5. pii: btv520.

HADDOCK 2.2 – Docking approach for the Modeling of Biomolecular Complexes

HADDOCK 2.2

:: DESCRIPTION

HADDOCK (High Ambiguity Driven protein-protein DOCKing) is an information-driven flexible docking approach for the modeling of biomolecular complexes. HADDOCK distinguishes itself from ab-initio docking methods in the fact that it encodes information from identified or predicted protein interfaces in ambiguous interaction restraints (AIRs) to drive the docking process. HADDOCK can deal with a large class of modeling problems including protein-protein, protein-nucleic acids and protein-ligand complexes.

Haddock Server

::DEVELOPER

BONVIN LAB

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX
  • Python

:: DOWNLOAD

  HADDOCK 

:: MORE INFORMATION

Citation

The HADDOCK2.2 web server: User-friendly integrative modeling of biomolecular complexes.
van Zundert GC, Rodrigues JP, Trellet M, Schmitz C, Kastritis PL, Karaca E, Melquiond AS, van Dijk M, de Vries SJ, Bonvin AM.
J Mol Biol. 2015 Sep 24. pii: S0022-2836(15)00537-9. doi: 10.1016/j.jmb.2015.09.014

Cyril Dominguez, Rolf Boelens and Alexandre M.J.J. Bonvin (2003).
HADDOCK: a protein-protein docking approach based on biochemical and/or biophysical information.
J. Am. Chem. Soc. 125, 1731-1737

S.J. de Vries, A.D.J. van Dijk, M. Krzeminski, M. van Dijk, A. Thureau, V. Hsu, T. Wassenaar and A.M.J.J. Bonvin
HADDOCK versus HADDOCK: New features and performance of HADDOCK2.0 on the CAPRI targets.
Proteins: Struc. Funct. & Bioinformatic 69, 726-733 (2007).

ProbRNA 20131213 – Modeling RNA Structure Probing data

ProbRNA 20131213

:: DESCRIPTION

ProbRNA produces features found to be related to both the secondary and tertiary structures of the RNAs.

::DEVELOPER

Kevin Yuk-Lap Yip

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / WIndows/ MacOsX
  • Perl
  • R Package

:: DOWNLOAD

 ProbRNA

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Jan 21.
Computational identification of protein binding sites on RNAs using high-throughput RNA structure-probing data.
Hu X1, Wong TK, Lu ZJ, Chan TF, Lau TC, Yiu SM, Yip KY.

GeneNet 1.2.14 – Modeling and Inferring Gene Networks

GeneNet 1.2.14

:: DESCRIPTION

GeneNet is a package for analyzing gene expression (time series) data with focus on the inference of gene networks.

::DEVELOPER

Strimmer Lab

:: SCREENSHOTS

GeneNet

:: REQUIREMENTS

  • Windows / Linux / MacOSX
  • R package

:: DOWNLOAD

 GeneNet

:: MORE INFORMATION

Citation

BMC Syst Biol. 2007 Aug 6;1:37.
From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data.
Opgen-Rhein R, Strimmer K.