Bi-Force v2 – Large-scale Bicluster Editing and its application to Gene Expression data Biclustering

Bi-Force v2

:: DESCRIPTION

Bi-Force is a novel way of modeling the problem as combinatorial optimization problem on graphs: Weighted Bi-Cluster Editing. It is a very flexible model that can handle arbitrary kinds of multi-condition data sets (not limited to gene expression).

::DEVELOPER

Baumbach lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • Java

:: DOWNLOAD

 Bi-Force

:: MORE INFORMATION

Citation

Bi-Force: large-scale bicluster editing and its application to gene expression data biclustering.
Sun P, Speicher NK, Röttger R, Guo J, Baumbach J.
Nucleic Acids Res. 2014 May;42(9):e78. doi: 10.1093/nar/gku201.

FABIA 2.20.0 – Factor Analysis for Bicluster Acquisition

FABIA 2.20.0

:: DESCRIPTION

FABIA (Factor Analysis for Bicluster Acquisition) is a model-based technique for biclustering, that is clustering rows and columns simultaneously. FABIA is a multiplicative model that assumes realistic non-Gaussian signal distributions with heavy tails. FABIA utilizes well understood model selection techniques like variational approaches and applies the Bayesian framework. The generative framework allows FABIA to determine the information content of each bicluster to separate spurious biclusters from true biclusters. On 100 simulated data sets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors. FABIA was tested on microarray data sets which known, biological verfified subclusters and performed on average best out of 11 biclustering approaches.

::DEVELOPER

Institute of Bioinformatics, Johannes Kepler University Linz

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  FABIA

:: MORE INFORMATION

Citation

Sepp Hochreiter, Ulrich Bodenhofer, Martin Heusel, Andreas Mayr, Andreas Mitterecker, Adetayo Kasim, Tatsiana Khamiakova, Suzy Van Sanden, Dan Lin, Willem Talloen, Luc Bijnens, Hinrich W.H. Göhlmann, Ziv Shkedy, and Djork-Arné Clevert.
FABIA: Factor Analysis for Bicluster Acquisition,
Bioinformatics 2010, 26(12):1520-1527,

Furby 3.1.3beta1 – Fuzzy Force-Directed Bicluster Visualization

Furby 3.1.3beta1

:: DESCRIPTION

Furby is an interactive visualization technique for analyzing biclustering results.

::DEVELOPER

Furby team

:: SCREENSHOTS

Furby

:: REQUIREMENTS

  • Linux / Windows / MacOsX

:: DOWNLOAD

  Furby

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2014;15 Suppl 6:S4. doi: 10.1186/1471-2105-15-S6-S4. Epub 2014 May 16.
Furby: fuzzy force-directed bicluster visualization.
Streit M, Gratzl S, Gillhofer M, Mayr A, Mitterecker A, Hochreiter S.

Bicluster – Seed-based Biclustering of Gene Expression Data

Bicluster

:: DESCRIPTION

Bicluster is a seed-based algorithm that identifies coherent genes in an exhaustive, but efficient manner.

::DEVELOPER

Systems Biology of Gene Regulatory Elements

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  •  java

:: DOWNLOAD

 Bicluster

:: MORE INFORMATION

Citation

An J, Liew AW-C, Nelson CC (2012)
Seed-Based Biclustering of Gene Expression Data.
PLoS ONE 7(8): e42431. doi:10.1371/journal.pone.0042431

LEB – Localize and Extract Biclusters

LEB

:: DESCRIPTION

LEB is a software of biclustering gene expression data, based on Localize-and-Extract Biclusters

::DEVELOPER

Cesim ErtenMelih Sözdinler

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows
  • Visual Studio 2005 C++

:: DOWNLOAD

 LEB

:: MORE INFORMATION

Citation

“Biclustering Expression Data Based on Expanding Localized Substructures” ,
C. Erten, M. Sozdinler,
Proc. of Int. Conf. on Bioinformatics and Computational Biology (BICOB 09).

EDISA 1.0 – Extracting Biclusters from multiple Time-series of Gene Expression Profiles

EDISA 1.0

:: DESCRIPTION

EDISA (Extended Dimension Iterative Signature Algorithm) is a novel probabilistic clustering approach for 3D gene-condition-time datasets. Based on mathematical definitions of gene expression modules, the EDISA samples initial modules from the dataset which are then refined by removing genes and conditions until they comply with the module definition. A subsequent extension step ensures gene and condition maximality. We applied the algorithm to a synthetic dataset and were able to successfully recover the implanted modules over a range of background noise intensities.

EDISA Online Version

::DEVELOPER

the Center for Bioinformatics Tübingen (Zentrum für Bioinformatik Tübingen, ZBIT).

:: SCREENSHOTS

EDISA

:: REQUIREMENTS

  • Linux/ WIndows
  • Matlab

:: DOWNLOAD

  EDISA

:: MORE INFORMATION

Citation

Jochen Supper, Martin Strauch, Dierk Wanke, Klaus Harter, Andreas Zell:
EDISA: extracting biclusters from multiple time-series of gene expression profiles
BMC Bioinformatics 2007, 8:334

BiVisu 1.3 – Bicluster Visualization

BiVisu 1.3

:: DESCRIPTION

BiVisu is an open-source software tool for detecting and visualizing biclusters embedded in a gene expression matrix.

::DEVELOPER

Dr. Bonnie Law

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • Matlab

:: DOWNLOAD

 BiVisu

:: MORE INFORMATION

Citation

K.O. Cheng, N.F. Law, W.C. Siu and T.H. Lau,
BiVisu: Software Tool for Bicluster Detection and Visualization “,
Bioinformatics, Vol. 23, No. 17, 2342-2344, 2007

relax_bicluster – Bicluster based the Probabilistic Relaxation Labeling Framework

relax_bicluster

:: DESCRIPTION

relax_bicluster is a biclustering algorithm based the probabilistic relaxation labeling framework for discovering geometric biclusters of gene expression data.

::DEVELOPER

 Hong Yan , Signal Processing Lab at City University of Hong Kong

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 relax_bicluster

:: MORE INFORMATION

Citation:

Hongya Zhao et al.
A probabilistic relaxation labeling framework for reducing the noise effect in geometric biclustering of gene expression data
Journal Pattern Recognition Volume 42 Issue 11, November, 2009 Pages 2578-2588

ExpressionView 1.00 – Explore Biclusters Identified in Gene Expression data

ExpressionView 1.00

:: DESCRIPTION

ExpressionView is an R package that provides an interactive environment to explore biclusters identified in gene expression data. A sophisticated ordering algorithm is used to present the biclusters in a visually appealing layout. From this overview, the user can select individual biclusters and access all the biologically relevant data associated with it. The package is aimed to facilitate the collaboration between bioinformaticians and life scientists who are not familiar with the R language.

::DEVELOPER

Computational Biology Group ,Department of Medical Genetics, University of Lausanne

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

 ExpressionView

:: MORE INFORMATION

Citation

ExpressionView–an interactive viewer for modules identified in gene expression data.
Lüscher A, Csárdi G, de Lachapelle AM, Kutalik Z, Peter B, Bergmann S.
Bioinformatics. 2010 Aug 15;26(16):2062-3. Epub 2010 Jul 29.