MetaGeneAlyse – Analysis of Gene Expression and Metabolite data

MetaGeneAlyse

:: DESCRIPTION

MetaGeneAlyse is a web-based tool for the visualization and analysis of large-scale transcript and metabolite profile datasets. Standard methods (PCA, clustering) are provided as well as state-of-the-art methods such as independent component analysis (ICA).

::DEVELOPER

Max Planck Institute for Molecular Plant Physiology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2003 Nov 22;19(17):2332-3.
MetaGeneAlyse: analysis of integrated transcriptional and metabolite data.
Daub CO, Kloska S, Selbig J.

AutoSOME 2.0 – Clustering method for Identifying Gene Expression Modules

AutoSOME 2.0

:: DESCRIPTION

AutoSOME (Automatic clustering of density-equalized self-organizing map ensembles) is a new unsupervised multidimensional clustering method for identifying clusters of diverse shapes and sizes from large numerical datasets without prior knowledge of cluster number. Given the general nature of data clustering, AutoSOME has utility for a wide range of applications, including whole-genome co-expression and transcriptome analysis.

::DEVELOPER

Newman Lab

:: SCREENSHOTS

AutoSOME

:: REQUIREMENTS

  • Linux /  MacOsX/ Windows
  • Java

:: DOWNLOAD

  AutoSOME

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2010 Mar 4;11:117. doi: 10.1186/1471-2105-11-117.
AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number.
Newman AM, Cooper JB.

targetedRetrieval 120705 – Targeted Retrieval of Gene Expression Measurements Using Regulatory Models

targetedRetrieval 120705

:: DESCRIPTION

targetedRetrieval is a model for the regulation of specific genes from a data repository and exploit it to construct a similarity metric for an information retrieval task.

::DEVELOPER

Probabilistic Machine Learning

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows /MacOsX
  • R package

:: DOWNLOAD

  targetedRetrieval

:: MORE INFORMATION

Citation

Elisabeth Georgii, Jarkko Salojärvi, Mikael Brosché, Jaakko Kangasjärvi, and Samuel Kaski.
Targeted retrieval of gene expression measurements using regulatory models.
Bioinformatics (2012) 28 (18): 2349-2356.

OLYMPUS – Hybrid Clustering method in Time Series Gene Expression

OLYMPUS

:: DESCRIPTION

OLYMPUS is an automated hybrid clustering method in the field of time series gene expression analysis.

::DEVELOPER

the Biosignal Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • MatLab

:: DOWNLOAD

 OLYMPUS

:: MORE INFORMATION

Citation

OLYMPUS: an automated hybrid clustering method in time series gene expression. Case study: host response after Influenza A (H1N1) infection.
Dimitrakopoulou K, Vrahatis AG, Wilk E, Tsakalidis AK, Bezerianos A.
Comput Methods Programs Biomed. 2013 Sep;111(3):650-61. doi: 10.1016/j.cmpb.2013.05.025.

DeltaNet 1.0 – Predicting Genetic Perturbations from Gene Expression data

DeltaNet 1.0

:: DESCRIPTION

DeltaNet is a network analysis tool for predicting genetic perturbations from gene expression data.

:: DEVELOPER

Chemical and Biological Systems Engineering Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / windows/ MacOsX
  • MatLab / R

:: DOWNLOAD

 DeltaNet

:: MORE INFORMATION

Citation

Noh, H., and Gunawan, R.
Direct inference of gene targets of drugs and chemical compounds from gene expression profiles, 2015.
submitted.

TriCluster / MicroCluster – Microarray Gene Expression Clustering

TriCluster / MicroCluster

:: DESCRIPTION

Tricluster is the first tri-clustering algorithm for microarray expression clustering. It builds upon the new microCluster bi-clustering approach. Tricluster first mines all the bi-clusters across the gene-sample slices, and then it extends these into tri-clusters across time or space (depending on the third dimension). It can find both scaling and shifting patterns

MicroCluster is a deterministic biclustering algorithm that can mine arbitrarily positioned and overlapping clusters of gene expression data to find interesting patterns

::DEVELOPER

Mohammed J. Zaki

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 TriCluster / MicroCluster

:: MORE INFORMATION

Citation

Lizhuang Zhao and Mohammed J. Zaki,
TriCluster: An Effective Algorithm for Mining Coherent Clusters in 3D Microarray Data.
In ACM SIGMOD Conference on Management of Data. Jun 2005.

Lizhuang Zhao and Mohammed J. Zaki,
MicroCluster: An Efficient Deterministic Biclustering Algorithm for Microarray Data.
IEEE Intelligent Systems, 20(6):40-49. Nov/Dec 2005

GiniClust3 1.0.1 – Detecting Rare Cell Types from Single-cell Gene Expression data with Gini Index

GiniClust 3 1.0.1

:: DESCRIPTION

GiniClust is a clustering method specifically designed for rare cell type detection. It uses the Gini index to identify genes that are associated with rare cell types without prior knowledge.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • Python

:: DOWNLOAD

GiniClust

:: MORE INFORMATION

Citation

Rui Dong. Guo-Cheng Yuan.
GiniClust3: a fast and memory-efficient tool for rare cell type identification.

Genome Biol, 17 (1), 144 2016 Jul 1
GiniClust: Detecting Rare Cell Types From Single-Cell Gene Expression Data With Gini Index
Lan Jiang, Huidong Chen, Luca Pinello, Guo-Cheng Yuan

Tsoucas D, Yuan GC.
GiniClust2: a cluster-aware, weighted ensemble clustering method for cell-type detection.
Genome Biology. 2018 May 10;19(1):58.

ECLAIR – Robust Lineage Reconstruction from Single-cell Gene Expression data

ECLAIR

:: DESCRIPTION

ECLAIR (Ensemble Clustering for Lineage Analysis, Inference and Robustness) achieves a higher level of confidence in the estimated lineages through the use of approximation algorithms for consensus clustering and by combining the information from an ensemble of minimum spanning trees so as to come up with an improved, aggregated lineage tree.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • Python

:: DOWNLOAD

ECLAIR

:: MORE INFORMATION

Citation

Giecold G, Marco E, Trippa L, Yuan GC.
Robust Lineage Reconstruction from High-Dimensional Single-Cell Data.
Nucleic Acids Res. 2016 May 20. pii: gkw452.

3TierMA – Three-Tiered Meta Analysis of Gene Expression Profiles of Co-morbid Diseases

3TierMA

:: DESCRIPTION

3TierMA is a three-tiered meta-analysis approach for studying the shared genetics of co-ocurring disease conditions in patients from their gene expression profiles.

::DEVELOPER

Bonnie Berger 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R
  • Python

:: DOWNLOAD

3TierMA

:: MORE INFORMATION

Citation:

Sumaiya Nazeen, Nathan P. Palmer, Bonnie Berger* and Isaac S. Kohane.
Integrative analysis of genetic datasets reveals a shared innate immune component in autism spectrum disorder and its co-morbidities.
Genome Biology 17(1):228, 2016

cisMetalysis 1.3 – Meta Analysis of Gene Expression data sets

cisMetalysis 1.3

:: DESCRIPTION

Metalysis is meant for revealing higher level insights from multiple gene expression data sets. In particular, if you have up- and down-regulated gene sets from several different conditions and want to know what might be common to those different gene sets, you can use the Metalysis program.

cis-Metalysis” is an extension to Metalysis specifically designed to use motif target sets as annotation sets. It takes gene target predictions of the transcription factor motifs and then uses the Metalysis framework to identify meta associations between a motif and set of conditions. Because of the general consensus that condition-specific expression of a gene may be determine by combinations of transcription factors, cis-Metalysis also searches for motif combinations associated with expression.

::DEVELOPER

The Sinha Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • C++ Compiler

:: DOWNLOAD

 cisMetalysis

:: MORE INFORMATION

Citation

Proc Natl Acad Sci U S A. 2012 Jun 26;109(26):E1801-10. doi: 10.1073/pnas.1205283109.
New meta-analysis tools reveal common transcriptional regulatory basis for multiple determinants of behavior.
Ament SA, Blatti CA, Alaux C, Wheeler MM, Toth AL, Le Conte Y, Hunt GJ, Guzmán-Novoa E, Degrandi-Hoffman G, Uribe-Rubio JL, Amdam GV, Page RE Jr, Rodriguez-Zas SL, Robinson GE, Sinha S