sydSeq 1.1.7 – Various Functions for Gene Expression Analysis

sydSeq 1.1.7

:: DESCRIPTION

sydSeq is an R package containing the functions exClust, TshrinkPlus and pMim.

::DEVELOPER

Ellis Patrick

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOS
  • R

:: DOWNLOAD

 sydSeq

:: MORE INFORMATION

Citation

Inferring data-specific micro-RNA function through the joint ranking of micro-RNA and pathways from matched micro-RNA and gene expression data.
Patrick E, Buckley M, Müller S, Lin DM, Yang JY.
Bioinformatics. 2015 Sep 1;31(17):2822-8. doi: 10.1093/bioinformatics/btv220.

Estimation of data-specific constitutive exons with RNA-Seq data.
Patrick E, Buckley M, Yang YH.
BMC Bioinformatics. 2013 Jan 29;14:31

BRB-ArrayTools 4.6.2 – Visualization & Analysis of DNA Microarray Gene Expression Data

BRB-ArrayTools 4.6.2

:: DESCRIPTION

BRB-ArrayTools is an integrated software package for the analysis of DNA microarray data.

BRB-ArrayTools contains utilities for processing expression data from multiple experiments, visualization of data, multidimensional scaling, clustering of genes and samples, and classification and prediction of samples. BRB-ArrayTools features drill-down linkage to NCBI databases using clone, GenBank, or UniGene identifiers, and drill-down linkage to the NetAffx database using Probeset ids.

::DEVELOPER

the Biometric Research Branch of the Division of Cancer Treatment & Diagnosis of the National Cancer Institute

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

BRB-ArrayTools

:: MORE INFORMATION

Citation

Analysis of gene expression data using BRB-ArrayTools.
Simon R, Lam A, Li MC, Ngan M, Menenzes S, Zhao Y.
Cancer Inform. 2007 Feb 4;3:11-7.

CLUSTERnGO v0.31 – Gene Expression Analysis Tool

CLUSTERnGO v0.31

:: DESCRIPTION

CnG (CLUSTERnGO) is a gene expression analysis tool that clusters gene (or protein) expression profiles, and extracts their GO enrichments.

::DEVELOPER

CnG team

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • QT

:: DOWNLOAD

 CnG

:: MORE INFORMATION

Citation

CLUSTERnGO: a user-defined modelling platform for two-stage clustering of time-series data.
Fidaner IB, Cankorur-Cetinkaya A, Dikicioglu D, Kirdar B, Cemgil AT, Oliver SG.
Bioinformatics. 2015 Sep 26. pii: btv532.

ExAtlas – Meta-analysis of Gene Expression data

ExAtlas

:: DESCRIPTION

ExAtlas is an on-line software tool for meta-analysis and visualization of gene expression data.

::DEVELOPER

Laboratory of Genetics, National Institute on Aging,  NIH

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

ExAtlas: An interactive online tool for meta-analysis of gene expression data.
Sharov AA, Schlessinger D, Ko MS.
J Bioinform Comput Biol. 2015 Jun 9:1550019.

GeneProgram 0.1 – Discovery of Functional Generality of Gene Expression programs

GeneProgram 0.1

:: DESCRIPTION

GeneProgram is a new unsupervised computational framework based on Hierarchical Dirichlet Processes that addresses each of the above challenges. GeneProgram uses expression data to simultaneously organize tissues into groups and genes into overlapping programs with consistent temporal behavior, to produce maps of expression programs, which are sorted by generality scores that exploit the automatically learned groupings.

::DEVELOPER

the Gifford Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/MacOsX
  • Java

:: DOWNLOAD

  GeneProgram

:: MORE INFORMATION

Citation

PLoS Comput Biol. 2007 Aug;3(8):e148. Epub 2007 Jun 13.
Automated discovery of functional generality of human gene expression programs.
Gerber GK, Dowell RD, Jaakkola TS, Gifford DK.

MetScape 3.1.3 – Analysis and Visualization of Metabolomics and Gene Expression data

MetScape 3.1.3

:: DESCRIPTION

The MetScape Plugin for Cytoscape provides a bioinformatics framework for the visualization and interpretation of metabolomic and expression profiling data in the context of human metabolism.

::DEVELOPER

MetScape team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/MacOsX
  • Java
  • Cytoscape

:: DOWNLOAD

 MetScape

:: MORE INFORMATION

Citation

Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data.
Karnovsky A, Weymouth T, Hull T, Tarcea VG, Scardoni G, Laudanna C, Sartor MA, Stringer KA, Jagadish HV, Burant C, Athey B, Omenn GS.
Bioinformatics. 2012 Feb 1;28(3):373-80. doi: 10.1093/bioinformatics/btr661.

SIGNATURE 20111025 – Gene Expression Signature Analysis

SIGNATURE 20111025

:: DESCRIPTION

SIGNATURE is a platform for gene expression signature analysis.

::DEVELOPER

the Microarray Core Facility at microarray@duke.edu.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOSX
  • Java

:: DOWNLOAD

 SIGNATURE

:: MORE INFORMATION

Citation

Chang JT, Gatza ML, Lucas JE, Barry W, Vaughn P, and Nevins JR (2011).
SIGNATURE: A Workbench for Gene Expression Signature Analysis.”
BMC Bioinformatics 12:443.

GenClust 2.0 – Clustering Gene Expression data

GenClust 2.0

:: DESCRIPTION

GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, compact and easy to update; (b) it can be used naturally in conjunction with data driven internal validation methods.

::DEVELOPER

Lo Bosco Giosuè , Raffaele Giancarlo

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOSX / Windows

:: DOWNLOAD

 GenClust

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2005 Dec 7;6:289.
GenClust: a genetic algorithm for clustering gene expression data.
Di Gesú V1, Giancarlo R, Lo Bosco G, Raimondi A, Scaturro D.

HyperPrior – Classify Gene Expression and ArrayCGH data with Prior knowledge

HyperPrior

:: DESCRIPTION

HyperPrior is a hypergraph-based semi-supervised learning algorithm to classify gene expression and arrayCGH data using biological knowledge as constraints on graph-based learning. HyperPrior is a robust two-step iterative method that alternatively finds the optimal labeling of the samples and the optimal weighting of the features, guided by constraints encoding prior knowledge. The prior knowledge for analyzing gene expression data is that cancer-related genes tend to interact with each other in a protein-protein interaction network. Similarly, the prior knowledge for analyzing arrayCGH data is that probes that are spatially nearby in their layout along the chromosomes tend to be involved in the same amplification or deletion event. Based on the prior knowledge, HyperPrior imposes a consistent weighting of the correlated genomic features in graph-based learning.

::DEVELOPER

Kuang Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / WIndows / MacOsX
  • Matlab

:: DOWNLOAD

  HyperPrior

:: MORE INFORMATION

Citation

Bioinformatics. 2009 Nov 1;25(21):2831-8. doi: 10.1093/bioinformatics/btp467. Epub 2009 Jul 30.
A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge.
Tian Z, Hwang T, Kuang R.

GeneExpressionSignature 1.38.0 – Gene Expression Signature based Similarity Metric

GeneExpressionSignature 1.38.0

:: DESCRIPTION

GeneExpressionSignature gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest.

::DEVELOPER

Yang Cao <yiluheihei at gmail.com>, Fei Li <pittacus at gmail.com>,Lu Han <hanl8910 at gmail.com>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /Windows/ MacOsX
  • R
  • BioConductor

:: DOWNLOAD

 GeneExpressionSignature

:: MORE INFORMATION

Citation

OMICS. 2013 Feb;17(2):116-8. doi: 10.1089/omi.2012.0087.
GeneExpressionSignature: an R package for discovering functional connections using gene expression signatures.
Li F1, Cao Y, Han L, Cui X, Xie D, Wang S, Bo X.