GFold 1.1.4 – Generalized fold change for Rank Differentially Expressed Genes from RNA-seq data

GFold 1.1.4

:: DESCRIPTION

gfold (Generalized fold change) generalizes the fold change by considering the posterior distribution of log fold change, such that each gene is assigned a reliable fold change. It overcomes the shortcoming of p-value that measures the significance of whether a gene is differentially expressed under different conditions instead of measuring relative expression changes, which are more interesting in many studies. It also overcomes the shortcoming of fold change that suffers from the fact that the fold change of genes with low read count are not so reliable as that of genes with high read count, even these two genes show the same fold change.

::DEVELOPER

X. Shirley Liu Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 GFold

:: MORE INFORMATION

Citation

Feng J, Meyer CA, Wang Q, Liu JS, Liu XS, Zhang Y.
GFOLD: a generalized fold change for ranking differentially ex-pressed genes from RNA-seq data.
Bioinformatics (2012) 28 (21): 2782-2788.

DEGseq 1.38.0 – Differentially Expressed Gene Identification for RNA-seq data

DEGseq 1.38.0

:: DESCRIPTION

DEGseq is an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples

::DEVELOPER

DEGseq team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • R package
  • Bioconductor

:: DOWNLOAD

  DEGseq

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Jan 1;26(1):136-8. doi: 10.1093/bioinformatics/btp612. Epub 2009 Oct 24.
DEGseq: an R package for identifying differentially expressed genes from RNA-seq data.
Wang L, Feng Z, Wang X, Wang X, Zhang X.

baySeq 2.6.0 – Identify Differential Expressed Genes

baySeq 2.6.0

:: DESCRIPTION

baySeq identifies differential expression in high-throughput ‘count’ data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods.

::DEVELOPER

Thomas J. Hardcastle

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 baySeq

:: MORE INFORMATION

Citation

Bioinformatics. 2015 Oct 1. pii: btv569.
Generalised empirical Bayesian methods for discovery of differential data in high-throughput biology.
Hardcastle TJ

BMC Bioinformatics. 2010 Aug 10;11:422. doi: 10.1186/1471-2105-11-422.
baySeq: empirical Bayesian methods for identifying differential expression in sequence count data.
Hardcastle TJ, Kelly KA.

RNASeqGUI 1.0.0 – GUI for the Identification of Differentially Expressed Genes

RNASeqGUI 1.0.0

:: DESCRIPTION

RNASeqGUI R package is a graphical user interface for the identification of differentially expressed genes from RNA-Seq experiments.

::DEVELOPER

Computational & Biology Open laboratory

:: SCREENSHOTS

RNASeqGUI

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • R package

:: DOWNLOAD

  RNASeqGUI

:: MORE INFORMATION

Citation:

Bioinformatics. 2014 May 7. [Epub ahead of print]
RNASeqGUI: A GUI for analysing RNA-Seq data.
Russo F1, Angelini C.

OpWise – Operons Aid the Identification of differentially Expressed Genes in Bacterial Microarray Experiments

OpWise

:: DESCRIPTION

To estimate the reliability of bacterial microarray experiments, OpWise uses the agreement of measurements within operons to estimate the amount of systematic bias in the data. OpWise relies on the MicrobesOnline operons predictions.

::DEVELOPER

Morgan N. PriceAdam P. Arkin, and Eric J. Alm

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • R package

:: DOWNLOAD

 OpWise

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2006 Jan 13;7:19.
OpWise: operons aid the identification of differentially expressed genes in bacterial microarray experiments.
Price MN, Arkin AP, Alm EJ.

IBMT – Testing for Differentially Expressed Genes in Microarrays

IBMT

:: DESCRIPTION

IBMT is a Bayesian hierarchical normal model to define a novel Intensity-Based Moderated T-statistic.The method is completely data-dependent using empirical Bayes philosophy to estimate hyperparameters, and thus does not require specification of any free parameters. IBMT has the strength of balancing two important factors in the analysis of microarray data: the degree of independence of variances relative to the degree of identity (i.e. t-tests vs. equal variance assumption), and the relationship between variance and signal intensity. When this variance-intensity relationship is weak or does not exist, IBMT reduces to a previously described moderated t-statistic. Furthermore, our method may be directly applied to any array platform and experimental design. Together, these properties show IBMT to be a valuable option in the analysis of virtually any microarray experiment.

:: DEVELOPER

Laboratory for Statistical Genomics, Univ. Cincinnati

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX/Windows
  • R Package

:: DOWNLOAD

 IBMT

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2006 Dec 19;7:538.
Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments.
Sartor MA, Tomlinson CR, Wesselkamper SC, Sivaganesan S, Leikauf GD, Medvedovic M.

EVE v04 – Detect Differentially Expressed Genes using Microarray Data

EVE v04

:: DESCRIPTION

EVE (External Variance Estimation) is an software to detect differentially expressed genes using microarray data.You can download an R-script that works for data from the Affymetrix ATH1 GeneChip® microarray.

::DEVELOPER

Gruissem laboratory at the Institute of Plant Sciences

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

EVE

:: MORE INFORMATION

Citation

Wille, A., Gruissem, W., Bühlmann, P., Hennig, L. (2007)
EVE – External Variance Estimation increases statistical power for detecting differentially expressed genes.
Plant J 52, 561-569