ChAMP 2.16.1 – Chip Analysis Methylation Pipeline for Illumina HumanMethylation450

ChAMP 2.16.1

:: DESCRIPTION

ChAMP includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number aberrations.

::DEVELOPER

Teschendorff Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R package
  • BioConductor

:: DOWNLOAD

  ChAMP

:: MORE INFORMATION

Citation

ChAMP: 450k Chip Analysis Methylation Pipeline.
Morris TJ, Butcher LM, Feber A, Teschendorff AE, Chakravarthy AR, Wojdacz TK, Beck S.
Bioinformatics. 2014 Feb 1;30(3):428-30. doi: 10.1093/bioinformatics/btt684.

Genome Biol. 2014 Feb 3;15(2):R30. [Epub ahead of print]
Using high-density DNA methylation arrays to profile copy number alterations.
Feber A, Guilhamon P, Lechner M, Fenton T, Wilson GA, Thirlwell C, Morris TJ, Flanagan AM, Teschendorff AE, Kelly JD, Beck S.

BMIQ 1.4 – Beta Mixture Quantile Model

BMIQ 1.4

:: DESCRIPTION

BMIQ (Beta Mixture Quantile) is a method for normalisation of Illumina Infinium 450k data

::DEVELOPER

Teschendorff Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R package

:: DOWNLOAD

 BMIQ 

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Jan 15;29(2):189-96. doi: 10.1093/bioinformatics/bts680. Epub 2012 Nov 21.
A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data.
Teschendorff AE1, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, Beck S.

QuSAGE 2.20.0 – Quantitative Set Analysis for Gene Expression

QuSAGE 2.20.0

:: DESCRIPTION

QuSAGE is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies.

::DEVELOPER

Kleinstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • R
  • BioConductor

:: DOWNLOAD

 QuSAGE

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2013 Oct;41(18):e170. doi: 10.1093/nar/gkt660. Epub 2013 Aug 5.
Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations.
Yaari G1, Bolen CR, Thakar J, Kleinstein SH.

BAGEL 4.1.1 – Bayesian Analysis of Gene Expression Levels

BAGEL 4.1.1

:: DESCRIPTION

Bayesian Analysis of Gene Expression Levels (BAGEL) is a program that allows statistical inferences to be made regarding differential gene expression between two or more samples measured on spotted (two-channel) microarrays. BAGEL makes these inferences from normalized ratio data, on a gene-by-gene basis. The advantages of BAGEL include ease of use, straightforward interpretation of results, statistical robustness, flexibility in accepting different experimental designs, and that it is free.

::DEVELOPER

the Townsend Lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / Mac OsX

:: DOWNLOAD

BAGEL

:: MORE INFORMATION

Please cite:

Townsend, J.P., and D.L. Hartl. 2002.
Bayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple strains or treatments.
Genome Biology 3 (12): research0071.1-0071.16.

LOX 1.8beta – Inferring Level of Expression from Diverse Methods of Census Sequencing

LOX 1.8beta

:: DESCRIPTION

LOX (Level Of eXpression) is a program that employs Markov Chain Monte Carlo to estimate level of expression from census sequencing data sets with multiple treatments or samples.

::DEVELOPER

the Townsend Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • C++Compiler

:: DOWNLOAD

 LOX

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Aug 1;26(15):1918-9. Epub 2010 Jun 10.
LOX: inferring Level Of eXpression from diverse methods of census sequencing.
Zhang Z, López-Giráldez F, Townsend JP.

ASTRO – Analysis of Short Time-series using Rank Order preservation

ASTRO

:: DESCRIPTION

ASTRO is a web server for analyzing and visualizing short time-series gene expression data

::DEVELOPER

Benos Lab

 SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2009 Aug 20;10:255. doi: 10.1186/1471-2105-10-255.
Extracting biologically significant patterns from short time series gene expression data.
Tchagang AB, Bui KV, McGinnis T, Benos PV.

fRMA 1.39.0 / frmaTools 1.39.0 – Single Microarray Preprocessing and Analysis

fRMA 1.39.0 / frmaTools 1.39.0

:: DESCRIPTION

fRMA (Frozen robust multiarray analysis) is a single-array preprocessing algorithm that retains the advantages of multiarray algorithms and removes certain batch effects by downweighting probes that have high between-batch residual variance.

frmaTools: Extension and customization of the frma package.

::DEVELOPER

Matthew N. McCall

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOsX / Linux
  • R package
  • Bioconductor

:: DOWNLOAD

 fRMA / frmaTools

:: MORE INFORMATION

Citation

Thawing Frozen Robust Multi-array Analysis (fRMA).
McCall MN, Irizarry RA.
BMC Bioinformatics. 2011 Sep 16;12:369. doi: 10.1186/1471-2105-12-369.

spkTools 1.43.0 – Collection of Functions to Analyze Microarray Spike-in data

spkTools 1.43.0

:: DESCRIPTION

spkTools contains functions that can be used to compare expression measures on different array platforms.

::DEVELOPER

Matthew N. McCall

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOsX / Linux
  • R package
  • Bioconductor

:: DOWNLOAD

 spkTools

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2008 Oct;36(17):e108. doi: 10.1093/nar/gkn430. Epub 2008 Aug 1.
Consolidated strategy for the analysis of microarray spike-in data.
McCall MN, Irizarry RA.

SAM 4.01 – Significance Analysis of Microarrays

SAM 4.01

:: DESCRIPTION

SAM (Significance Analysis of Microarrays) is a statistical technique for finding significant genes in a set of microarray experiments, a supervised learning software for genomic expression data mining.

The input to SAM is gene expression measurements from a set of microarray experiments, as well as a response variable from each experiment. The response variable may be a grouping like untreated, treated [either unpaired or paired], a multiclass grouping (like breast cancer, lymphoma, colon cancer, . . . ), a quantitative variable (like blood pressure) or a possibly censored survival time.

::DEVELOPER

Stanford University Statistics and Biochemistry Labs

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

 SAM

:: MORE INFORMATION

Citation

Jun Li and Robert Tibshirani (2011)
Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data.
Stat Methods Med Res. 2011 Nov 28.

RobiNA 1.2.4 – Open Source Microarray and RNA-Seq Processing

RobiNA 1.2.4

:: DESCRIPTION

RobiNA is an integrated solution that consolidates all steps of RNA-Seq-based differential gene-expression analysis in one user-friendly cross-platform application featuring a rich graphical user interface. RobiNA accepts raw FastQ files, SAM/BAM alignment files and counts tables as input. It supports quality checking, flexible filtering and statistical analysis of differential gene expression based on state-of-the art biostatistical methods developed in the R/Bioconductor projects. In-line help and a step-by-step manual guide users through the analysis.

::DEVELOPER

Max Planck Institute for Molecular Plant Physiology

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java

:: DOWNLOAD

 RobiNA

:: MORE INFORMATION

Citation

Lohse M, Bolger AM, Nagel A, Fernie AR, Lunn JE, Stitt M, Usadel B. (2012)
RobiNA: A user-friendly, integrated software solution for RNA-Seq-based transcriptomics.
Nucleic Acids Res. 2012 Jul;40(Web Server issue):W622-7.