DESeq2 1.36.0 – Differential Expression Analysis for Sequence Count Data

DESeq2 1.36.0

:: DESCRIPTION

DESeq is an R package to analyse count data from high-throughput sequencing assays such as RNA-Seq and test for differential expression.

::DEVELOPER

Huber Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/MacOsX
  • R package
  • Bioconductor

:: DOWNLOAD

 DESeq

:: MORE INFORMATION

Citation

Genome Biol. 2014;15(12):550.
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.
Love MI, Huber W, Anders S.

Genome Biol. 2010;11(10):R106. doi: 10.1186/gb-2010-11-10-r106. Epub 2010 Oct 27.
Differential expression analysis for sequence count data.
Anders S, Huber W.

Ballgown – Isoform-level Differential Expression Analysis in R

Ballgown

:: DESCRIPTION

Ballgown is a software package designed to facilitate flexible differential expression analysis of RNA-Seq data. It also provides functions to organize, visualize, and analyze the expression measurements for your transcriptome assembly.

::DEVELOPER

The Center for Computational Biology at Johns Hopkins University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Mac OsX
  • R/BioCouductor

:: DOWNLOAD

 Ballgown

:: MORE INFORMATION

Citation

Nat Biotechnol. 2015 Mar;33(3):243-6. doi: 10.1038/nbt.3172.
Ballgown bridges the gap between transcriptome assembly and expression analysis.
Frazee AC, Pertea G, Jaffe AE, Langmead B, Salzberg SL, Leek JT

TCC 1.10.0 – Differential Expression Analysis for Tag Count data with Robust Normalization Strategies

TCC 1.10.0

:: DESCRIPTION

TCC provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES).

::DEVELOPER

Jianqiang Sun <wukong at bi.a.u-tokyo.ac.jp>, Tomoaki Nishiyama <tomoakin at staff.kanazawa-u.ac.jp>, Koji Kadota

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R
  • BioConductor

:: DOWNLOAD

 TCC

:: MORE INFORMATION

Citation

TCC: an R package for comparing tag count data with robust normalization strategies.
Sun J, Nishiyama T, Shimizu K, Kadota K.
BMC Bioinformatics. 2013 Jul 9;14:219. doi: 10.1186/1471-2105-14-219.

decode 1.1 – Differential Co-Expression and Differential Expression Analysis

decode 1.1

:: DESCRIPTION

DECODE is a novel analytical approach to integrate DC and DE analyses of gene expression data.

::DEVELOPER

Thomas Lui <tlui27 at yahoo.com>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOsX
  • R

:: DOWNLOAD

 DECODE

:: MORE INFORMATION

Citation

DECODE: an integrated differential co-expression and differential expression analysis of gene expression data.
Lui TW, Tsui NB, Chan LW, Wong CS, Siu PM, Yung BY.
BMC Bioinformatics. 2015 May 31;16:182. doi: 10.1186/s12859-015-0582-4.

derfinder v0.0.42 / regionReport 0.0.1 – Fast Differential Expression Analysis of RNA-seq data at Base-pair Resolution

derfinder 0.0.42 / regionReport 0.0.1

:: DESCRIPTION

derfinder is an annotation-agnostic fast differential expression analysis software of RNA-seq data at base-pair resolution.

regionReport: Generate HTML reports for a set of regions such as those from derfinder results.

::DEVELOPER

derfinder team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • R

:: DOWNLOAD

 derfinder , regionReport

:: MORE INFORMATION

Citation

Biostatistics. 2014 Jul;15(3):413-26. doi: 10.1093/biostatistics/kxt053. Epub 2014 Jan 6.
Differential expression analysis of RNA-seq data at single-base resolution.
Frazee AC, Sabunciyan S, Hansen KD, Irizarry RA, Leek JT

MetaDE 1.0.5 – Meta-analysis for Differential Expression Analysis

MetaDE 1.0.5

:: DESCRIPTION

MetaDE (Meta-analysis for Differential Expression Analysis)

::DEVELOPER

George C. Tseng 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 MetaDE

:: MORE INFORMATION

Citation

Jia Li and George C. Tseng. (2010)
An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies.
Annals of Applied Statistics 2011, Vol. 5, No. 2A, 994-1019

Shuya Lu, Jia Li, Chi Song, Kui Shen and George C Tseng. (2010)
Biomarker Detection in the Integration of Multiple Multi-class Genomic Studies.
Bioinformatics. 26:333-340.