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http://bowtie-bio.sourceforge.net/myrna/index.shtml

A cloud computing tool for calculating differential gene expression in large RNA-seq datasets. It uses Bowtie for short read alignment and R/Bioconductor for interval calculations, normalization, and statistical testing. These tools are combined in an automatic, parallel pipeline that runs in the cloud (Elastic MapReduce in this case) on a local Hadoop cluster, or on a single computer, exploiting multiple computers and CPUs wherever possible.

Proper citation: Myrna (RRID:SCR_006951) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/src/contrib/Archive/iFad/

An R software package implementing a bayesian sparse factor model for the joint analysis of paired datasets, the gene expression and drug sensitivity profiles, measured across the same panel of samples, e.g. cell lines.

Proper citation: iFad (RRID:SCR_000271) Copy   

•   Source: SciCrunch Registry

http://mfuzz.sysbiolab.eu/

Software package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface).

Proper citation: Mfuzz (RRID:SCR_000523) Copy   

•   Source: SciCrunch Registry

http://campagnelab.org/software/gobyweb/

Web application that facilitates the management and analysis of high-throughput sequencing (HTS) data. In the back-end, it uses the Goby framework, BWA, STAR, Last, GSNAP, Samtools, VCF-tools, along with a cluster of servers to provide rapid alignment and efficient analyses. GobyWeb makes it possible to analyze hundreds of samples in consistent ways without having to use command line tools. GobyWeb provides tools that streamline frequent data analyses for RNA-Seq, Methyl-Seq, RRBS, or DNA-Seq datasets and to enable teams of investigators to share reads and results of analyses. GobyWeb can be extended for new analyses by developing plugins.

Proper citation: GobyWeb (RRID:SCR_005443) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/netclassr/

An R package for network-based feature (gene) selection for biomarkers discovery via integrating biological information. The package adapts the following 5 algorithms for classifying and predicting gene expression data using prior knowledge: # average gene expression of pathway (aep); # pathway activities classification (PAC); # Hub network classification (hubc); # filter via top ranked genes (FrSVM); # network smoothed t-statistic (stSVM).

Proper citation: netClass (RRID:SCR_005672) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/devel/bioc/html/ChIPXpress.html

A R package designed to improve ChIP-seq and ChIP-chip target gene ranking using publicly available gene expression data. It takes as input predicted transcription factor (TF) bound genes from ChIPx data and uses a corresponding database of gene expression profiles downloaded from NCBI GEO to rank the TF bound targets in order of which gene is most likely to be functional TF target.

Proper citation: ChIPXpress (RRID:SCR_006653) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/pathview.html

A tool set for pathway-based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis.

Proper citation: Pathview (RRID:SCR_002732) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/sasqpcr/

All-in-one computer program for robust and rapid analysis of quantitative reverse transcription real-time polymerase chain reaction (RT-qPCR) data in SAS. It incorporates all functions important for RT-qPCR data analysis including assessment of PCR efficiencies, validation of internal reference genes and normalizers, normalization of confounding variations across samples and statistical comparisons of target gene expression in parallel samples. The program is highly automatic in data analyses and result output. The input data have no limitations for the number of genes or cDNA samples. Users can simply change the macro variables to test various analytical strategies, optimize results and customize the analytical processes. The program is also extendable allowing advanced SAS users to develop particular statistical tests appropriate for their experimental designs. Thus users are the actual decision-makers controlling RT-qPCR data analyses. The program has to be used in SAS software; however, extensive SAS programming knowledge is not required.

Proper citation: SASqPCR (RRID:SCR_003056) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/survcomp.html

R package providing functions to assess and to compare the performance of risk prediction (survival) models.

Proper citation: SurvComp (RRID:SCR_003054) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/ddCt.html

Software package providing an approximation method to determine relative gene expression with quantitative real-time PCR (qRT-PCR) experiments. It requires no standard curve for each primer-target pair, therefore reducing the working load and yet returning accurate enough results as long as the assumptions of the amplification efficiency hold. The package implements a pipeline to collect, analyze and visualize qRT-PCR results, for example those from TaqMan SDM software, mainly using the ddCt method. The pipeline can be either invoked by a script in command-line or through the API consisting of S4-Classes, methods and functions.

Proper citation: ddCt (RRID:SCR_003396) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/unifiedWMWqPCR.html

Software package that implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data.

Proper citation: unifiedWMWqPCR (RRID:SCR_001706) Copy   

•   Source: SciCrunch Registry

http://blog.expressionplot.com/

Software package consisting of a default back end, which prepares raw sequencing or Affymetrix microarray data, and a web-based front end, which offers a biologically centered interface to browse, visualize, and compare different data sets.

Proper citation: ExpressionPlot (RRID:SCR_001904) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/devel/bioc/html/massiR.html

Software that predicts the sex of samples in gene expression microarray datasets.

Proper citation: massiR (RRID:SCR_001157) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/yaqcaffy.html

Software package for quality control of Affymetrix GeneChip expression data and reproducibility analysis of human whole genome chips with the MAQC reference datasets.

Proper citation: yaqcaffy (RRID:SCR_001295) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/RCASPAR.html

Software package for survival time prediction based on a piecewise baseline hazard Cox regression model. It is meant to help predict survival times in the presence of high-dimensional explanatory covariates.

Proper citation: RCASPAR (RRID:SCR_001253) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/sRAP.html

Software package that provides a pipeline for gene expression analysis (primarily for RNA-Seq data). The normalization function is specific for RNA-Seq analysis, but all other functions (Quality Control Figures, Differential Expression and Visualization, and Functional Enrichment via BD-Func) will work with any type of gene expression data.

Proper citation: sRAP (RRID:SCR_001297) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/snm.html

Software package that uses a modeling strategy especially designed for normalizing high-throughput genomic data. The premise is that your data is a function of study-specific variables which are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest.

Proper citation: SNM (RRID:SCR_001299) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/waveTiling.html

Software package to conduct transcriptome analysis for tiling arrays based on fast wavelet-based functional models.

Proper citation: waveTiling (RRID:SCR_001322) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/AffyExpress.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Software package for quality assessment and to identify differentially expressed genes in the Affymetrix gene expression data.

Proper citation: AffyExpress (RRID:SCR_001321) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/dexus.html

Software package that identifies differentially expressed genes in RNA-Seq data under all possible study designs such as studies without replicates, without sample groups, and with unknown conditions. It works also for known conditions, for example for RNA-Seq data with two or multiple conditions. RNA-Seq read count data can be provided both by the S4 class Count Data Set and by read count matrices. Differentially expressed transcripts can be visualized by heatmaps, in which unknown conditions, replicates, and samples groups are also indicated. This software is fast since the core algorithm is written in C. For very large data sets, a parallel version of DEXUS is provided in this package. DEXUS is a statistical model that is selected in a Bayesian framework by an EM algorithm. It does not need replicates to detect differentially expressed transcripts, since the replicates (or conditions) are estimated by the EM method for each transcript. The method provides an informative/non-informative value to extract differentially expressed transcripts at a desired significance level or power.

Proper citation: DEXUS (RRID:SCR_001309) Copy   

•   Source: SciCrunch Registry