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http://bios.unc.edu/~weisun/software/asSeq.htm

Software that establishes a statistical framework for future developments of eQTL (expression quantitative trait locus) mapping methods using RNA-seq data (e.g., linkage-based eQTL mapping), and the joint study of multiple genetic markers and/or multiple genes. This R package has been submitted to R/bioconductor. It will be available on bioconductor soon. It is recommended to install this R package from bioconductor. You can also install this R package from the source code by yourself. Since the R package contains C code, a C complier is required for installation. With both R and appropriate c complier installed, this R package can be installed using the following command (in Mac Terminal window or Windows command window) R CMD INSTALL asSeq

Proper citation: asSeq (RRID:SCR_001625) Copy   

•   Source: SciCrunch Registry

http://crcview.hegroup.org/

Web-based microarray data analysis and visualization system powered by CRC, or Chinese Restaurant cluster, a Dirichlet process model-based clustering algorithm recently developed by Dr. Steve Qin. It also incorporates several gene expression analysis programs from Bioconductor, including GOStats, genefilter, and Heatplus. CRCView also installs from the Bioconductor system 78 annotation libraries of microarray chips for human (31), mouse (24), rat (14), zebrafish (1), chicken (1), Drosophila (3), Arabidopsis (2), Caenorhabditis elegans (1), and Xenopus Laevis (1). CRCView allows flexible input data format, automated model-based CRC clustering analysis, rich graphical illustration, and integrated Gene Ontology (GO)-based gene enrichment for efficient annotation and interpretation of clustering results. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. You need to register for an account in the system to store your data and analyses. The data and results can be visited again anytime you log in.

Proper citation: CRCView (RRID:SCR_007092) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 18,2025. Software tools for meta-analysis in the presence of hierarchical (and/or sampling) dependence, including with gene expression studies.

Proper citation: metahdep (RRID:SCR_001225) Copy   

•   Source: SciCrunch Registry

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

A software package for meta-analysis of high-throughput experiments using feature annotations. It calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested).

Proper citation: categoryCompare (RRID:SCR_001223) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 18,2025. R extension whose functions are intended for cross-study comparison of gene expression array data. Required from the user is gene expression matrices, their corresponding gene-id vectors and other useful information, and they could be "list", "matrix", or "ExpressionSet". The main function is "mergeExprs" which transforms the input objects into data in the merged format, such that common genes in different datasets can be easily found. And the function "intcor" calculate the correlation coefficients. Other functions use the output from "modelOutcome" to graphically display the results and cross-validate associations of gene expression data with survival.

Proper citation: MergeMaid (RRID:SCR_001221) Copy   

•   Source: SciCrunch Registry

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

Software for adaptive Gene Picking for Microarray Expression Data Analysis.

Proper citation: pickgene (RRID:SCR_001331) Copy   

•   Source: SciCrunch Registry

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

A regression based software package to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray experiments.

Proper citation: maSigPro (RRID:SCR_001349) Copy   

•   Source: SciCrunch Registry

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

Software to identify low-quality data using metrics developed for expression data derived from Formalin-Fixed, Paraffin-Embedded (FFPE) data. Also a function for making Concordance at the Top plots (CAT-plots).

Proper citation: ffpe (RRID:SCR_001307) Copy   

•   Source: SciCrunch Registry

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

Software package that uses signal-to-noise ratios (SNRs) as a proxy for quality of gene expression studies and samples. The SNRs can be calculated on any gene expression data set as long as gene IDs are available, no access to the raw data files is necessary. This allows to flag problematic studies and samples in any public data set.

Proper citation: SNAGEE (RRID:SCR_001301) Copy   

•   Source: SciCrunch Registry

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

Software that infers biological processes which are active in individual gene sets from corresponding microarray measurements. It achieves this inference by combining a MCMC matrix decomposition algorithm (GAPS) with a novel statistic inferring activity on gene sets.

Proper citation: CoGAPS (RRID:SCR_001479) Copy   

•   Source: SciCrunch Registry

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

Software package for low-throughput real-time quantitative PCR data analysis. The package allows you to import easily qPCR data files. Thereafter, you can calculate amplification efficiencies, relative quantities and their standard errors, normalization factors based on the best reference genes choosen (using the SLqPCR package), and then the normalized relative quantities, the NRQs scaled to your control and their standard errors.

Proper citation: EasyqpcR (RRID:SCR_003406) Copy   

•   Source: SciCrunch Registry

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

Software package providing functions for the selection of optimal reference genes and the normalization of real-time quantitative PCR data.

Proper citation: NormqPCR (RRID:SCR_003388) Copy   

•   Source: SciCrunch Registry

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

Software package that helps with the assessment and correction of RNA degradation effects in Affymetrix 3' expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation.

Proper citation: AffyRNADegradation (RRID:SCR_000118) Copy   

•   Source: SciCrunch Registry

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

Software package to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq.

Proper citation: TransView (RRID:SCR_000358) Copy   

•   Source: SciCrunch Registry

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

Software for integrated Bayesian Modeling of eQTL data. It implements a joint hierarchical Bayesian model where all genes and SNPs are modeled concurrently.

Proper citation: iBMQ (RRID:SCR_000481) Copy   

•   Source: SciCrunch Registry

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

An R package developed for the large-scale analysis of gene expression signatures. It 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.

Proper citation: GeneExpressionSignature (RRID:SCR_000455) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/2.11/bioc/html/easyRNASeq.html

Software that calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as ''RPKM'' or by the ''DESeq'' or ''edgeR'' package.

Proper citation: easyRNASeq (RRID:SCR_012020) Copy   

•   Source: SciCrunch Registry

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

A software package that provides functions to read raw RT-qPCR data of different platforms.

Proper citation: ReadqPCR (RRID:SCR_000030) Copy   

•   Source: SciCrunch Registry

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

Software application for discovering direct or indirect targets of transcription factors (TFs) using ChIP-chip or ChIP-seq, and microarray or RNA-seq gene expression data. Inputting a list of genes of potential targets of one TF from ChIP-chip or ChIP-seq, and the gene expression results, it generates a regulatory network of the TF.

Proper citation: GeneNetworkBuilder (RRID:SCR_006455) Copy   

•   Source: SciCrunch Registry

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

Software package that provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene''s differential expression and splicing, respectively.

Proper citation: SeqGSEA (RRID:SCR_005724) Copy   

•   Source: SciCrunch Registry