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http://artiva.gene-networks.net/artiva-2/

Algorithm available in a R package that is a statistical framework to infer time-varying structures of gene-regulation networks.

Proper citation: ARTIVA (RRID:SCR_011946) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/adtex/

A software tool for copy number variation (CNV) detection for whole-exome data from paired tumour/matched normal samples.

Proper citation: ADTEx (RRID:SCR_012059) Copy   

•   Source: SciCrunch Registry

http://www.few.vu.nl/~mavdwiel/ShrinkBayes.html

Software for detecting differential features across the entire spectrum, including the lower counts.

Proper citation: ShrinkSeq (RRID:SCR_012022) Copy   

•   Source: SciCrunch Registry

http://www.stat.purdue.edu/~doerge/software/TSPM.R

Software using a statistical approach, based on a two-stage Poisson model, for modeling RNA sequencing data and testing for biologically important changes in gene expression.

Proper citation: TSPM.R (RRID:SCR_012021) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/edgeR/

Bioconductor software package for Empirical analysis of Digital Gene Expression data in R. Used for differential expression analysis of RNA-seq and digital gene expression data with biological replication.

Proper citation: edgeR (RRID:SCR_012802) Copy   

•   Source: SciCrunch Registry

https://github.com/alyssafrazee/polyester

An R package designed to simulate RNA sequencing experiments with differential transcript expression. Given a set of annotated transcripts, it will simulate the steps of an RNA-seq experiment (fragmentation, reverse-complementing, and sequencing) and produce files containing simulated RNA-seq reads. Simulated reads can be analyzed using a choice of downstream analysis tools. Polyester has a built-in wrapper function to simulate a case/control experiment with differential transcript expression and biological replicates. Users are able to set the levels of differential expression at transcripts of their choosing. This means they know which transcripts are differentially expressed in the simulated dataset, so accuracy of statistical methods for differential expression detection can be analyzed. Polyester offers several unique features: * Built-in functionality to simulate differential expression at the transcript level * Ability to explicitly set differential expression signal strength * Simulation of small datasets, since large RNA-seq datasets can require lots of time and computing resources to analyze * Generation of raw RNA-seq reads, as opposed to alignments or transcript-level abundance estimates * Transparency/open-source code

Proper citation: Polyester (RRID:SCR_003602) Copy   

•   Source: SciCrunch Registry

http://www.stat.purdue.edu/~cheng70/code.html

A classification software for DNA methylation profiling with bisulphite next-generation sequencing data.

Proper citation: MSC (RRID:SCR_005450) Copy   

•   Source: SciCrunch Registry

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

Software for identifying differentially methylated regions between unique samples using array based methylation profiles. It allows researchers to compare n greater than or equal to 2 unique samples with regard to their methylation profile. The (pairwise) comparison of n unique single samples distinguishesit from other existing pipelines as these often compare groups of samples in either single CpG locus or region based analysis. DMRforPairs defines regions of interest as genomic ranges with sufficient probes located in close proximity to each other. Probes in one region are optionally annotated to the same functional class(es). Differential methylation is evaluated by comparing the methylation values within each region between individual samples and (if the difference is sufficiently large), testing this difference formally for statistical significance.

Proper citation: DMRforPairs (RRID:SCR_005702) Copy   

•   Source: SciCrunch Registry

http://pages.stat.wisc.edu/~yandell/qtl/software/qtlbim/

Software library for QTL Bayesian Interval Mapping that provides a Bayesian model selection approach to map multiple interacting QTL. It works on experimentally inbred lines and performs a genome-wide search to locate multiple potential QTL. The package can handle continuous, binary and ordinal traits. (entry from Genetic Analysis Software)

Proper citation: R/QTLBIM (RRID:SCR_009375) Copy   

•   Source: SciCrunch Registry

http://www.arrayanalysis.org/

Tools for microarray quality control and pre-processing.

Proper citation: ArrayAnalysis.org (RRID:SCR_010932) Copy   

•   Source: SciCrunch Registry

http://www.dkfz.de/en/epidemiologie-krebserkrankungen/software/software.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 24,2023. Software program that performs estimation of power and sample sizes required to detect genetic and environmental main, as well as gene-environment interaction (GxE) effects in indirect matched case-control studies (1:1 matching). When the hypothesis of GxE is tested, power/sample size will be estimated for the detection of GxE, as well as for the detection of genetic and environmental marginal effects. Furthermore, power estimation is implemented for the joint test of genetic marginal and GxE effects (Kraft P et al., 2007). Power and sample size estimations are based on Gauderman''s (2002) asymptotic approach for power and sample size estimations in direct studies of GxE. Hardy-Weinberg equilibrium and independence of genotypes and environmental exposures in the population are assumed. The estimates are based on genotypic codes (G=1 (G=0) for individuals who carry a (non-) risk genotype), which depend on the mode of inheritance (dominant, recessive, or multiplicative). A conditional logistic regression approach is used, which employs a likelihood-ratio test with respect to a biallelic candidate SNP, a binary environmental factor (E=1 (E=0) in (un)exposed individuals), and the interaction between these components. (entry from Genetic Analysis Software)

Proper citation: PIAGE (RRID:SCR_013124) Copy   

•   Source: SciCrunch Registry

http://folk.uio.no/thoree/FEST/

An R package for simulations and likelihood calculations of pair-wise family relationships using DNA marker data. (entry from Genetic Analysis Software)

Proper citation: R/FEST (RRID:SCR_013347) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/web/packages/MetaQC/

Software for quality control and diagnosis for microarray meta-analysis. Quantitative quality control measures include: (1) internal homogeneity of co-expression structure among studies (internal quality control; IQC); (2) external consistency of co-expression structure correlating with pathway database (external quality control; EQC); (3) accuracy of differentially expressed gene detection (accuracy quality control; AQCg) or pathway identification (AQCp); (4) consistency of differential expression ranking in genes (consistency quality control; CQCg) or pathways (CQCp). For each quality control index, the p-values from statistical hypothesis testing are minus log transformed and PCA biplots were applied to assist visualization and decision. Results generate systematic suggestions to exclude problematic studies in microarray meta-analysis and potentially can be extended to GWAS or other types of genomic meta-analysis. The identified problematic studies can be scrutinized to identify technical and biological causes (e.g. sample size, platform, tissue collection, preprocessing etc) of their bad quality or irreproducibility for final inclusion / exclusion decision.

Proper citation: MetaQC (RRID:SCR_006000) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/web/packages/QCGWAS/

Software tools for (automated and manual) quality control of the results of Genome Wide Association Studies.

Proper citation: QCGWAS (RRID:SCR_006408) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/release/bioc/html/sapFinder.html

An R software package, for detection of the variant peptides based on tandem mass spectrometry (MS/MS)-based proteomics data. It automates (1) variation-associated database construction, (2) database searching, (3) post-processing, (4) HTML-based report generation in shotgun proteomics.

Proper citation: sapFinder (RRID:SCR_002685) 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

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

Software R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions.

Proper citation: DSS (RRID:SCR_002754) Copy   

•   Source: SciCrunch Registry

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

An R package for array comparative genomic hybridization (aCGH) dye bias normalization.

Proper citation: aCGH.Spline (RRID:SCR_002927) Copy   

•   Source: SciCrunch Registry

https://github.com/itojal/hot_scan

A free software to detect genomic regions unusually rich in translocation breakpoints. More generally, it may be used to detect a region that is unusually rich in a given character of a binary sequence.

Proper citation: hot scan (RRID:SCR_002840) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/Basic4Cseq.html

An R/Bioconductor package for basic filtering, analysis and subsequent near-cis visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile.

Proper citation: Basic4Cseq (RRID:SCR_002836) Copy   

•   Source: SciCrunch Registry