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http://compbio.cs.sfu.ca/software-novelseq

Software pipeline to detect novel sequence insertions using high throughput paired-end whole genome sequencing data.

Proper citation: NovelSeq (RRID:SCR_003136) Copy   

•   Source: SciCrunch Registry

http://mrcanavar.sourceforge.net/

Copy number caller that analyzes the whole-genome next-generation sequence mapping read depth to discover large segmental duplications and deletions. It also has the capability of predicting absolute copy numbers of genomic intervals.

Proper citation: mrCaNaVaR (RRID:SCR_003135) Copy   

•   Source: SciCrunch Registry

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

Software for differential expression analysis of RNA-seq using the Poisson-Tweedie family of distributions.

Proper citation: tweeDEseq (RRID:SCR_003038) Copy   

•   Source: SciCrunch Registry

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

Software package for calculating aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S).

Proper citation: BRAIN (RRID:SCR_003018) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/salt1/

Software that can accurately and sensitivity classify short reads of next-generation sequencing (NGS) into protein domain families. It is based on profile HMM and a supervised graph contribution algorithm. Compared to existing tools, it has high sensitivity and specificity in classifying short reads into their native domain families.

Proper citation: SALT (RRID:SCR_003187) Copy   

•   Source: SciCrunch Registry

https://github.com/brunonevado/Pipeliner

Software for evaluating the performance of bioinformatics pipelines for Next Generation re-Sequencing.

Proper citation: Pipeliner (RRID:SCR_003171) Copy   

•   Source: SciCrunch Registry

https://github.com/ggloor/ALDEx2

Software tool to examine compositional high-throughput sequence data with Welch's t-test. A differential relative count abundance analysis for the comparison of two conditions. For example, single-organism and meta-rna-seq high-throughput sequencing assays, or of selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, that has been optimized for three or more experimental replicates. Infers sampling variation and calculates the expected Benjamini-Hochberg false discovery rate given the biological and sampling variation using several parametric and non-parametric tests. Can to glm and Kruskal-Wallace tests on one-way ANOVA style designs.

Proper citation: ALDEx2 (RRID:SCR_003364) Copy   

•   Source: SciCrunch Registry

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

An R package for extending the grammar of graphics for genomic data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries.

Proper citation: ggbio (RRID:SCR_003313) Copy   

•   Source: SciCrunch Registry

http://primerseq.sourceforge.net/

Software that designs RT-PCR primers that evaluate alternative splicing events by incorporating RNA-Seq data. It is particularly advantageous for designing a large number of primers for validating alternative splicing events found in RNA-Seq data. It incorporates RNA-Seq data in the design process to weight exons by their read counts. Essentially, the RNA-Seq data allows primers to be placed using actually expressed transcripts. This could be for a particular cell line or experimental condition, rather than using annotations that incorporate transcripts that are not expressed for the data. Alternatively, you can design primers that are always on constitutive exons. PrimerSeq does not limit the use of gene annotations and can be used for a wide array of species.

Proper citation: PrimerSeq (RRID:SCR_003295) Copy   

•   Source: SciCrunch Registry

http://shendurelab.github.io/MIPGEN/

Software for a fast, simple way to generate designs for MIP assays targeting hundreds or thousands of genomic loci in parallel. Packaged with MIPgen are scripts that aid in visualization of MIP designs and processing of MIP sequence reads to SAM files that can then be passed through any standard variant calling pipeline.

Proper citation: MIPgen (RRID:SCR_003325) Copy   

•   Source: SciCrunch Registry

https://bitbucket.org/johanneskoester/snakemake/wiki/

A Python based language and execution environment for make-like workflows. The system supports the use of automatically inferred multiple named wildcards (or variables) in input and output filenames.

Proper citation: Snakemake (RRID:SCR_003475) Copy   

•   Source: SciCrunch Registry

http://knowledgemap.mc.vanderbilt.edu/research/content/phewas-r-package

Software package contains methods for performing Phenome-Wide Association Study.

Proper citation: PheWAS R Package (RRID:SCR_003512) Copy   

•   Source: SciCrunch Registry

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

Software tool for running and managing bioinformatics pipelines. It specializes in enabling users to turn existing pipelines based on shell scripts or command line tools into highly flexible, adaptable and maintainable workflows with a minimum of effort. Bpipe ensures that pipelines execute in a controlled and repeatable fashion and keeps audit trails and logs to ensure that experimental results are reproducible. Requiring only Java as a dependency, it is fully self-contained and cross-platform, making it very easy to adopt and deploy into existing environments.

Proper citation: Bpipe (RRID:SCR_003471) Copy   

•   Source: SciCrunch Registry

http://www.lgm.upmc.fr/parseq/

Statistical software for transcription landscape reconstruction at a basepair resolution from RNA Seq read counts. It is based on a state-space model which describes, in terms of abrupt shifts and more progressive drifts, the transcription level dynamics along the genome. Alongside variations of transcription level, it incorporates a component of short-range variation to pull apart local artifacts causing correlated dispersion. Reconstruction of the transcription level relies on a conditional sequential Monte Carlo approach that is combined with parameter estimation in a Markov chain Monte Carlo algorithm known as particle Gibbs. The method allows to estimate the local transcription level, to call transcribed regions, and to identify the transcript borders.

Proper citation: Parseq (RRID:SCR_003464) Copy   

•   Source: SciCrunch Registry

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

Software package that performs genetic association tests between SNPs (one-at-a-time) and multiple phenotypes (separately or in joint model).

Proper citation: MultiPhen (RRID:SCR_003498) Copy   

•   Source: SciCrunch Registry

http://www.biostat.wisc.edu/~kendzior/EBSEQ/

Software R package for RNA-Seq Differential Expression Analysis.

Proper citation: EBSeq (RRID:SCR_003526) Copy   

•   Source: SciCrunch Registry

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

A Java interface to the mzTab data exchange format for reporting a summary of proteomics results.

Proper citation: jmzTab (RRID:SCR_003481) Copy   

•   Source: SciCrunch Registry

http://genome.sph.umich.edu/wiki/RAREMETAL

A software program that facilitates the meta-analysis of rare variants from genotype arrays or sequencing.

Proper citation: RAREMETAL (RRID:SCR_003573) Copy   

•   Source: SciCrunch Registry

http://www.compgen.org/tools/metagen

Software program providing a method for meta-analysis of case-control genetic association studies using random-effects logistic regression.

Proper citation: metagen (RRID:SCR_003443) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/a/lbl.gov/biopig/

Software providing a framework for genomic data analysis using Apache Pig and Hadoop.

Proper citation: BioPig (RRID:SCR_004636) Copy   

•   Source: SciCrunch Registry