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https://github.com/PathwayAnalysisPlatform/PathwayMatcher

Software tool for multi omics pathway mapping and proteoform network generation. Open source software writen in Java to search for pathways related to a list of proteins in Reactome.

Proper citation: PathwayMatcher (RRID:SCR_016759) Copy   

•   Source: SciCrunch Registry

https://pachterlab.github.io/sleuth/about

Software tool for analysis of RNA-Seq experiments for which transcript abundances have been quantified with kallisto. Used for the differential analysis of gene expression data that utilizes bootstrapping in conjunction with response error linear modeling to decouple biological variance from inferential variance.

Proper citation: sleuth (RRID:SCR_016883) Copy   

•   Source: SciCrunch Registry

https://github.com/yarden/MISO/blob/fastmiso/docs/source/sashimi.rst

Software tool for quantitative visualization of aligned RNA-Seq reads that enables quantitative comparison of exon usage across samples or experimental conditions.

Proper citation: Sashimiplot (RRID:SCR_016861) Copy   

•   Source: SciCrunch Registry

https://panoramaweb.org/project/home/begin.view?

Repository software for targeted mass spectrometry assays from Skyline. Targeted proteomics knowledge base. Public repository for quantitative data sets processed in Skyline. Facilitates viewing, sharing, and disseminating results contained in Skyline documents.

Proper citation: PanoramaWeb (RRID:SCR_017136) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/GenomicRanges/

Software R package for computing and annotating genomic ranges. Used for storing and manipulating genomic intervals and variables defined along genome.

Proper citation: Genomic Ranges (RRID:SCR_017051) Copy   

•   Source: SciCrunch Registry

https://combine-lab.github.io/salmon/

Software tool for quantifying expression of transcripts using RNA-seq data. Provides fast and bias-aware quantification of transcript expression. Transcriptome-wide quantifier to correct for fragment GC-content bias.

Proper citation: Salmon (RRID:SCR_017036) Copy   

•   Source: SciCrunch Registry

http://research.mssm.edu/integrative-network-biology/Software.html

Software tool as probabilistic multi omics data matching procedure to curate data, identify and correct data annotation and errors in large databases. Used to check potential labeling errors in profiles where number of cis relationships is small, such as miRNA and RPPA profiles.

Proper citation: proMODMatcher (RRID:SCR_017219) Copy   

•   Source: SciCrunch Registry

https://github.com/aidenlab/juicer.git

Software platform for analyzing kilobase resolution Hi-C data. Open source tool for analyzing terabase scale Hi-C datasets. Allowes to transform raw sequence data into normalized contact maps.

Proper citation: Juicer (RRID:SCR_017226) Copy   

•   Source: SciCrunch Registry

http://pathwaynet.princeton.edu/

Web user interface for interaction predictions of human gene networks and integrative analysis of user data types that takes advantage of data from diverse tissue and cell-lineage origins. Predicts presence of functional association and interaction type among human genes or its protein products on whole genome scale. Used to analyze experimetnal gene in context of interaction networks.

Proper citation: PathwayNet (RRID:SCR_017353) Copy   

•   Source: SciCrunch Registry

https://github.com/brentp/peddy

Software package that evaluates correspondence between stated sexes, relationships, and ancestries in pedigree file and those inferred from genotypes in VCF file resulting from human whole genome sequencing or whole exome sequencing studies. Facilitates both automated and interactive, visual detection of sample swaps, poor sequencing quality, and other indicators of sample problems.

Proper citation: peddy (RRID:SCR_017287) Copy   

•   Source: SciCrunch Registry

https://pachterlab.github.io/cgal/

Software tool for computing genome assembly likelihoods.Computes likelihood of reads with respect to assembly and statistical model which can be used as metric for evaluating assemblies. Novel likelihood based approach to assembly assessment in absence of ground truth.

Proper citation: Computing Genome Assembly Likelihoods (RRID:SCR_017624) Copy   

•   Source: SciCrunch Registry

https://github.com/YosefLab/FastProject

Software Python tool for low dimensional analysis of single-cell RNA-Seq data. Software package for two dimensional visualization of single cell data. Analyzes gene expression matrix and produces output report in which two-dimensional of data can be explored.

Proper citation: FastProject (RRID:SCR_017462) Copy   

•   Source: SciCrunch Registry

http://geneontology.org/docs/go-consortium/

Consortium integrates resources from variety of research groups, from model organisms to protein databases to biological research communities actively involved in development and implementation of Gene Ontology. Mission to develop up to date, comprehensive, computational model of biological systems, from molecular level to larger pathways, cellular and organism level systems.

Proper citation: GO Gene Ontology Consortium and Knowledgebase (RRID:SCR_017505) Copy   

•   Source: SciCrunch Registry

https://imputationserver.sph.umich.edu/

Web server to implement whole genotype imputation workflow for efficient parallelization of computationally intensive tasks. Service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity. Used to find haplotype segments and reference panel of sequenced genomes, assign genotypes at untyped markers, improve genome coverage, facilitate comparison and combination of studies that use different marker panels, increase power to detect genetic association, and guide fine mapping.

Proper citation: Michigan Imputation Server (RRID:SCR_017579) Copy   

•   Source: SciCrunch Registry

http://topaz.gatech.edu/GeneMark/

Software package for ab initio identification of protein coding regions in RNA transcripts. Algorithm parameters are estimated by unsupervised training which makes unnecessary manually curated preparation of training sets. Sets of assembled eukaryotic transcripts can be analyzed by modified GeneMarkS-T algorithm which part of gene prediction programs GeneMark.

Proper citation: GeneMarkS-T (RRID:SCR_017648) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/sites/GeneTests/lab

The GeneTests Web site, a publicly funded medical genetics information resource developed for physicians, other healthcare providers, and researchers, is available at no cost to all interested persons. By providing current, authoritative information on genetic testing and its use in diagnosis, management, and genetic counseling, GeneTests promotes the appropriate use of genetic services in patient care and personal decision making. At This Site: * GeneReviews: Expert-authored peer-reviewed disease descriptions * Laboratory Directory: International directory of genetic testing laboratories * Clinic Directory: International directory of genetics and prenatal diagnosis clinics * Educational Materials: Illustrated glossary, information on genetic services, PowerPoint presentations, annotated Internet resources We comply with the HONcode standard for trustworthy health information.

Proper citation: GeneTests (RRID:SCR_010725) Copy   

•   Source: SciCrunch Registry

http://oligogenome.stanford.edu/

The Stanford Human OligoGenome Project hosts a database of capture oligonucleotides for conducting high-throughput targeted resequencing of the human genome. This set of capture oligonucleotides covers over 92% of the human genome for build 37 / hg19 and over 99% of the coding regions defined by the Consensus Coding Sequence (CCDS). The capture reaction uses a highly multiplexed approach for selectively circularizing and capturing multiple genomic regions using the in-solution method developed in Natsoulis et al, PLoS One 2011. Combined pools of capture oligonucleotides selectively circularize the genomic DNA target, followed by specific PCR amplification of regions of interest using a universal primer pair common to all of the capture oligonucleotides. Unlike multiplexed PCR methods, selective genomic circularization is capable of efficiently amplifying hundreds of genomic regions simultaneously in multiplex without requiring extensive PCR optimization or producing unwanted side reaction products. Benefits of the selective genomic circularization method are the relative robustness of the technique and low costs of synthesizing standard capture oligonucleotide for selecting genomic targets.

Proper citation: OligoGenome (RRID:SCR_006025) Copy   

•   Source: SciCrunch Registry

http://modencode.org/

A comprehensive encyclopedia of genomic functional elements in the model organisms C. elegans and D. melanogaster. modENCODE is run as a Research Network and the consortium is formed by 11 primary projects, divided between worm and fly, spanning the domains of gene structure, mRNA and ncRNA expression profiling, transcription factor binding sites, histone modifications and replacement, chromatin structure, DNA replication initiation and timing, and copy number variation. The raw and interpreted data from this project is vetted by a data coordinating center (DCC) to ensure consistency and completeness. The entire modENCODE data corpus is now available on the Amazon Web Services EC2 cloud. What this means is that virtual machines and virtual compute clusters that you run within the EC2 cloud can mount the modENCODE data set in whole or in part. Your software can run analyses against the data files directly without experiencing the long waits and logistics associated with copying the datasets over to your local hardware. You may also view the data using GBrowse, Dataset Search, or download the data via FTP, as well as download pre-release datasets.

Proper citation: modENCODE (RRID:SCR_006206) Copy   

•   Source: SciCrunch Registry

http://sparkinsight.org

A clustering and visualization tool that enables the interactive exploration of genome-wide data, with a specialization in epigenomics data. Spark is also available as a service within the Epigenome toolset of the Genboree Workbench. The approach utilizes data clusters as a high-level visual guide and supports interactive inspection of individual regions within each cluster. The cluster view links to gene ontology analysis tools and the detailed region view connects to existing genome browser displays taking advantage of their wealth of annotation and functionality.

Proper citation: Spark (RRID:SCR_006207) Copy   

•   Source: SciCrunch Registry

http://phenomebrowser.net/

PhenomeNet is a cross-species phenotype similarity network. It contains the experimentally observed phenotypes of multiple species as well as the phenotypes of human diseases. PhenomeNet provides a measure of phenotypic similarity between the phenotypes it contains. The latest release (from 22 June 2012) contains 124,730 complex phenotype nodes taken from the yeast, fish, worm, fly, rat, slime mold and mouse model organism databases as well as human disease phenotypes from OMIM and OrphaNet. The network is a complete graph in which edge weights represent the degree of phenotypic similarity. Phenotypic similarity can be used to identify and prioritize candidate disease genes, find genes participating in the same pathway and orthologous genes between species. To compute phenotypic similarity between two sets of phenotypes, we use a weighted Jaccard index. First, phenotype ontologies are used to infer all the implications of a phenotype observation using several phenotype ontologies. As a second step, the information content of each phenotype is computed and used as a weight in the Jaccard index. Phenotypic similarity is useful in several ways. Phenotypic similarity between a phenotype resulting from a genetic mutation and a disease can be used to suggest candidate genes for a disease. Phenotypic similarity can also identify genes in a same pathway or orthologous genes. PhenomeNet uses the axioms in multiple species-dependent phenotype ontologies to infer equivalent and related phenotypes across species. For this purpose, phenotype ontologies and phenotype annotations are integrated in a single ontology, and automated reasoning is used to infer equivalences. Specifically, for every phenotype, PhenomeNet infers the related mammalian phenotype and uses the Mammalian Phenotype Ontology for computing phenotypic similarity. Tools: * PhenomeBLAST - A tool for cross-species alignments of phenotypes * PhenomeDrug - method for drug-repurposing

Proper citation: phenomeNET (RRID:SCR_006165) Copy   

•   Source: SciCrunch Registry