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https://CRAN.R-project.org/package=ComplexUpset

Software R package for visualization of intersecting sets. Used for quantitative analysis of sets, their intersections, and aggregates of intersections. Visualizes set intersections in matrix layout and introduces aggregates based on groupings and queries.

Proper citation: ComplexUpset (RRID:SCR_022752) Copy   

•   Source: SciCrunch Registry

https://upset.app/#:~:text=UpSet%20plots%20the%20intersections%20of,is%20part%20of%20an%20intersection.

Software tool to visualize set intersections in matrix layout. Interactive, web based visualization technique designed to analyze set based data. Visualizes both, set intersections and their properties, and elements in dataset. Used for quantitative analysis of data with more than three sets.

Proper citation: UpSet (RRID:SCR_022731) Copy   

•   Source: SciCrunch Registry

https://github.com/hetio/hetmatpy

Software Python package for matrix storage and operations on hetnets. Enables identifying relevant network connections between set of query nodes.

Proper citation: HetMatPy (RRID:SCR_023409) Copy   

•   Source: SciCrunch Registry

https://github.com/tobiasrausch/alfred

Web application as interactive multi-sample BAM alignment statistics, feature counting and feature annotation for long- and short-read sequencingas.

Proper citation: Alfred (RRID:SCR_023354) Copy   

•   Source: SciCrunch Registry

https://upsetplot.readthedocs.io/en/stable/

Software Python implementation of UpSet plots to visualize set overlaps.

Proper citation: UpSetPlot (RRID:SCR_023225) Copy   

•   Source: SciCrunch Registry

http://genomespace.org

Interoperability framework which supports integrative genomics analysis via access to various bioinformatics tools. Rather than performing analyses itself, GenomeSpace acts as a hub for data from supported bioinformatics tools and reformats data and results when necessary.

Proper citation: GenomeSpace (RRID:SCR_014967) Copy   

•   Source: SciCrunch Registry

https://www.cbcb.umd.edu/software/epiviz

Software package as interactive visualization tool for functional genomics data. Interactive visual analytics for functional genomics data.

Proper citation: Epiviz (RRID:SCR_022796) Copy   

•   Source: SciCrunch Registry

https://github.com/marbl/merqury

Software toolkit for reference free quality, completeness, and phasing assessment for genome assemblies. Genome assembly quality evaluation based on k-mers.

Proper citation: Merqury (RRID:SCR_022964) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/Maaslin2.html

SoftwareR package that identifies microbial taxa correlated with factors of interest using generalized linear models and mixed models.Used for efficiently determining multivariable association between clinical metadata and microbial meta'omic features.

Proper citation: MaAsLin2 (RRID:SCR_023241) Copy   

•   Source: SciCrunch Registry

https://github.com/ParkerLab/ataqv

Software package for QC and visualization of ATAC-seq results. Used to examine aligned reads and report basic metrics, including reads mapped in proper pairs, optical or PCR duplicates, reads mapping to autosomal or mitochondrial references, ratio of short to mononucleosomal fragment counts, mapping quality, various kinds of problematic alignments.

Proper citation: ataqv (RRID:SCR_023112) Copy   

•   Source: SciCrunch Registry

https://github.com/fritzsedlazeck/SURVIVOR

Software tool set for simulating and evaluating SVs, merging and comparing SVs within and among samples, and includes various methods to reformat or summarize SVs. Used for for SV simulation, comparison and filtering.

Proper citation: SURVIVOR (RRID:SCR_022995) Copy   

•   Source: SciCrunch Registry

https://github.com/virajbdeshpande/AmpliconArchitect

Software package designed to call circular DNA from short read WGS data.Used to identify one or more connected genomic regions which have simultaneous copy number amplification and elucidates architecture of amplicon.Used to reconstruct structure of focally amplified regions using whole genome sequencing and validate it extensively on multiple simulated and real datasets, across wide range of coverage and copy numbers.

Proper citation: AmpliconArchitect (RRID:SCR_023150) Copy   

•   Source: SciCrunch Registry

https://github.com/genome/bam-readcount

Software tool that runs on BAM or CRAM file and generates low level information about sequencing data at specific nucleotide positions. Its outputs include observed bases, readcounts, summarized mapping and base qualities, strandedness information, mismatch counts, and position within the reads.

Proper citation: bam readcount (RRID:SCR_023653) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/oligo.html

Software R package to analyze oligonucleotide arrays at probe level. Supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). Used for annotation of Affymetrix Gene Array data.

Proper citation: Preprocessing tools for oligonucleotide arrays (RRID:SCR_023726) Copy   

•   Source: SciCrunch Registry

https://github.com/churchmanlab/genewalk

Software for individual genes functions determination that are relevant in particular biological context and experimental condition. Quantifies similarity between vector representations of gene and annotated GO terms through representation learning with random walks on condition specific gene regulatory network. Similarity significance is determined through comparison with node similarities from randomized networks.

Proper citation: GeneWalk (RRID:SCR_023787) Copy   

•   Source: SciCrunch Registry

https://github.com/smithlabcode/riborex

Software R package for identification of differential translation from Ribo-seq data. Computational tool for mapping genome wide differences in translation efficiency.

Proper citation: riborex (RRID:SCR_019104) Copy   

•   Source: SciCrunch Registry

http://www.cidr.jhmi.edu/

Next generation sequencing and genotyping services provided to investigators working to discover genes that contribute to disease. On-site statistical geneticists provide insight into analysis issues as they relate to study design, data production and quality control. In addition, CIDR has a consulting agreement with the University of Washington Genetics Coordinating Center (GCC) to provide statistical and analytical support, most predominantly in the areas of GWAS data cleaning and methods development. Completed studies encompass over 175 phenotypes across 530 projects and 620,000 samples. The impact is evidenced by over 380 peer-reviewed papers published in 100 journals. Three pathways exist to access the CIDR genotyping facility: * NIH CIDR Program: The CIDR contract is funded by 14 NIH Institutes and provides genotyping and statistical genetic services to investigators approved for access through competitive peer review. An application is required for projects supported by the NIH CIDR Program. * The HTS Facility: The High Throughput Sequencing Facility, part of the Johns Hopkins Genetic Resources Core Facility, provides next generation sequencing services to internal JHU investigators and external scientists on a fee-for-service basis. * The JHU SNP Center: The SNP Center, part of the Johns Hopkins Genetic Resources Core Facility, provides genotyping to internal JHU investigators and external scientists on a fee-for-service basis. Data computation service is included to cover the statistical genetics services provided for investigators seeking to identify genes that contribute to human disease. Human Genotyping Services include SNP Genome Wide Association Studies, SNP Linkage Scans, Custom SNP Studies, Cancer Panel, MHC Panels, and Methylation Profiling. Mouse Genotyping Services include SNP Scans and Custom SNP Studies.

Proper citation: Center for Inherited Disease Research (RRID:SCR_007339) Copy   

•   Source: SciCrunch Registry

https://www.mc.vanderbilt.edu/victr/dcc/projects/acc/index.php/Main_Page

A national consortium formed to develop, disseminate, and apply approaches to research that combine DNA biorepositories with electronic medical record (EMR) systems for large-scale, high-throughput genetic research. The consortium is composed of seven member sites exploring the ability and feasibility of using EMR systems to investigate gene-disease relationships. Themes of bioinformatics, genomic medicine, privacy and community engagement are of particular relevance to eMERGE. The consortium uses data from the EMR clinical systems that represent actual health care events and focuses on ethical issues such as privacy, confidentiality, and interactions with the broader community.

Proper citation: eMERGE Network: electronic Medical Records and Genomics (RRID:SCR_007428) Copy   

•   Source: SciCrunch Registry

http://enhancer.lbl.gov/

Resource for experimentally validated human and mouse noncoding fragments with gene enhancer activity as assessed in transgenic mice. Most of these noncoding elements were selected for testing based on their extreme conservation in other vertebrates or epigenomic evidence (ChIP-Seq) of putative enhancer marks. Central public database of experimentally validated human and mouse noncoding fragments with gene enhancer activity as assessed in transgenic mice. Users can retrieve elements near single genes of interest, search for enhancers that target reporter gene expression to particular tissue, or download entire collections of enhancers with defined tissue specificity or conservation depth.

Proper citation: VISTA Enhancer Browser (RRID:SCR_007973) Copy   

•   Source: SciCrunch Registry

http://www.biodas.org

The Distributed Annotation System (DAS) defines a communication protocol used to exchange annotations on genomic or protein sequences. It is motivated by the idea that such annotations should not be provided by single centralized databases, but should instead be spread over multiple sites. Data distribution, performed by DAS servers, is separated from visualization, which is done by DAS clients. The advantages of this system are that control over the data is retained by data providers, data is freed from the constraints of specific organisations and the normal issues of release cycles, API updates and data duplication are avoided. DAS is a client-server system in which a single client integrates information from multiple servers. It allows a single machine to gather up sequence annotation information from multiple distant web sites, collate the information, and display it to the user in a single view. Little coordination is needed among the various information providers. DAS is heavily used in the genome bioinformatics community. Over the last years we have also seen growing acceptance in the protein sequence and structure communities. A DAS-enabled website or application can aggregate complex and high-volume data from external providers in an efficient manner. For the biologist, this means the ability to plug in the latest data, possibly including a user''s own data. For the application developer, this means protection from data format changes and the ability to add new data with minimal development cost. Here are some examples of DAS-enabled applications or websites for end users: :- Dalliance Experimental Web/Javascript based Genome Viewer :- IGV Integrative Genome Viewer java based browser for many genomes :- Ensembl uses DAS to pull in genomic, gene and protein annotations. It also provides data via DAS. :- Gbrowse is a generic genome browser, and is both a consumer and provider of DAS. :- IGB is a desktop application for viewing genomic data. :- SPICE is an application for projecting protein annotations onto 3D structures. :- Dasty2 is a web-based viewer for protein annotations :- Jalview is a multiple alignment editor. :- PeppeR is a graphical viewer for 3D electron microscopy data. :- DASMI is an integration portal for protein interaction data. :- DASher is a Java-based viewer for protein annotations. :- EpiC presents structure-function summaries for antibody design. :- STRAP is a STRucture-based sequence Alignment Program. Hundreds of DAS servers are currently running worldwide, including those provided by the European Bioinformatics Institute, Ensembl, the Sanger Institute, UCSC, WormBase, FlyBase, TIGR, and UniProt. For a listing of all available DAS sources please visit the DasRegistry. Sponsors: The initial ideas for DAS were developed in conversations with LaDeana Hillier of the Washington University Genome Sequencing Center.

Proper citation: Distributed Annotation System (RRID:SCR_008427) Copy   

•   Source: SciCrunch Registry