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https://github.com/Kingsford-Group/kourami

Software graph guided assembly for novel human leukocyte antigen allele discovery. Graph guided assembly for HLA haplotypes covering typing exons using high coverage whole genome sequencing data.Implemented in Java and supported on Linux and Mac OS X.

Proper citation: Kourami (RRID:SCR_022280) Copy   

•   Source: SciCrunch Registry

https://gillisweb.cshl.edu/Primate_MTG_coexp/

We aligned single-nucleus atlases of middle temporal gyrus (MTG) of 5 primates (human, chimp, gorilla, macaque and marmoset) and identified 57 consensus cell types common to all species. We provide this resource for users to: 1) explore conservation of gene expression across primates at single cell resolution; 2) compare with conservation of gene coexpression across metazoa, and 3) identify genes with changes in expression or connectivity that drive rapid evolution of human brain.

Proper citation: Gene functional conservation across cell types and species (RRID:SCR_023292) Copy   

•   Source: SciCrunch Registry

http://mousesnp.roche.com/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. This website contains a database of the mouse SNP. DNA sequencing was performed along with genotyping. There is information on genotyping, mouse strain, and haplotype map.

Proper citation: Mouse Single Nucleotide Polymorphism Database (RRID:SCR_000033) Copy   

•   Source: SciCrunch Registry

http://lincsportal.ccs.miami.edu/dcic-portal/

Portal which provides a unified interface for searching LINCS dataset packages and reagents. Users can use the portal to access datasets, small molecules, cells, genes, proteins and peptides, and antibodies.

Proper citation: LINCS Data Portal (RRID:SCR_014939) Copy   

•   Source: SciCrunch Registry

https://github.com/kstreet13/slingshot

Software R package for identifying and characterizing continuous developmental trajectories in single cell data. Cell lineage and pseudotime inference for single-cell transcriptomics.

Proper citation: Slingshot (RRID:SCR_017012) Copy   

•   Source: SciCrunch Registry

http://rarediseases.info.nih.gov/GARD/Default.aspx

Genetic and Rare Diseases Information Center (GARD) is a collaborative effort of two agencies of the National Institutes of Health, The Office of Rare Diseases Research (ORDR) and the National Human Genome Research Institute (NHGRI) to help people find useful information about genetic conditions and rare diseases. GARD provides timely access to experienced information specialists who can furnish current and accurate information about genetic and rare diseases. So far, GARD has responded to 27,635 inquiries on about 7,147 rare and genetic diseases. Requests come not only from patients and their families, but also from physicians, nurses and other health-care professionals. GARD also has proved useful to genetic counselors, occupational and physical therapists, social workers, and teachers who work with people with a genetic or rare disease. Even scientists who are studying a genetic or rare disease and who need information for their research have contacted GARD, as have people who are taking part in a clinical study. Community leaders looking to help people find resources for those with genetic or rare diseases and advocacy groups who want up-to-date disease information for their members have contacted GARD. And members of the media who are writing stories about genetic or rare diseases have found the information GARD has on hand useful, accurate and complete. GARD has information on: :- What is known about a genetic or rare disease. :- What research studies are being conducted. :- What genetic testing and genetic services are available. :- Which advocacy groups to contact for a specific genetic or rare disease. :- What has been written recently about a genetic or rare disease in medical journals. GARD information specialists get their information from: :- NIH resources. :- Medical textbooks. :- Journal articles. :- Web sites. :- Advocacy groups, and their literature and services. :- Medical databases.

Proper citation: Genetic and Rare Diseases Information Center (RRID:SCR_008695) Copy   

•   Source: SciCrunch Registry

http://interactome.baderlab.org/

Project portal for the Human Reference Protein Interactome Project, which aims generate a first reference map of the human protein-protein interactome network by identifying binary protein-protein interactions (PPIs). It achieves this by systematically interrogating all pairwise combinations of predicted human protein-coding genes using proteome-scale technologies.

Proper citation: Human Reference Protein Interactome Project (RRID:SCR_015670) Copy   

•   Source: SciCrunch Registry

https://github.com/satijalab/seurat

Software R package designed for QC, analysis, and exploration of single cell RNA-seq data. Enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. Used for quality control, analysis, and exploration of single-cell RNA sequencing (scRNA-seq) data.

Proper citation: Seurat (RRID:SCR_016341) Copy   

•   Source: SciCrunch Registry

https://github.com/davidaknowles/leafcutter/

Software tool for identifying and quantifying RNA splicing variation. Used to study sample and population variation in intron splicing. Identifies variable intron splicing events from short read RNA-seq data and finds alternative splicing events of high complexity. Used for detecting differential splicing between sample groups, and for mapping splicing quantitative trait loci (sQTLs).

Proper citation: LeafCutter (RRID:SCR_017639) Copy   

•   Source: SciCrunch Registry

https://github.com/hahnlab/CAFExp

Software tool for computational analysis of gene family evolution. Used for statistical analysis of evolution gene family sizes. Models evolution of gene family sizes over phylogeny.

Proper citation: Computational Analysis of gene Family Evolution (RRID:SCR_018924) Copy   

•   Source: SciCrunch Registry

https://github.com/broadinstitute/Drop-seq

Software Java tools for analyzing Drop-seq data. Used to analyze gene expression from thousands of individual cells simultaneously. Analyzes mRNA transcripts while remembering origin cell transcript.

Proper citation: Drop-seq tools (RRID:SCR_018142) Copy   

•   Source: SciCrunch Registry

https://github.com/sreeramkannan/Shannon

Software tool for de novo transcriptome assembly from RNA-Seq data.

Proper citation: Shannon (RRID:SCR_017037) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/SNPRelate/

Software R package as parallel computing toolset for relatedness and principal component analysis of SNP data.

Proper citation: SNPRelate (RRID:SCR_022719) Copy   

•   Source: SciCrunch Registry

https://github.com/xfengnefx/hifiasm-meta

Software tool as metagenome assembler that exploits high accuracy of recent data. De novo metagenome assembler, based on haplotype resolved de novo assembler for PacBio Hifi reads. Workflow consists of optional read selection, sequencing error correction, read overlapping, string graph construction and graph cleaning.

Proper citation: hifiasm-meta (RRID:SCR_022771) Copy   

•   Source: SciCrunch Registry

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://github.com/FunctionLab/sei-framework

Web server for systematically predicting sequence regulatory activities and applying sequence information to human genetics data. Provides global map from any sequence to regulatory activities, as represented by sequence classes, and each sequence class integrates predictions for chromatin profiles like transcription factor, histone marks, and chromatin accessibility profiles across wide range of cell types.

Proper citation: sei (RRID:SCR_022571) 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

http://www.broad.mit.edu/annotation/fungi/fgi/

Produces and analyzes sequence data from fungal organisms that are important to medicine, agriculture and industry. The FGI is a partnership between the Broad Institute and the wider fungal research community, with the selection of target genomes governed by a steering committee of fungal scientists. Organisms are selected for sequencing as part of a cohesive strategy that considers the value of data from each organism, given their role in basic research, health, agriculture and industry, as well as their value in comparative genomics.

Proper citation: Fungal Genome Initiative (RRID:SCR_003169) Copy   

•   Source: SciCrunch Registry

https://pephub.databio.org

Web biological metadata server to view, store, and share your sample metadata in form of Portable Encapsulated Projects. PEPhub takes advantage of PEP biological metadata standard to store, edit, and access your PEPs in one place. Components include database where PEPs are stored; API to programmatically read and write PEPs in database; web based user interface to view and manage these PEPs via front end.

Proper citation: PEPhub (RRID:SCR_024892) Copy   

•   Source: SciCrunch Registry

https://www.clinicalgenome.org

Genomics knowledgebase for clinical relevance of genes and variants for use in research. ClinGen's primary function is to store and share information for the benefit of the scientific community. Laboratory scientists, clinicians, and patients can share and access data.

Proper citation: ClinGen (RRID:SCR_014968) Copy   

•   Source: SciCrunch Registry