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http://chgr.mc.vanderbilt.edu/page/gist

Software package to test if a marker can account in part for the linkage signal in its region. There are two versions of the software: Windows and Linux/Unix.

Proper citation: Genotype-IBD Sharing Test (RRID:SCR_006257) 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://github.com/christopher-vollmers/C3POa

Software to detect DNA splint sequence raw reads. Computational pipeline for calling consensi on R2C2 nanopore data.

Proper citation: C3Poa (RRID:SCR_025484) Copy   

•   Source: SciCrunch Registry

https://github.com/YingMa0107/CARD/

Software R package for spatial transcriptomics. Deconvolution method that combines cell-type-specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition across tissue locations.

Proper citation: Conditional AutoRegressive Deconvolution (RRID:SCR_026310) Copy   

•   Source: SciCrunch Registry

https://github.com/kaizhang/SnapATAC2

Software Python/Rust package for single-cell epigenomics analysis.

Proper citation: SnapATAC2 (RRID:SCR_026622) Copy   

•   Source: SciCrunch Registry

https://github.com/higlass/higlass

Web-based visual exploration and analysis of genome interaction maps.

Proper citation: HiGlass (RRID:SCR_026687) Copy   

•   Source: SciCrunch Registry

https://github.com/KrishnaswamyLab/PHATE

Software tool for visualizing high dimensional data using novel conceptual framework for learning and visualizing manifold to preserve both local and global distances.

Proper citation: PHATE (RRID:SCR_027119) Copy   

•   Source: SciCrunch Registry

https://github.com/Yonghao-Holden/TEProf3

Software pipeline to detect Transposable Elements transcripts. Used to identify TE-derived promoters and transcripts using transcriptomic data from multiple sources, including short-read RNA-seq data, long-read RNA-seq data and single cell RNA-seq data.

Proper citation: TEProf3 (RRID:SCR_027288) Copy   

•   Source: SciCrunch Registry

http://www.genome.gov/12514286

Current Topics in Genome Analysis lecture series consists of 13 lectures on successive Wednesdays, with a mixture of local and outside speakers covering the major areas of genomics. In this tenth edition of the series, rather than splitting the lectures into laboratory-based and computationally-based blocks, we have intermingled the lectures by general subject area. We hope that this approach conveys the idea that both laboratory- and computationally-based approaches are necessary in order to do cutting-edge biological research in the future. The lectures are geared at the level of first year graduate students, are practical in nature, and are intended for a diverse audience. Handouts will be provided for each lecture, and time will be available at the end of each lecture for questions and discussion. All lectures are held on Wednesday mornings from 9:30 a.m. to 11:00 a.m. in the Lipsett Amphitheatre of the National Institutes of Health Clinical Center (Building 10). Course Directors: Andy Baxevanis, Ph.D., Eric Green, M.D., Ph.D., Tyra Wolfsberg, Ph.D. Lectures in this series will be available on the GenomeTV channel of YouTube viewing shortly after the live lecture and also includes all of the handouts. Lectures will not be Webcast live. The lecture series archives (available from 2005-) covers important milestones in genetics. CME Credits: This activity has been approved for AMA PRA Category 1 Credits. The intended audience includes clinicians, clinical geneticists, social and behavioral scientists, genetic counselors, those involved with genetics and public policy, health educators, and other biomedical and clinical scientists with an interest in genetics, genomics and personalized medicine. No prior expertise on the part of the audience will be required and the lecturers will be instructed to provide any relevant background as part of their lectures.

Proper citation: Current Topics in Genome Analysis (RRID:SCR_006475) Copy   

•   Source: SciCrunch Registry

http://jbrowse.org/

A high-performance visualization tool for interactive exploration of large, integrated genomic datasets written primarily in JavaScript. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations.

Proper citation: JBrowse (RRID:SCR_001004) Copy   

•   Source: SciCrunch Registry

https://www.phenx.org/Default.aspx?tabid=56

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on 05 01 2025. PhenX is a project to prioritize Phenotype and eXposure measures for Genome-wide Association Studies (GWAS). Leaders of the scientific community will assess and prioritize a broad range of domains relevant to genomics research and public health. The PhenX Steering Committee (SC), chaired by Dr. Jonathan Haines, provides leadership in the selection of domains and domain experts. Members of the SC include outstanding scientists from the research community and liaisons from the Institutes and Centers of the National Institutes of Health. Consensus measures for GWAS will have a direct impact on biomedical research and ultimately on public health. During the course of this project, up to 20 research domains will be examined, with up to 15 measures being recommended for use in future GWAS and other large-scale genomic research efforts. The goal is to maximize the benefits of future research by having comparable measures so that studies can be integrated. Each selected domain will be reviewed by a Working Group (WG) of scientists who are experts in the research area. A systematic review of the literature will guide the WGs selection of up to 15 high priority measures with standardized approaches for measurement. Selection criteria for the measures include factors such as validity, reproducibility, cost, feasibility, and burden to both investigators and participants. The scientific community will be asked to provide input on proposed measures. Consensus development is a key component of the project.

Proper citation: Consensus Measures for Phenotype and Exposure (RRID:SCR_006688) Copy   

•   Source: SciCrunch Registry

http://www.phrap.org/consed/consed.html

A graphical tool for sequence finishing (BAM File Viewer, Assembly Editor, Autofinish, Autoreport, Autoedit, and Align Reads To Reference Sequence)

Proper citation: Consed (RRID:SCR_005650) Copy   

•   Source: SciCrunch Registry

http://compgen.bscb.cornell.edu/phast/

A freely available software package for comparative and evolutionary genomics that consists of about half a dozen major programs, plus more than a dozen utilities for manipulating sequence alignments, phylogenetic trees, and genomic annotations. For the most part, PHAST focuses on two kinds of applications: the identification of novel functional elements, including protein-coding exons and evolutionarily conserved sequences; and statistical phylogenetic modeling, including estimation of model parameters, detection of signatures of selection, and reconstruction of ancestral sequences. It consists of over 60,000 lines of C code.

Proper citation: PHAST (RRID:SCR_003204) Copy   

•   Source: SciCrunch Registry

http://ccb.jhu.edu/software/ASprofile/

A suite of programs for extracting, quantifying and comparing alternative splicing (AS) events from RNA-seq data.

Proper citation: ASprofile (RRID:SCR_001833) Copy   

•   Source: SciCrunch Registry

http://mendel.stanford.edu/SidowLab/downloads/MAPP/

Java program that predicts the impact of all possible amino acid substitutions on the function of the protein., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: MAPP (RRID:SCR_010775) Copy   

•   Source: SciCrunch Registry

http://www.ensembl.org/

Collection of genome databases for vertebrates and other eukaryotic species with DNA and protein sequence search capabilities. Used to automatically annotate genome, integrate this annotation with other available biological data and make data publicly available via web. Ensembl tools include BLAST, BLAT, BioMart and the Variant Effect Predictor (VEP) for all supported species.

Proper citation: Ensembl (RRID:SCR_002344) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/

Community model organism database for laboratory mouse and authoritative source for phenotype and functional annotations of mouse genes. MGD includes complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics.Contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology.

Proper citation: Mouse Genome Database (RRID:SCR_012953) Copy   

•   Source: SciCrunch Registry

http://hb.flatironinstitute.org/

Formerly known as GIANT (Genome-scale Integrated Analysis of gene Networks in Tissues), HumanBase applies machine learning algorithms to learn biological associations from massive genomic data collections. These integrative analyses reach beyond existing "biological knowledge" represented in the literature to identify novel, data-driven associations.

Proper citation: HumanBase (RRID:SCR_016145) Copy   

•   Source: SciCrunch Registry

https://phenodb.org/

Database for phenotype genotype associations for humans. Used by clinical researchers to store standardized phenotypic information, diagnosis, and pedigree data and then run analyses on VCF files from individuals, families or cohorts with suspected Mendelian disease.

Proper citation: PhenoDB (RRID:SCR_016551) Copy   

•   Source: SciCrunch Registry