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SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
http://www.broadinstitute.org/genome_bio/siphy/
Software that implements rigorous statistical tests to detect bases under selection from a multiple alignment data. It takes full advantage of deeply sequenced phylogenies to estimate both unlikely substitution patterns as well as slowdowns or accelerations in mutation rates. It can be applied as an Hidden Markov Model (HMM), in sliding windows, or to specific regions.
Proper citation: SiPhy (RRID:SCR_000564) Copy
https://sites.google.com/site/jingyijli/SLIDE.zip
Software package that takes exon boundaries and RNA-Seq data as input to discern the set of mRNA isoforms that are most likely to present in an RNA-Seq sample. It is based on a linear model with a design matrix that models the sampling probability of RNA-Seq reads from different mRNA isoforms. To tackle the model unidentifiability issue, SLIDE uses a modified Lasso procedure for parameter estimation. Compared with deterministic isoform assembly algorithms (e.g., Cufflinks), SLIDE considers the stochastic aspects of RNA-Seq reads in exons from different isoforms and thus has increased power in detecting more novel isoforms. Another advantage of SLIDE is its flexibility of incorporating other transcriptomic data such as RACE, CAGE, and EST into its model to further increase isoform discovery accuracy. SLIDE can also work downstream of other RNA-Seq assembly algorithms to integrate newly discovered genes and exons. Besides isoform discovery, SLIDE sequentially uses the same linear model to estimate the abundance of discovered isoforms.
Proper citation: SLIDE (RRID:SCR_005137) Copy
Software application for annotating character matrix files with ontology terms. Character states can be annotated using Entity-Quality syntax, where entity, quality, and possibly related entities are drawn from requisite ontologies. In addition, taxa (the rows of a character matrix) can be annotated with identifiers from taxonomy ontology. Phenex saves ontology annotations alongside original free text character matrix data using new NeXML format standard for evolutionary data.
Proper citation: Phenex (RRID:SCR_021748) Copy
https://www.bcgsc.ca/resources/software/ntcard
Software tool for estimating k-mer coverage histogram of genomics data. Streaming algorithm for estimating frequencies of k-mers in genomics datasets.
Proper citation: ntCard (RRID:SCR_022010) Copy
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
Web application that helps design, evaluate and clone guide sequences for the CRISPR/Cas9 system. This sgRNA design tool assists with guide selection in a variety of genomes and pre-calculated results for all human coding exons as a UCSC Genome Browser track.
Proper citation: CRISPOR (RRID:SCR_015935) Copy
https://gitlab.com/rosen-lab/white-adipose-atlas
Single cell atlas of human and mouse white adipose tissue.
Proper citation: White Adipose Atlas (RRID:SCR_023625) Copy
Software library and suite of command line tools for working with DNA sequence that takes a k-mer-centric approach to sequence analysis. It is primarily aimed at short-read sequencing data such as that produced by the Illumina platform.
Proper citation: khmer (RRID:SCR_001156) Copy
Catalog of published genome-wide association studies. Genome-wide set of genetic variants in different individuals to see if any variant is associated with trait and disease. Database of genome-wide association study (GWAS) publications including only those attempting to assay single nucleotide polymorphisms (SNPs). Publications are organized from most to least recent date of publication. Studies are identified through weekly PubMed literature searches, daily NIH-distributed compilations of news and media reports, and occasional comparisons with an existing database of GWAS literature (HuGE Navigator). Works with HANCESTRO ancestry representation.
Proper citation: GWAS: Catalog of Published Genome-Wide Association Studies (RRID:SCR_012745) Copy
https://github.com/bioinform/somaticseq
Software accurate somatic mutation detection pipeline implementing stochastic boosting algorithm to produce somatic mutation calls for both single nucleotide variants and small insertions and deletions. NGS variant calling and classification.
Proper citation: SomaticSeq (RRID:SCR_024891) Copy
https://github.com/xinhe-lab/GSFA
Software R package that performs sparse factor analysis and differential gene expression discovery simultaneously on single cell CRISPR screening data.
Proper citation: Guided Sparse Factor Analysis (RRID:SCR_025023) Copy
https://pvactools.readthedocs.io/en/latest/
Software toolkit to identify and visualize cancer neoantigens. Cancer immunotherapy tools suite consisting of following tools: pVACseq as cancer immunotherapy pipeline for identifying and prioritizing neoantigens from VCF file; pVACbind as cancer immunotherapy pipeline for identifying and prioritizing neoantigens from FASTA file; pVACfuse as tool for detecting neoantigens resulting from gene fusions; pVACvector as tool designed to aid specifically in construction of DNA-based cancer vaccines; pVACview as application based on R Shiny that assists users in reviewing, exploring and prioritizing neoantigens from results of pVACtools processes for personalized cancer vaccine design.
Proper citation: pVACtools (RRID:SCR_025435) Copy
http://www.informatics.jax.org/mgihome/GO/project.shtml
This resource is part of the Gene Ontology Consortium which seeks to provide controlled vocabularies for the description of the molecular function, biological process, and cellular component of gene products. These terms are to be used as attributes of gene products by collaborating databases, facilitating uniform queries across them. GO team members at MGI participate in ontology development, outreach, and functional curation of mouse gene products. The GO vocabularies have a hierarchical structure that permits a range of detail from high-level, broadly descriptive terms to very low level, highly specific terms. This broad range is useful both in annotating genes and in searching for gene information using these terms as search criteria. GO terms are defined, allowing all databases to use the terms consistently and properly. GO annotations in the databases additionally include the publication reference which allowed the association to be made and an evidence statement citing how the association was determined.
Proper citation: Mouse Genome Informatics: The Gene Ontology Project (RRID:SCR_006447) Copy
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. Integrated database of genomic, expression and protein data for Drosophila, Anopheles, C. elegans and other organisms. You can run flexible queries, export results and analyze lists of data. FlyMine presents data in categories, with each providing information on a particular type of data (for example Gene Expression or Protein Interactions). Template queries, as well as the QueryBuilder itself, allow you to perform searches that span data from more than one category. Advanced users can use a flexible query interface to construct their own data mining queries across the multiple integrated data sources, to modify existing template queries or to create your own template queries. Access our FlyMine data via our Application Programming Interface (API). We provide client libraries in the following languages: Perl, Python, Ruby and & Java API
Proper citation: FlyMine (RRID:SCR_002694) Copy
https://bioconductor.org/packages/release/bioc/html/apeglm.html
Software package provides Bayesian shrinkage estimators for effect sizes for variety of GLM models, using approximation of posterior for individual coefficients.
Proper citation: apeglm (RRID:SCR_026951) Copy
https://github.com/zhoujt1994/scHiCluster
Software Python package for single-cell chromosome contact data analysis. It includes the identification of cell types (clusters), loop calling in cell types, and domain and compartment calling in single cells. Facilitates visualization and comparison of single-cell 3D genomes.
Proper citation: scHiCluster (RRID:SCR_027854) Copy
https://bioconductor.org/packages/release/bioc/html/tximeta.html
Software R package for reference sequence checksums for provenance identification in RNA-seq. Performs numerous annotation and metadata gathering tasks on behalf of users during the import of transcript counts and abundance from quantification tools such as salmon. Data are imported as SummarizedExperiment objects with associated GenomicRanges metadata. Correct metadata is added automatically via reference sequence digests, facilitating genomic analyses and assisting in computational reproducibility.
Proper citation: tximeta (RRID:SCR_028005) Copy
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
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
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
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