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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/epurdom/clusterExperiment

Software open source R package for executing, evaluating and visualizing different clusterings of experimental data, including data from single cell RNA-Seq studies. Software for running and comparing different clusterings of single cell sequencing data.

Proper citation: clusterExperiment (RRID:SCR_017439) Copy   

•   Source: SciCrunch Registry

https://www.hmtphenome.uniba.it

Collection of data about variants, genes, phenotypes and diseases involved in mitochondrial functionality. Users can search for variant position, gene, phenotype or disease and retrieve all related information through integrated network of biological entities.

Proper citation: HmtPhenome (RRID:SCR_017289) Copy   

•   Source: SciCrunch Registry

https://scfind.sanger.ac.uk/

Software R package as search tool for single cell RNA-seq data by gene lists. Builds index from scRNA-seq datasets which organizes information in suitable and compact manner so that datasets can be very efficiently searched for either cells or cell types in which given list of genes is expressed.

Proper citation: Scfind (RRID:SCR_017339) Copy   

•   Source: SciCrunch Registry

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

Software package for interactive graphics for gene expression analysis. Generates interactive visualisations for analysis of RNA-sequencing data.

Proper citation: Glimma (RRID:SCR_017389) Copy   

•   Source: SciCrunch Registry

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

MGI GO project provides functional annotations for mouse gene products using Gene Ontology. Functional annotation using Gene Ontology (GO).

Proper citation: Functional Annotation (RRID:SCR_017519) Copy   

•   Source: SciCrunch Registry

https://4dgenome.research.chop.edu/

Repository for chromatin interaction data. Records can be queried by genomic regions, gene names, organism, and detection technology. Database is continuously updated by curators. Contributions from scientific community.

Proper citation: 4D Genome (RRID:SCR_017489) 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

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

https://amp.pharm.mssm.edu/geneshot/

Software tool as search engine for ranking genes from arbitrary text queries. Enables to enter arbitrary search terms, to receive ranked lists of genes relevant to search terms. Returned ranked gene lists contain genes that were previously published in association with search terms, as well as genes predicted to be associated with terms based on data integration from multiple sources. Search results are presented with interactive visualizations.

Proper citation: Geneshot (RRID:SCR_017582) 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

http://www.informatics.jax.org/mgihome/nomen/index.shtml

Authoritative source of official names for mouse genes, alleles, and strains. Nomenclature follows rules and guidelines established by International Committee on Standardized Genetic Nomenclature for Mice.

Proper citation: Nomenclature (RRID:SCR_017511) Copy   

•   Source: SciCrunch Registry

https://www.jax.org/research-and-faculty/resources/knockout-mouse-project

Information from JAX about their contributions to KOMP project coordinated by International Mouse Phenotyping Consortium. National Institutes of Health has funded three KOMP2 centers in United States, including one at Jackson Laboratory, to work together on task of producing and phenotyping mice to establish resource of knockout mice and related database of gene function.

Proper citation: Knockout Mouse Project Repository at JAX (RRID:SCR_017512) Copy   

•   Source: SciCrunch Registry

http://sydney.edu.au/medicine/bosch/facilities/molecular-biology/nucleic-acid/corbett-rotor-gene.php

Rotor-Gene real-time analysis software system for ROTOR GENE 6000 REAL-TIME PCR machine. Software has been refined to provide an intuitive, Wizard driven interface, enabling flexibility and automation. Build-in extensive analysis, graphing and statistical functions. Unlimited use software license for Windows XP, Pentium IV (2GHz) or higher PC.

Proper citation: Rotor-Gene 6000 series software (RRID:SCR_017552) Copy   

•   Source: SciCrunch Registry

https://nyumc.ilab.agilent.com/service_center/4273

Core offers services for researchers who want to apply advanced molecular genetic techniques in rodent models of physiology and disease. Provides expertise in generating novel mutant and transgenic mouse strains using genome engineering in mouse embryos and in embryonic stem cells (ESCs). Available technologies include:Generation of genome-edited mice by embryo pronuclear microinjection of DNA and genome editors (e.g., CRISPR/Cas9, site-specific recombinases) or traditional BAC transgenesis;Generation of genome-edited mice from mouse embryonic stem cells (mESCs) by chimeric blastocyst injection;Generation of genome-edited mice from mESCs by tetraploid blastocyst injection; Generation of mice from induced pluripotent stem cells;Assisted reproductive technologies; Sperm and embryo cryopreservation, storage and import/export;in vitro fertilization (IVF); Embryo rederivation technologies for animal import into barrier vivaria through quarantine.

Proper citation: NYU Langone’s Advanced Rodent Transgenics Laboratory ART-Lab Core Facility (RRID:SCR_017692) Copy   

•   Source: SciCrunch Registry

https://github.com/hariszaf/pema

Software as flexible pipeline for environmental DNA metabarcoding analysis of 16S/18S rRNA, ITS and COI marker genes. Performs reads’ pre-processing, clustering to (M)OTUs and taxonomy assignment for 16S rRNA and COI marker gene data. Allows users to explore alternative algorithms for specific steps of pipeline without need of complete re-execution.

Proper citation: PEMA (RRID:SCR_017676) Copy   

•   Source: SciCrunch Registry

https://github.com/shanglicheng/BootstrappingWithoutReplacement

Software tool to dig out more robust and reliable differentially expressed genes between two groups. Samples from different groups will be re-sampled randomly associated with total number of samples.

Proper citation: BootstrappingWithoutReplacement (RRID:SCR_017673) Copy   

•   Source: SciCrunch Registry

http://gbrowse.csbio.unc.edu/cgi-bin/gb2/gbrowse/slep/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Database of genetic and gene expression data from the published literature on psychiatric disorders. Users can search the accumulated data to find the evidence in support of the involvement of a particular genomic region with a set of important psychiatric disorders, ADHD, autism, bipolar disorder, eating disorder, major depressive disorder, schizophrenia, and smoking behavior. It contains findings from manual reviews of 144 papers in psychiatric genetics, 136 primary reports and 8 meta-analyses. Disorders covered include schizophrenia (44 papers), autism (24 papers), bipolar disorder (24 papers), smoking behavior (24 papers), major depressive disorder and neuroticism (14 papers), ADHD (8 papers), eating disorders (3 papers), and a combined schizophrenia-bipolar phenotype (3 papers). The unbiased searches integrated into SLEP include genomewide linkage (117 papers), genomewide association (15 papers), copy number variation (9 papers), and gene expression studies of post-mortem brain tissue (3 meta-analyses courtesy of the Stanley Foundation). In total, SLEP captures 3,741 findings from these 144 papers. SLEP also contains over 70,000 SignPosts. These annotations derive from many different sources and are designed to try to capture current state of knowledge about disease associations in the human genome. SignPosts can be searched simultaneously with the psychiatric genetics literature in order to integrate these two bodies of knowledge. The SignPosts include: accumulated GWAS findings from the human genetics literature, the OMIM database, candidate gene association study literature, CNV location and frequency data, SNPs that influence gene expression in brain, genes expressed in brain, genes with evidence of imprinting and random monoalleleic expression, genes mutated in breast or colorectal cancer, and pathway data from BioCyc.

Proper citation: Sullivan Lab Evidence Project (RRID:SCR_000753) Copy   

•   Source: SciCrunch Registry

http://gene64.dna.affrc.go.jp/RPD/main_en.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 22, 2016.

A database on the proteome of rice that contains reference maps based on two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) of proteins from rice tissues and subcellular compartments.

Proper citation: Rice Proteome Database (RRID:SCR_000743) Copy   

•   Source: SciCrunch Registry

http://www.biorag.org/index.php

Bio Resource for array genes is a free online resource for easy access to collective and integrated information from various public biological resources for human, mouse, rat, fly and c. elegans genes. The resource includes information about the genes that are represented in Unigene clusters. This resource provides interactive tools to selectively view, analyze and interpret gene expression patterns against the background of gene and protein functional information. Different query options are provided to mine the biological relationships represented in the underlying database. Search button will take you to the list of query tools available. This Bio resource is a platform designed as an online resource to assist researchers in analyzing results of microarray experiments and developing a biological interpretation of the results. This site is mainly to interpret the unique gene expression patterns found as biological changes that can lead to new diagnostic procedures and drug targets. This interactive site allows users to selectively view a variety of information about gene functions that is stored in an underlying database. Although there are other online resources that provide a comprehensive annotation and summary of genes, this resource differs from these by further enabling researchers to mine biological relationships amongst the genes captured in the database using new query tools. Thus providing a unique way of interpreting the microarray data results based on the knowledge provided for the cellular roles of genes and proteins. A total of six different query tools are provided and each offer different search features, analysis options and different forms of display and visualization of data. The data is collected in relational database from public resources: Unigene, Locus link, OMIM, NCBI dbEST, protein domains from NCBI CDD, Gene Ontology, Pathways (Kegg, Genmapp and Biocarta) and BIND (Protein interactions). Data is dynamically collected and compiled twice a week from public databases. Search options offer capability to organize and cluster genes based on their Interactions in biological pathways, their association with Gene Ontology terms, Tissue/organ specific expression or any other user-chosen functional grouping of genes. A color coding scheme is used to highlight differential gene expression patterns against a background of gene functional information. Concept hierarchies (Anatomy and Diseases) of MESH (Medical Subject Heading) terms are used to organize and display the data related to Tissue specific expression and Diseases. Sponsors: BioRag database is maintained by the Bioinformatics group at Arizona Cancer Center. The material presented here is compiled from different public databases. BioRag is hosted by the Biotechnology Computing Facility of the University of Arizona. 2002,2003 University of Arizona.

Proper citation: Bio Resource for Array Genes Database (RRID:SCR_000748) Copy   

•   Source: SciCrunch Registry