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http://platform.invbrain.neuroinf.jp/

Database of information on nervous systems and behavior of various species of invertebrates and a large body of ancillary material to promote the use of invertebrate systems in research and education and facilitate information transfer to engineers that are looking for mechanisms that may be useful to solve a wide range of technological problems. The database is linked to explanations of the contents to allow users to familiarize themselves with the data and the context in which they were obtained. The platform has four entrance points tailored to different target user groups. The first entrance point is designed for users that are interested in using invertebrates for research purposes, in particular in the field of neuroscience, to assist them in initiating research projects. This includes databases of sensory systems, brains, and behavior of invertebrates, especially insects. The databases contain sensory organ structure and function, photographs and movies documenting insect behavior, data acquisition equipment and other instrumentation, software, material for eduction, and bibliography. A second entrance point is available for those that are concerned with implementations of design principles of invertebrate nervous systems and behavior in industrial applications. The third portal is destined for providing quick access for instructors that intend to use invertebrates for educational purposes and the remaining entrance point facilitates obtaining general comparative information on sensory and central nervous systems and behavior of invertebrates.

Proper citation: Invertebrate Brain Platform (RRID:SCR_006764) Copy   

•   Source: SciCrunch Registry

http://www.openbioinformatics.org/annovar/

An efficient software tool to utilize update-to-date information to functionally annotate genetic variants detected from diverse genomes (including human genome hg18, hg19, as well as mouse, worm, fly, yeast and many others). Given a list of variants with chromosome, start position, end position, reference nucleotide and observed nucleotides, ANNOVAR can perform: 1. gene-based annotation. 2. region-based annotation. 3. filter-based annotation. 4. other functionalities. (entry from Genetic Analysis Software)

Proper citation: ANNOVAR (RRID:SCR_012821) Copy   

•   Source: SciCrunch Registry

http://opossum.cisreg.ca

A web-based system for the detection of over-represented conserved transcription factor binding sites and binding site combinations in sets of genes or sequences.

Proper citation: oPOSSUM (RRID:SCR_010884) Copy   

•   Source: SciCrunch Registry

http://www.rcsb.org/#Category-welcome

Collection of structural data of biological macromolecules. Database of information about 3D structures of large biological molecules, including proteins and nucleic acids. Users can perform queries on data and analyze and visualize results.

Proper citation: Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) (RRID:SCR_012820) Copy   

•   Source: SciCrunch Registry

http://gsgator.ewha.ac.kr/

A web-based platform for functional interpretation of gene sets with features such as cross-species Gene Set Analysis (GSA), Flexible and Interactive GSA, simultaneous GSA for multiple gene set, and and a fully integrated network viewer for both visualizing GSA results and molecular networks.

Proper citation: gsGator (RRID:SCR_012035) Copy   

•   Source: SciCrunch Registry

http://compartments.jensenlab.org/Downloads

Web resource that integrates evidence on protein subcellular localization from manually curated literature, high-throughput screens, automatic text mining, and sequence-based prediction methods. All evidence is mapped to common protein identifiers and Gene Ontology terms, and further unify it by assigning confidence scores that facilitate comparison of the different types and sources of evidence and visualize these scores on a schematic cell.

Proper citation: COMPARTMENTS Subcellular localization database (RRID:SCR_015561) Copy   

•   Source: SciCrunch Registry

http://pstiing.icr.ac.uk/

A publicly accessible knowledgebase about protein-protein, protein-lipid, protein-small molecules, ligand-receptor interactions, receptor-cell type information, transcriptional regulatory and signal transduction modules relevant to inflammation, cell migration and tumourigenesis. It integrates in-house curated information from the literature, biochemical experiments, functional assays and in vivo studies, with publicly available information from multiple and diverse sources across human, rat, mouse, fly, worm and yeast. The knowledgebase allowing users to search and to dynamically generate visual representations of protein-protein interactions and transcriptional regulatory networks. Signalling and transcriptional modules can also be displayed singly or in combination. This allow users to identify important "cross-talks" between signalling modules via connections with key components or "hubs". The knowledgebase will facilitate a "systems-wide" understanding across many protein, signalling and transcriptional regulatory networks triggered by multiple environmental cues, and also serve as a platform for future efforts to computationally and mathematically model the system behavior of inflammatory processes and tumourigenesis.

Proper citation: pSTIING (RRID:SCR_002045) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/PHENOMEBLAST

A cross-species phenotype and anatomy ontology resulting from combining available anatomy and phenotype ontologies and their definitions. The ontology includes phenotype definitions for yeast, mouse, fish, worm, fly and human phenotypes and diseases.

Proper citation: PhenomeBLAST Ontology (RRID:SCR_005139) Copy   

•   Source: SciCrunch Registry

http://phenomebrowser.net/

PhenomeNet is a cross-species phenotype similarity network. It contains the experimentally observed phenotypes of multiple species as well as the phenotypes of human diseases. PhenomeNet provides a measure of phenotypic similarity between the phenotypes it contains. The latest release (from 22 June 2012) contains 124,730 complex phenotype nodes taken from the yeast, fish, worm, fly, rat, slime mold and mouse model organism databases as well as human disease phenotypes from OMIM and OrphaNet. The network is a complete graph in which edge weights represent the degree of phenotypic similarity. Phenotypic similarity can be used to identify and prioritize candidate disease genes, find genes participating in the same pathway and orthologous genes between species. To compute phenotypic similarity between two sets of phenotypes, we use a weighted Jaccard index. First, phenotype ontologies are used to infer all the implications of a phenotype observation using several phenotype ontologies. As a second step, the information content of each phenotype is computed and used as a weight in the Jaccard index. Phenotypic similarity is useful in several ways. Phenotypic similarity between a phenotype resulting from a genetic mutation and a disease can be used to suggest candidate genes for a disease. Phenotypic similarity can also identify genes in a same pathway or orthologous genes. PhenomeNet uses the axioms in multiple species-dependent phenotype ontologies to infer equivalent and related phenotypes across species. For this purpose, phenotype ontologies and phenotype annotations are integrated in a single ontology, and automated reasoning is used to infer equivalences. Specifically, for every phenotype, PhenomeNet infers the related mammalian phenotype and uses the Mammalian Phenotype Ontology for computing phenotypic similarity. Tools: * PhenomeBLAST - A tool for cross-species alignments of phenotypes * PhenomeDrug - method for drug-repurposing

Proper citation: phenomeNET (RRID:SCR_006165) Copy   

•   Source: SciCrunch Registry

http://marrvel.org/

Web tool to search multiple public variant databases simultaneously and provide a unified interface to facilitate the search process. Used for integration of human and model organism genetic resources to facilitate functional annotation of the human genome. Used for analysis of human genes and variants by cross-disciplinary integration of records available in public databases to facilitate clinical diagnosis and basic research.

Proper citation: MARRVEL (RRID:SCR_016871) Copy   

•   Source: SciCrunch Registry

http://www.webgestalt.org/

Web based gene set analysis toolkit designed for functional genomic, proteomic, and large-scale genetic studies from which large number of gene lists (e.g. differentially expressed gene sets, co-expressed gene sets etc) are continuously generated. WebGestalt incorporates information from different public resources and provides a way for biologists to make sense out of gene lists. This version of WebGestalt supports eight organisms, including human, mouse, rat, worm, fly, yeast, dog, and zebrafish.

Proper citation: WebGestalt: WEB-based GEne SeT AnaLysis Toolkit (RRID:SCR_006786) Copy   

•   Source: SciCrunch Registry

http://targetscan.org/

Web tool to predict biological targets of miRNAs by searching for presence of conserved 8mer, 7mer and 6mer sites that match seed region of each miRNA. Nonconserved sites are also predicted and sites with mismatches in seed region that are compensated by conserved 3' pairing. Used to search for predicted microRNA targets in mammals.

Proper citation: TargetScan (RRID:SCR_010845) Copy   

•   Source: SciCrunch Registry

http://genomics.senescence.info/genes/

Collection of annotated and manually curated data of genes related to aging divided into genes related to longevity and/or aging in model organisms (yeast, worms, flies, mice, etc.) and aging related human genes.

Proper citation: GenAge (RRID:SCR_010223) Copy   

•   Source: SciCrunch Registry

https://dfam.org/home

Open collection of Transposable Element DNA sequence alignments, hidden Markov Models, consensus sequences, and genome annotations.Dfam 3.2 provides early access to uncurated, de novo generated families.

Proper citation: Dfam (RRID:SCR_021168) Copy   

•   Source: SciCrunch Registry

http://bowtie-bio.sourceforge.net/recount/

RNA-seq gene count datasets built using the raw data from 18 different studies. The raw sequencing data (.fastq files) were processed with Myrna to obtain tables of counts for each gene. For ease of statistical analysis, they combined each count table with sample phenotype data to form an R object of class ExpressionSet. The count tables, ExpressionSets, and phenotype tables are ready to use and freely available. By taking care of several preprocessing steps and combining many datasets into one easily-accessible website, we make finding and analyzing RNA-seq data considerably more straightforward.

Proper citation: ReCount - A multi-experiment resource of analysis-ready RNA-seq gene count datasets (RRID:SCR_001774) Copy   

•   Source: SciCrunch Registry