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http://www.brain-map.org/api/index.html

API and demo application for accessing the Allen Brain Atlas Mouse Brain data. Data available via the API includes download high resolution images, expression data from a 3D volume, 3D coordinates of the Allen Reference Atlas, and searching genes with similar gene expression profiles using NeuroBlast. Data made available includes: * High resolution images for gene expression, connectivity, and histology experiments, as well as annotated atlas images * 3-D expression summaries registered to a reference space for the Mouse Brain and Developing Mouse Brain * Primary microarray results for the Human Brain and Non-Human Primate * RNA sequencing results for the Developing Human Brain * MRI and DTI files for Human Brain The API consists of the following resources: * RESTful model access * Image download service * 3-D expression summary download service * Differential expression search services * NeuroBlast correlative searches * Image-to-image synchronization service * Structure graph download service

Proper citation: Allen Brain Atlas API (RRID:SCR_005984) Copy   

•   Source: SciCrunch Registry

http://athina.biol.uoa.gr/bioinformatics/GENEVITO/

A JAVA-based computer application that serves as a workbench for genome-wide analysis through visual interaction. GeneViTo offers an inspectional view of genomic functional elements, concerning data stemming both from database annotation and analysis tools for an overall analysis of existing genomes. The application deals with various experimental information concerning both DNA and protein sequences (derived from public sequence databases or proprietary data sources) and meta-data obtained by various prediction algorithms, classification schemes or user-defined features. Interaction with a Graphical User Interface (GUI) allows easy extraction of genomic and proteomic data referring to the sequence itself, sequence features, or general structural and functional features. Emphasis is laid on the potential comparison between annotation and prediction data in order to offer a supplement to the provided information, especially in cases of poor annotation, or an evaluation of available predictions. Moreover, desired information can be output in high quality JPEG image files for further elaboration and scientific use. GeneViTo has already been applied to visualize the genomes of two microbial organisms: the bacterion Chlamydia trachomatis and the archaeon Methanococcus jannaschii. The application is compatible with Linux or Windows ME-2000-XP operating systems, provided that the appropriate Java Runtime Environment (Java 1.4.1) is already installed in the system.

Proper citation: GeneVito (RRID:SCR_006211) Copy   

•   Source: SciCrunch Registry

http://mango.adult-neurogenesis.de

Database of genes concerning adult neurogenesis mapped to cell types and processes that have been curated from the literature. In its present state, the database is restricted to neurogenesis in the hippocampus.

Proper citation: Mammalian Adult Neurogenesis Gene Ontology (RRID:SCR_006176) Copy   

•   Source: SciCrunch Registry

http://www.ddduk.org/

The Deciphering Developmental Disorders (DDD) study aims to find out if using new genetic technologies can help doctors understand why patients get developmental disorders. To do this we have brought together doctors in the 23 NHS Regional Genetics Services throughout the UK and scientists at the Wellcome Trust Sanger Institute, a charitably funded research institute which played a world-leading role in sequencing (reading) the human genome. The DDD study involves experts in clinical, molecular and statistical genetics, as well as ethics and social science. It has a Scientific Advisory Board consisting of scientists, doctors, a lawyer and patient representative, and has received National ethical approval in the UK. Over the next few years, we are aiming to collect DNA and clinical information from 12,000 undiagnosed children in the UK with developmental disorders and their parents. The results of the DDD study will provide a unique, online catalogue of genetic changes linked to clinical features that will enable clinicians to diagnose developmental disorders. Furthermore, the study will enable the design of more efficient and cheaper diagnostic assays for relevant genetic testing to be offered to all such patients in the UK and so transform clinical practice for children with developmental disorders. Over time, the work will also improve understanding of how genetic changes cause developmental disorders and why the severity of the disease varies in individuals. The Sanger Institute will contribute to the DDD study by performing genetic analysis of DNA samples from patients with developmental disorders, and their parents, recruited into the study through the Regional Genetics Services. Using microarray technology and the latest DNA sequencing methods, research teams will probe genetic information to identify mutations (DNA errors or rearrangements) and establish if these mutations play a role in the developmental disorders observed in patients. The DDD initiative grew out of the groundbreaking DECIPHER database, a global partnership of clinical genetics centres set up in 2004, which allows researchers and clinicians to share clinical and genomic data from patients worldwide. The DDD study aims to transform the power of DECIPHER as a diagnostic tool for use by clinicians. As well as improving patient care, the DDD team will empower researchers in the field by making the data generated securely available to other research teams around the world. By assembling a solid resource of high-quality, high-resolution and consistent genomic data, the leaders of the DDD study hope to extend the reach of DECIPHER across a broader spectrum of disorders than is currently possible.

Proper citation: Deciphering Developmental Disorders (RRID:SCR_006171) Copy   

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http://kobas.cbi.pku.edu.cn/

Web server to identify statistically enriched pathways, diseases, and GO terms for a set of genes or proteins, using pathway, disease, and GO knowledge from multiple famous databases. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). A standalone command line version is also available, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: KOBAS (RRID:SCR_006350) Copy   

•   Source: SciCrunch Registry

https://health.uconn.edu/worm-lab/track-a-worm/

Open source system for quantitative assessment of C. Elegans locomotory and bending behavior. Used for quantitative behavioral analyses to understand circuit and gene bases of behavior. Constantly records and analyzes position and body shape of freely moving worm at high magnification.

Proper citation: Track-A-Worm (RRID:SCR_018299) Copy   

•   Source: SciCrunch Registry

https://dice-database.org

Database of Immune Cell Expression, Expression quantitative trait loci (eQTLs) and Epigenomics. Collection of identified cis-eQTLs for 12,254 unique genes, which represent 61% of all protein-coding genes expressed in human cell types. Datasets to help reveal effects of disease risk associated genetic polymorphisms on specific immune cell types, providing mechanistic insights into how they might influence pathogenesis.

Proper citation: Database of Immune Cell Epigenomes (RRID:SCR_018259) Copy   

•   Source: SciCrunch Registry

https://metacpan.org/pod/Bio::CUA

Software tool as flexible and comprehensive codon usage analyzer. Used to analyze codon usage bias (CUB) and relevant problems.

Proper citation: Codon Usage Analyzer (RRID:SCR_018500) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/minet/

Open source software R package for inferring large transcriptional networks using mutual information. Implements algorithms for inferring networks such as gene networks from microarray data.

Proper citation: minet (RRID:SCR_018661) Copy   

•   Source: SciCrunch Registry

https://mygene.info/

Web service for querying or retrieving gene annotation data.

Proper citation: MyGene.info (RRID:SCR_018660) Copy   

•   Source: SciCrunch Registry

https://factory.euromov.eu/sml/index.php

Open source Java library dedicated to semantic measures computation and analysis. Tools based on the SML are also provided through the SML-Toolkit, a command line software giving access to some of the functionalities of the library. The SML and the toolkit can be used to compute semantic similarity and semantic relatedness between semantic elements (e.g. concepts, terms) or entities semantically characterized (e.g. entities defined in a semantic graph, documents annotated by concepts defined in an ontology).

Proper citation: Semantic Measures Library (RRID:SCR_001383) Copy   

•   Source: SciCrunch Registry

http://talasso.cnb.csic.es/

Tool for quantification of human miRNA-mRNA Interactions. TaLasso is also available as Matlab or R code.

Proper citation: TaLasso (RRID:SCR_001726) Copy   

•   Source: SciCrunch Registry

https://www.hgsc.bcm.edu/content/sea-urchin-genome-project

Provides informationa about Genome of California Purple Sea Urchin, one species (Strongylocentrotus purpuratus) of which has been sequenced and annotated by Sea Urchin Genome Sequencing Consortium led by HGSC. Reports sequence and analysis of genome of sea urchin Strongylocentrotus purpuratus, a model for developmental and systems biology.

Proper citation: Sea Urchin Genome Project (RRID:SCR_001735) Copy   

•   Source: SciCrunch Registry

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http://www.nitrc.org/projects/plink

Open source whole genome association analysis toolset, designed to perform range of basic, large scale analyses in computationally efficient manner. Used for analysis of genotype/phenotype data. Through integration with gPLINK and Haploview, there is some support for subsequent visualization, annotation and storage of results. PLINK 1.9 is improved and second generation of the software.

Proper citation: PLINK (RRID:SCR_001757) Copy   

•   Source: SciCrunch Registry

http://www.oege.org/

Portal for researchers to locate information relevant to interpretation and follow-up of human genetic epidemiological discoveries, including: a range of population and case and family genetic epidemiological studies, relevant gene and sequence databases, genetic variation databases, trait measurement, resource labs, journals, software, general information, disease genes and genetic diversity.

Proper citation: Online Encyclopedia for Genetic Epidemiology studies (RRID:SCR_001825) Copy   

•   Source: SciCrunch Registry

http://meme-suite.org/

Suite of motif-based sequence analysis tools to discover motifs using MEME, DREME (DNA only) or GLAM2 on groups of related DNA or protein sequences; search sequence databases with motifs using MAST, FIMO, MCAST or GLAM2SCAN; compare a motif to all motifs in a database of motifs; associate motifs with Gene Ontology terms via their putative target genes, and analyze motif enrichment using SpaMo or CentriMo. Source code, binaries and a web server are freely available for noncommercial use.

Proper citation: MEME Suite - Motif-based sequence analysis tools (RRID:SCR_001783) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/biomodels-main/

Repository of mathematical models of biological and biomedical systems. Hosts selection of existing literature based physiologically and pharmaceutically relevant mechanistic models in standard formats. Features programmatic access via Web Services. Each model is curated to verify that it corresponds to reference publication and gives proper numerical results. Curators also annotate components of models with terms from controlled vocabularies and links to other relevant data resources allowing users to search accurately for models they need. Models can be retrieved in SBML format and import/export facilities are being developed to extend spectrum of formats supported by resource.

Proper citation: BioModels (RRID:SCR_001993) Copy   

•   Source: SciCrunch Registry

http://www.aspgd.org/

Database of genetic and molecular biological information about the filamentous fungi of the genus Aspergillus including information about genes and proteins of Aspergillus nidulans and Aspergillus fumigatus; descriptions and classifications of their biological roles, molecular functions, and subcellular localizations; gene, protein, and chromosome sequence information; tools for analysis and comparison of sequences; and links to literature information; as well as a multispecies comparative genomics browser tool (Sybil) for exploration of orthology and synteny across multiple sequenced Sgenus species. Also available are Gene Ontology (GO) and community resources. Based on the Candida Genome Database, the Aspergillus Genome Database is a resource for genomic sequence data and gene and protein information for Aspergilli. Among its many species, the genus contains an excellent model organism (A. nidulans, or its teleomorph Emericella nidulans), an important pathogen of the immunocompromised (A. fumigatus), an agriculturally important toxin producer (A. flavus), and two species used in industrial processes (A. niger and A. oryzae). Search options allow you to: *Search AspGD database using keywords. *Find chromosomal features that match specific properties or annotations. *Find AspGD web pages using keywords located on the page. *Find information on one gene from many databases. *Search for keywords related to a phenotype (e.g., conidiation), an allele (such as veA1), or an experimental condition (e.g., light). Analysis and Tools allow you to: *Find similarities between a sequence of interest and Aspergillus DNA or protein sequences. *Display and analyze an Aspergillus sequence (or other sequence) in many ways. *Navigate the chromosomes set. View nucleotide and protein sequence. *Find short DNA/protein sequence matches in Aspergillus. *Design sequencing and PCR primers for Aspergillus or other input sequences. *Display the restriction map for a Aspergillus or other input sequence. *Find similarities between a sequence of interest and fungal nucleotide or protein sequences. AspGD welcomes data submissions.

Proper citation: ASPGD (RRID:SCR_002047) Copy   

•   Source: SciCrunch Registry

http://www.sanbi.ac.za/resources/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23, 2022. The South African National Bioinformatics Institute delivers biomedical discovery appropriate to both international and African context. Researchers at SANBI perform the highest level of research and provide excellence in education. Research at SANBI has set well recognized milestones in the field of computational biology. The tools and techniques used have not only been developed but also implemented across heterogeneous domains of advanced research. Local and international efforts have driven our discoveries. Until recently, the core of SANBIs research has focused upon gene expression biology. Methods developed and applied at SANBI revolve around a greater understanding of the underlying causes of diseases. SANBI approaches the problem by comparison of genes, genomes and transcriptomes. It uses computational gene expression biology to create novel biological insights and to provide biomarkers for experimental validation. It also performs analysis of human genome variation, transcriptional diversity on both the expression and splicing level and the unravelling of transcriptional regulatory networks. Resources - Hinv, STACKdb, Malaria resources and Trypanosome databases are available for on-line seaching. - SANBI offers WCD, STACKdb, stackPACK and eVOC and the eVOKE viewer as tools that can be downloaded. Sponsors: SANBI receives funding and support from a range of organisations in South Africa and Internationally. Organisations currently supporting SANBI include: South Africa * South African Medical Research Council * South African AIDS Vaccine Initiative * National Bioinformatics Network * National Research Foundation * Claude Leon Foundation * International Business Machines Inc. Europe * European Unions 6th Framework Programme * World Health Organization USA * US National Institutes of Health * Fogarty International Centre * Ludwig Institute for Cancer Research

Proper citation: South African National Bioinformatics Institute: Resources (RRID:SCR_001867) Copy   

•   Source: SciCrunch Registry

http://pga.gs.washington.edu

The SeattleSNPs PGA is focused on identifying, genotyping, and modeling the associations between single nucleotide polymorphisms (SNPs) in candidate genes and pathways that underlie inflammatory responses in humans. SeattleSNPs is focused on variation analysis in genes related to the inflammatory response. These gene targets are found in specific pathways and from interacting molecules contributing to this response. Available Resources: - Baseline assembled and complete genomic sequence and chromosomal location for candidate gene targets - Mapping of exon and repeat structure for candidate genes - Amplification primers and conditions - SNPs mapped by location in gene structure - SNPs with immediate surrounding sequence for genotype assay design - Genotypes and relative allele frequencies of the SNPs - Special features of SNPs - location (5', coding, etc.), amino acid substitutions, recurrent variation - Manuals on all protocols, data analysis procedures, and use of software tools - Workshop on genetic variation analysis and a gene submission program for variation analysis Sponsors: SeattleSNPs is funded as part of the National Heart Lung and Blood Institute's (NHLBI) Programs for Genomic Applications (PGA).

Proper citation: SeattleSNPs - Variation Discovery Resource (RRID:SCR_001859) Copy   

•   Source: SciCrunch Registry