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http://www.cdtdb.brain.riken.jp/CDT/Top.jsp

Transcriptomic information (spatiotemporal gene expression profile data) on the postnatal cerebellar development of mice (C57B/6J & ICR). It is a tool for mining cerebellar genes and gene expression, and provides a portal to relevant bioinformatics links. The mouse cerebellar circuit develops through a series of cellular and morphological events, including neuronal proliferation and migration, axonogenesis, dendritogenesis, and synaptogenesis, all within three weeks after birth, and each event is controlled by a specific gene group whose expression profile must be encoded in the genome. To elucidate the genetic basis of cerebellar circuit development, CDT-DB analyzes spatiotemporal gene expression by using in situ hybridization (ISH) for cellular resolution and by using fluorescence differential display and microarrays (GeneChip) for developmental time series resolution. The CDT-DB not only provides a cross-search function for large amounts of experimental data (ISH brain images, GeneChip graph, RT-PCR gel images), but also includes a portal function by which all registered genes have been provided with hyperlinks to websites of many relevant bioinformatics regarding gene ontology, genome, proteins, pathways, cell functions, and publications. Thus, the CDT-DB is a useful tool for mining potentially important genes based on characteristic expression profiles in particular cell types or during a particular time window in developing mouse brains.

Proper citation: Cerebellar Development Transcriptome Database (RRID:SCR_013096) Copy   

•   Source: SciCrunch Registry

http://dorina.mdc-berlin.de/rbp_browser/dorina.html

In animals, RNA binding proteins (RBPs) and microRNAs (miRNAs) post-transcriptionally regulate the expression of virtually all genes by binding to RNA. Recent advances in experimental and computational methods facilitate transcriptome-wide mapping of these interactions. It is thought that the combinatorial action of RBPs and miRNAs on target mRNAs form a post-transcriptional regulatory code. We provide a database that supports the quest for deciphering this regulatory code. Within doRiNA, we are systematically curating, storing and integrating binding site data for RBPs and miRNAs. Users are free to take a target (mRNA) or regulator (RBP and/or miRNA) centric view on the data. We have implemented a database framework with short query response times for complex searches (e.g. asking for all targets of a particular combination of regulators). All search results can be browsed, inspected and analyzed in conjunction with a huge selection of other genome-wide data, because our database is directly linked to a local copy of the UCSC genome browser. At the time of writing, doRiNA encompasses RBP data for the human, mouse and worm genomes. For computational miRNA target site predictions, we provide an update of PicTar predictions.

Proper citation: doRiNA (RRID:SCR_013222) Copy   

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http://proline.bic.nus.edu.sg/dedb/

Database on Drosophila melanogaster exons presented in a splicing graph form. Data is based on release 3.2 of the Drosophila melanogaster genome annotations available at FlyBase. The gene structure information extracted from the annotations were checked, clustered and transformed into splicing graph. The splicing graph form of the gene constructs were then used for classification of the various types of alternative splicing events. In addition, Pfam domains were mapped onto the gene structure. Users can query the database using the query page using BLAST, FlyBase Gene Name, FlyBase Gene Symbol, Pfam Accession Number and Pfam Identifier. This allows users to determine the Drosophila melanogaster homology of their gene using a BLAST search and to visualize the alternative splicing variants if any. Users can also determine genes containing a particular domain using the Pfam Accession Numbers and Identifiers.

Proper citation: Drosophila melanogaster Exon Database (RRID:SCR_013441) Copy   

•   Source: SciCrunch Registry

http://toxodb.org/toxo/

A genome and functional genomic database for the protozoan parasite Toxoplasma gondii. It incorporates the sequence and annotation of the T. gondii ME49 strain, as well as genome sequences for the GT1, VEG and RH (Chr Ia, Chr Ib) strains. Sequence information is integrated with various other genomic-scale data, including community annotation, ESTs, gene expression and proteomics data. Organisms * Toxoplasma gondii (ME49, RH, GT1, Veg strains) * Neospora caninum * environmental isolate sequences from numerous species Tools * BLAST: Identify Sequence Similarities * Sequence Retrieval: Retrieve Specific Sequences using IDs and coordinates * PubMed and Entrez: View the Latest Toxoplasma, Neospora Pubmed and Entrez Results * Genome Browser: View Sequences and Features in the genome browser * Ancillary Genome Browse: Access Additional info like Probeset data and Toxoplasma Array info

Proper citation: ApiDB ToxoDB (RRID:SCR_013453) Copy   

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http://www.echinobase.org/

Database that provide a genomic information and comparative genomics platform on sea urchins and related echinoderms. It provide collection of information to directly support experimental work on these useful research models in cell and developmental biology.

Proper citation: EchinoBase (RRID:SCR_013732) Copy   

•   Source: SciCrunch Registry

http://www.kazusa.or.jp/kaos/

This site has been developed by Kazusa DNA Research Institute for the purpose of offering the science community the analyzed sequence data produced by a multi-national Arabidopsis genome sequencing project coordinated by the Arabidopsis Genome Initiatives (AGI). The aim of this service is to enable users to browse the annotated sequence data produced by all the sequencing teams of AGI through an user-friendly graphic display system and search engines. Gene structures proposed on the annotated sequences as well as those predicted by computer programs are presented and each graphic item has a hyperlink to detailed information of the corresponding area. The nucleotide sequence data deposited in GenBank by AGI was downloaded, re-computer-analyzed at Kazusa and parsed results are displayed graphically.

Proper citation: Kazusa Arabidopsis data opening site (RRID:SCR_013511) Copy   

•   Source: SciCrunch Registry

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

Searchable database of mouse genes, DNA segments, cytogenetic markers and QTLs. MGI provides access to integrated data on mouse genes and genome features, from sequences and genomic maps to gene expression and disease models.

Proper citation: Genes, Genome Features and Maps (RRID:SCR_017524) Copy   

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http://www.mirtoolsgallery.org/miRToolsGallery/node/1055

Comprehensive resource of microRNA target predictions and expression profiles. Used for whole genome prediction of miRNA target genes. For each miRNA, target genes are selected on basis of sequence complementarity using position weighted local alignment algorithm, free energies of RNA-RNA duplexes, and conservation of target sites in related genomes. Provides information about set of genes potentially regulated by particular microRNA, co-occurrence of predicted target sites for multiple microRNAs in mRNA and microRNA expression profiles in tissues. Users are allowed to customize algorithm, numerical parameters, and position-specific rules., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: miRanda (RRID:SCR_017496) Copy   

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http://www.mqtldb.org/

Data collection of large scale genome wide DNA methylation analysis of 1,000 mother-child pairs at serial time points across life course (ARIES).

Proper citation: mqtldb (RRID:SCR_018002) Copy   

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http://noble.gs.washington.edu/proj/genomedata/

A format for efficient storage of multiple tracks of numeric data anchored to a genome. The format allows fast random access to hundreds of gigabytes of data, while retaining a small disk space footprint. They have also developed utilities to load data into this format. Retrieving data from this format is more than 2900 times faster than a naive approach using wiggle files. A reference implementation in Python and C components is available here under the GNU General Public License. The software has only been tested on Linux and Mac systems.

Proper citation: Genomedata (RRID:SCR_004544) 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

http://bioinformatics.udel.edu/Research/skategenomeproject

Core facility provides a model for collaborative approaches to use specialized resources and expertise in an integrated process. Core builds on the expertise and resources provided by the Bioinformatics Cores of the five northeastern states that form NECC. The Skate Genome Annotation Workshops and Jamborees offer training and opportunities for faculty and students to work with and annotate genome sequences. Workshops include lectures, tutorials and exercises annotating the genome of the little skate, Leucoraja erinacea.

Proper citation: University of Delaware Skate Genome Project (RRID:SCR_005300) Copy   

•   Source: SciCrunch Registry

http://aws.amazon.com/1000genomes/

A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.

Proper citation: 1000 Genomes Project and AWS (RRID:SCR_008801) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/Projects/Fungi/

Fungal genomes available from the Sanger Institute. Data are accessible in a number of ways; for each organism there is a BLAST server, allowing search of the sequences. Sequences can also be down-loaded directly by FTP. In addition, for those organisms being sequenced using a cosmid approach, finished and annotated cosmids are submitted to EMBL and other public databases.

Proper citation: Fungi Sequencing Projects (RRID:SCR_008524) Copy   

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http://www.scripps.edu/florida/technologies/cbs/index.html

Core facility that provides access to genome-wide collections of cDNAs and siRNAs that can be used to interrogate cellular models of signal transduction pathways and phenotypes. Services include cell lines, hit-picking clones and various screening sets, and access to equipment.Provides instruments:Analyst Molecular Devices,Embla Molecular Devices, Envision Perkin Elmer, Platemate Matrix, Tecan M200, Wellmate Matrix.

Proper citation: Scripps Research Institute Florida Cell Based High Throughput Screening Core Facility (RRID:SCR_014877) Copy   

•   Source: SciCrunch Registry

https://github.com/Nextomics/NextPolish

Software tool to fix base errors SNV/Indel in genome generated by noisy reads. Used to correct error bases in reference genome.

Proper citation: NextPolish (RRID:SCR_025232) Copy   

•   Source: SciCrunch Registry

https://github.com/BackofenLab/HVSeeker/tree/main

Software tool for distinguishing between bacterial and phage sequences. Consists of two separate models: one analyzing DNA sequences and the other focusing on proteins.

Proper citation: HVSeeker (RRID:SCR_026120) Copy   

•   Source: SciCrunch Registry

http://www.genome.gov/27549169

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 30,2025. 2012 workshop to establish a Central Resource of Data from Genome Sequencing Projects. The workshop addressed the challenges to aggregating and analyzing data sets from genome sequencing studies, such as: * Data sets being generally hard to access. * Data residing in various databases. * Variant and exposure/phenotype data not being comparable across studies. Participants in the workshop discussed options for dealing with these challenges, along with their costs and tradeoffs. Videos and accompanying slides from the workshop are available. Also available as a video playlist on GenomeTV

Proper citation: NHGRI: Establishing a Central Resource of Data from Genome Sequencing Projects (RRID:SCR_003205) Copy   

•   Source: SciCrunch Registry

http://www.omicia.com/products/opal-research

Software which integrates a comprehensive, automated genome annotation engine with the VAAST and Phevor disease gene prioritization tools to rank gene variants on the severity of their impact on protein function and likelihood to cause disease. Each variant in a gene is analyzed for its impact on protein function, conservation and frequency. Each gene is ranked rather than filtered in order to ensure critical targets are not prematurely removed.

Proper citation: Opal Research (RRID:SCR_000405) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/cancer/cga/mutect

Software for the reliable and accurate identification of somatic point mutations in next generation sequencing data of cancer genomes.

Proper citation: MuTect (RRID:SCR_000559) Copy   

•   Source: SciCrunch Registry