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http://www.janelia.org/team-project/fly-em

A project producing datasets, software, and algorithms that is developing the technology to produce connectomes at the electron microscopic level of behaviorally-relevant neural circuits as well as the entire Drosophila nervous system. This technology will enable them to create a map of every neuron and synapse in the Drosophila nervous system, using novel approaches to electron microscopy (EM) as the foundation. In the same way that the fly genome paved the way for larger projects, including sequencing the human genome, Fly EM may ultimately contribute to our understanding of the human brain by establishing a fly "connectome" a map that shows how all neurons in the fly brain are connected to each other. They began their entry into EM reconstruction with the fly's adult visual system, where much is known about cell types from previous EM and histological studies, as well as ongoing studies in the Fly Light Project. In addition to establishing and publishing a fly connectome, Fly EM will make technology and methodology available that is needed to perform large-scale EM reconstructions. Fly EM will generally pursue an open policy with their datasets, software, and algorithms after relevant publications. When an EM reconstruction is published, the derived connectome and reconstructed neuronal skeletons will be made available online. The raw data and annotatations will be made available upon request as logistics dictate. To encourage further collaboration and scientific discovery, a small fraction of their raw data and corresponding segmentation will be made available independent of publication. Their goal is to enable others who wish to approach the many algorithmic challenges, but who do not have access to an EM facility, to have the data they need to support methods development, as well as their results to use as a benchmark. Fly EM emphasizes publication of supporting techniques and software approaches before major EM reconstruction releases to encourage rapid feedback from the community and adoption of their strategies. FlyEM maintains much of its software in the open-source repository GitHub:http://janelia-flyem.github.com. They will provide information on official release versions of these packages on git-hub when it reaches reasonable maturity.

Proper citation: Fly EM (RRID:SCR_002242) Copy   

•   Source: SciCrunch Registry

https://www.genevestigator.com/gv/

A high performance search engine for gene expression that integrates thousands of manually curated public microarray and RNAseq experiments and nicely visualizes gene expression across different biological contexts (diseases, drugs, tissues, cancers, genotypes, etc.). There are two basic analysis approaches: # for a gene of interest, identify which conditions affect its expression. # for condition(s) of interest, identify which genes are specifically expressed in this/these conditions. Genevestigator builds on the deep integration of data, both at the level of data normalization and on the level of sample annotations. This deep integration allows scientists to ask new types of questions that cannot be addressed using conventional tools.

Proper citation: Genevestigator (RRID:SCR_002358) Copy   

•   Source: SciCrunch Registry

http://crcview.hegroup.org/

Web-based microarray data analysis and visualization system powered by CRC, or Chinese Restaurant cluster, a Dirichlet process model-based clustering algorithm recently developed by Dr. Steve Qin. It also incorporates several gene expression analysis programs from Bioconductor, including GOStats, genefilter, and Heatplus. CRCView also installs from the Bioconductor system 78 annotation libraries of microarray chips for human (31), mouse (24), rat (14), zebrafish (1), chicken (1), Drosophila (3), Arabidopsis (2), Caenorhabditis elegans (1), and Xenopus Laevis (1). CRCView allows flexible input data format, automated model-based CRC clustering analysis, rich graphical illustration, and integrated Gene Ontology (GO)-based gene enrichment for efficient annotation and interpretation of clustering results. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. You need to register for an account in the system to store your data and analyses. The data and results can be visited again anytime you log in.

Proper citation: CRCView (RRID:SCR_007092) Copy   

•   Source: SciCrunch Registry

http://text0.mib.man.ac.uk/software/mldic/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 9, 2022. System that retrieves relevant UniProt IDs from BioThesaurus entries using a soft string matching algorithm.

Proper citation: Smart Dictionary Lookup (RRID:SCR_000568) Copy   

•   Source: SciCrunch Registry