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Showing 20 out of 29,142 Resources on page 1364

The Alternatve Splicing Database

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. The Alternative Splicing and Transcript Diversity (ASTD) database project is creating a database of alternative splice events and transcripts of genes from human, mouse and rat. Full length transcripts are generated with the aim of understanding the mechanism of alternative splicing on a genome-wide scale. The current release of the human genome consists of: 16715 genes, 14101 have more than one splice isoform, with an average of 5.6 splice patterns per gene. 10831 transcripts are annotated as full length with a transcription start site and a poly(A). The current release of the mouse genome consists of: 16491 genes, 13028 have more than one splice isoform, with an average of 4 splice patterns per gene. 6011 transcripts are annotated as full length with a transcription start site and a poly(A). The current release of the rat genome consists of: 10424 genes, 6344 have more than one splice isoform, with an average of 2.6 splice patterns per gene. 1250 transcripts are annotated as full length with a transcription start site and a poly(A). Sponsors: The ASTD project at EBI is supported by a grant from the EC: Eurasnet Network of Excellence (LSHG-CT-2005-518238). It was also supported by the ASD grant from the EC (QLRT-CT-2001-02062) until November 2005 and the ATD grant from the EC (LSHG-CT-2003-503329) until May 2007.

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  • 17 years ago - by Anonymous

EPMBA.ORG: Electronic Prenatal Mouse Brain Atlas

The Electronic Prenatal Mouse Brain Atlas, EPMBA, at present consists of two sets of annotated images of coronal sections from Gestational Day (GD) 12 heads and GD 16 brains of C57BL/6J mice. Ten micron thick sections were stained with hematoxylin and eosin. Images were prepared at various resolutions for annotations and for high resolution presentation. A subset of sections were annotated and linked to anatomical terms. Additionally, horizontal sections of a GD 12 head were aligned and re-assembled into a 3D volume for digital sectioning in arbitrarily oblique planes. These images were captured using a Nikon E800 stereomicroscope with a 10X objective. The resolution is 1.35 pixels/micrometer. The PC program used to grab the images, Microbrightfield's Neurolucida (version 6), stitched together a mosaic of between 10 and 50 high-res images for each tissue slice, while the user focused the scope for each mosaic tile. Since the nature of optic lenses is to focus on one central point, it was difficult to obtain a uniformly-focused field of vision; as such, small areas of these images are blurred. Images were then transferred to a Macintosh and processed in Adobe Photoshop (version 7). Color levels were adjusted for maximum clarity of the tissue, and areas surrounding the tissue were cleared of artifacts. Each image is approximately 3350 pixels wide by 2650 pixels high. A scale bar with a length of 1350 pixels/mm is visible in the lower right-hand corner of each image. The annotations have been completed for the Atlas of Developing Mouse Brain Gestational (Embryonic) Day 12 (7/5/07) as well as the Atlas of Developing Mouse Brain Embryonic Day 16 (4/26/07). The 3D EPMBA data set has been mounted on a NeuroTerrain Atlas Server (NtAS). (6/27/07).

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  • 17 years ago - by Anonymous

DAVID

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025. Bioinformatics resource system including web server and web service for functional annotation and enrichment analyses of gene lists. Consists of comprehensive knowledgebase and set of functional analysis tools. Includes gene centered database integrating heterogeneous gene annotation resources to facilitate high throughput gene functional analysis.

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  • 14 years ago - by Anonymous

Aspergillus Genomes

A resource for viewing annotated genes arising from various Aspergillus sequencing and annotation projects, resulting from the merging of Central Aspergillus Data REpository (CADRE) and The Aspergillus Website, which took place in June 2008. The principal role of CADRE is to aid the Aspergillus research community by managing Aspergillus genome data and by providing visualization tools, ranging from relatively simple annotation displays to more complex data integration displays. In contrast, The Aspergillus Website provides a range of information to the medical community (i.e., clinicians, patients and scientists) regarding the genus Aspergillus and the diseases, such as Aspergillosis, that it can cause. CADRE has been implemented using the Ensembl v22 suite. This suite comprises: * a database schema, which has been devised for storing annotated eukaryotic genomes. The schema is implemented with the MySQL relational database management system. * several specialized programming modules for building interfaces (i.e., BioPerl and Ensembl API modules). * a series of programs (i.e., Perl CGI scripts using the API modules) for viewing genomic data within a web browser., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

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  • 17 years ago - by Anonymous

flowUtils

Software that provides utilities for flow cytometry data.

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  • 12 years ago - by Anonymous

Degust

An interactive web tool for visualizing differential gene expression data.

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  • 13 years ago - by Anonymous

Flybrain at Stanford

Project content including raw image data, neuronal tracings, image registration tools and analysis scripts covering three manuscripts: Comprehensive Maps of DrosophilaHigher Olfactory Centres : Spatially Segregated Fruit and Pheromone Representation which uses single cell labeling and image registration to describe the organization of the higher olfactory centers of Drosophila; Diversity and wiring variability of olfactory local interneurons in the Drosophila antennal lobe which uses single cell labeling to describe the organization of the antennal lobe local interneurons; and Sexual Dimorphism in the Fly Brain which uses clonal analysis and image registration to identify a large number of sex differences in the brain and VNC of Drosophila. Data * Raw Data of Reference Brain (pic, amira) (both seed and average) * Label field of LH and MB calyx and surfaces for these structures * Label field of neuropil of Reference Brain * Traces (before and after registration). Neurolucida, SWC and AmiraMesh lineset. * MB and LH Density Data for different classes of neuron. In R format and as separate amira files. * Registration files for all brains used in the study * MBLH confocal images for all brains actually used in the study (Biorad pic format) * Sample confocal images for antennal lobe of every PN class * Confocal stacks of GABA stained ventral PNs Programs * ImageJ plugins (Biorad reader /writer/Amira reader/writer/IGS raw Reader) * Binary of registration, warp and gregxform (macosx only, others on request) * Simple GUI for registration tools (macosx only at present) * R analysis/visualization functions * Amira Script to show examples of neuronal classes The website is a collaboration between the labs of Greg Jefferis and Liqun Luo and has been built by Chris Potter and Greg Jefferis. The core Image Registration tools were created by Torsten Rohlfing and Calvin Maurer.

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  • 15 years ago - by Anonymous

GATK

A software package to analyze next-generation resequencing data. The toolkit offers a wide variety of tools, with a primary focus on variant discovery and genotyping as well as strong emphasis on data quality assurance. Its robust architecture, powerful processing engine and high-performance computing features make it capable of taking on projects of any size. This software library makes writing efficient analysis tools using next-generation sequencing data very easy, and second it's a suite of tools for working with human medical resequencing projects such as 1000 Genomes and The Cancer Genome Atlas. These tools include things like a depth of coverage analyzers, a quality score recalibrator, a SNP/indel caller and a local realigner. (entry from Genetic Analysis Software)

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  • 14 years ago - by Anonymous

TCW

Software package for assembling, annotating, querying, and comparing transcript and expression level data that consists of two parts: * singleTCW (sTCW): Single transcript sets or assemblies; annotation; differential expression (EdgeR, DEGSeq, DESeq, GoSeq) * multiTCW (mTCW): Comparison of multiple transcript sets; ortholog grouping (e.g., OrthoMCL) It has been tested on Linux and uses Java, mySQL and optionally R.

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  • 13 years ago - by Anonymous

Stanford Genomic Resourses

This resource hyperlinks to systematic analysis projects, resources, laboratories, and departments at Stanford University.

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  • 17 years ago - by Anonymous

Stable Isotope Labeling with Amino Acids in Cell Culture

Stable isotope labeling with amino acids in cell culture (SILAC) is a simple and straightforward approach for in vivo incorporation of a label into proteins for mass spectrometry (MS)-based quantitative proteomics. SILAC relies on metabolic incorporation of a given "light" or "heavy" form of the amino acid into the proteins. The method relies on the incorporation of amino acids with substituted stable isotopic nuclei (e.g. deuterium, 13C, 15N). In an experiment, two cell populations are grown in culture media that are identical except that one of them contains a "light" and the other a "heavy" form of a particular amino acid (e.g. 12C and 13C labeled L-lysine, respectively). When the labeled analog of an amino acid is supplied to cells in culture instead of the natural amino acid, it is incorporated into all newly synthesized proteins. After a number of cell divisions, each instance of this particular amino acid will be replaced by its isotope labeled analog. Since there is hardly any chemical difference between the labeled amino acid and the natural amino acid isotopes, the cells behave exactly like the control cell population grown in the presence of normal amino acid. It is efficient and reproducible as the incorporation of the isotope label is 100%. SILAC Applications: - Differential expression of proteins and identification of disease biomarkers - Cell signaling dynamics - Analysis of yeast pheromone signaling pathway - Identification of methylation sites - Identification of protease substrates - Study of protein complexes/protein interactions - Analysis of signaling pathways and effect of pharmacological inhibitors - Subcellular proteomics Sponsors: Supported in part by an NIH Roadmap grant Technology Center for Networks &amp; Pathways of Lysine Modification.

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  • 17 years ago - by Anonymous

AutDB

Curated public database for autism research built on information extracted from the studies on molecular genetics and biology of Autism Spectrum Disorders (ASD). The genetic information includes data from linkage and association studies, cytogenetic abnormalities, and specific mutations associated with ASD. New gene submissions are welcome. Modules: * Human Gene: thoroughly annotated list of genes that have been studied in the context of autism, with information on the genes themselves, relevant references from the literature, and the nature of the evidence. Uniquely, SFARI Gene incorporates information on both common and rare variants. * Animal Model: information about lines of genetically modified mice that represent potential models of autism. This information includes the nature of the targeting construct, the background strain and, most importantly, a thorough summary of the phenotypic features of the mice that are most relevant to autism. * Protein Interaction (PIN): compilation of all known direct protein interactions for those gene products implicated in autism. It presents both graphical and tabular views of interactomes, highlighting connections between autism candidate genes. Each protein interaction is manually verified by consultation with the primary reference. * Copy Number Variant (CNV): a parallel resource providing genetic information about all known copy number variants linked to autism. * Gene Scoring: includes a "score" for each autism candidate gene, based on an assessment of the strength of human genetic evidence.

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  • 13 years ago - by Anonymous

Korean Ministry of Education Science and Technology

THIS RESOURCE IS NO LONGER IN SERVICE, documented April 11, 2017. Division within the South Korean government that handled affairs of education and science. It has since been split into Ministry of Science, ICT and Future Planning and Ministry of Education.

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  • 13 years ago - submitted by Andrea Stagg

Sparse Learning with Efficient Projections

Software package that provides functions for solving a family of sparse learning algorithms. The functions implemented enjoy the convergence rate of O(1/k^2), although the objective function is non-smooth. Main features: * First-Order Method. At each iteration, they only need to evaluate the function value and the gradient; and thus the algorithms can handle large-scale sparse data. * Optimal Convergence Rate. The convergence rate O(1/k^2) is optimal for smooth convex optimization via the first-order black-box methods. * Efficient Projection. The projection problem (proximal operator) can be solved efficiently. * Pathwise Solutions. The SLEP package provides functions that efficiently compute the pathwise solutions corresponding to a series of regularization parameters by the warm-start technique.

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  • 13 years ago - by Anonymous

SPRUSTON / KATH LAB: Neuraling Modeling Database NEURAL MODELING DATABASE

This database contains morphologies of hippocampal pyramidal cells and interneurons (in Neurolucida, NEURON, and pdf formats) as well as data recorded from those cells. Sponsors:This work was supported by grants from the NIH (T32-GM-08061 to T.J.M., F32-NS-10532 to N.L.G., and R01-NS35180 and R01-NS 46064 to N.S. and W.L.K.) and NSF (IGERT fellowship to Y.K.). NS46064 is part of the NSF/NIH Collaborative Research in Computational Neuroscience Program

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  • 17 years ago - by Anonymous

Spike Train Analysis Software by Attila Szucs: Orbital Spike 4

Orbital Spike is a tool for time series analysis. It contains a wide range of methods to analyze data from point processes such as spike arrival times, heart beats or other behavioral episodes. It is optimized this program for spike trains but it works with other types of data, too. The program can analyze up to 8 channels recorded simultaneously each containing a maximum of 132,000 events (spikes). Assuming an average firing rate of 10 Hz for a neuron, you can then analyze a time series of approximately 3 and half hours long. There are up to 8 panels shown in the Orbital Spike desktop. The panels will contain the kind of data of interest. The graphs are associated with a bunch of parameters like window width, bin size, resolution, delay etc. All these parameters are listed in the parameter box, which appears on the right side of the desktop. It is pretty easy to change the parameters and what is nice, the corresponding graph(s) will be recalculated immediately. You can also use a dialog box to change parameters. There are a lot of functions, statistics, graphs and diagrams available. A few of them are: * Interspike interval sequences * ISI Poincar * maps or return maps Instantaneous firing rate * ISI histograms and probability densities * Joint ISI and MSI probability densitograms * Autocorrelation, crosscorrelation * Spike density functions using kernel estimators * Fourier-amplitude spectrum and spectogram * Symbolic maps, recurrence plots * Phase plots of spike density functions Sponsors: Support for this work came from the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Engineering and Geosciences, under Grants DE-FG03-90ER14138 and DE-FG03-96ER14592; from the Office of Naval Research under Grant N00014-00-1-0181; from the National Science Foundation under Grant PHY0097134; from the National Institutes of Health under Grants R01 NS-40110-01A2 and 1RO1 NS-40110; and from the Army Research Office under Contract DAAD19-01-1-0026. R. D. Pinto was supported by the State of Sao Paulo Research Foundation (FAPESP).

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  • 17 years ago - by Anonymous

South African National Bioinformatics Institute: 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

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  • 17 years ago - by Anonymous

Stony Brook University Medical Center: Neuropathology Primer

This is a primer of basic neuropathology- The Central Nervous System and Skeletal Muscle. It is organized in chapters by category of disease with a separate chapter for skeletal muscle. Many of the diseases could be included in more than one chapter because of overlapping pathophysiology; in each case the disorder is included in a single section in the interest of convenience. In order to recognize pathology one must have a basic foundation in normal structure, so the first chapter is an overview of basic regional central nervous system structure and anatomy. It includes an introduction to neurohistology. Other chapters address the pathophysiology of different categories of disease and provide examples of gross and microscopic pathology when they are available.

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  • 17 years ago - by Anonymous

Dendritica: Software Tools for Studying Dendritic Signaling

Dendritica is a program package for relating dendritic geometry and signal propagation. The programs are based on those used for the simulations described in the following paper: Vetter, P., Roth, A. &amp; Husser, M. (2001). Action potential propagation in dendrites depends on dendritic morphology. Journal of Neurophysiology, 85: 926-937. Dendritica can functionally be divided into three main parts: - Interactive morphological analysis and electrophysiological simulation of single cells - Automated batch simulations across a set of morphologies using the same simulation parameters - Automated analysis of batch simulation runs Dendritica requires NEURON 4.1.1 with some modifications described in Appendix 1. It was tested for NEURON 4.1.1 on Linux and SGI IRIX. Some modifications to the Dendritica code may be necessary in order to run it on older or newer versions of NEURON. Sponsors: This work was supported by the Wellcome Trust, the European Community, the Max-Planck-Gesellschaft, the Wellcome Trust 4-year PhD Programme in Neuroscience.

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  • 17 years ago - by Anonymous

Konrad Lorenz Institute for Evolution and Cognition Research; Altenberg; Austria

Institute for the study of theoretical biology with a focus on evolutionary developmental biology and cultural complexity.

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  • 15 years ago - submitted by Andrea Stagg