Curator Dashboard

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 16, 2019. Multi-database system providing large-scale lists of user-classified sequence identifiers with genome-scale biological information and functional profiles biologically characterizing the different gene classes in the list. GFINDer automatically retrieves updated annotations of several functional categories from different sources, identifies the categories enriched in each class of a user-classified gene list, and calculates statistical significance values for each category. Moreover, GFINDer enables to functionally classify genes according to mined functional categories and to statistically analyze the obtained classifications, aiding in better interpreting microarray experiment results.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

An algorithm to estimate isoforms expression from RNA-seq data based on a model that doesn''t assume uniform distribution of count within transcripts.

• Resource
• SciCrunch
• 13 years ago - by Anonymous

The primary purpose is to assess the importance of environmental factors for Parkinson's Disease (PD) in a population-based sample of Swedish twins. In PD discordant twin pairs, what are the environmental factors that contribute to the disease in the affected twin and or protect the unaffected twin? Second, we want to investigate whether the earlier reports of low heritability for elderly male twins can be confirmed for female pairs. All twins 55 years of age and older in the Swedish Twin Registry have been screened for most complex diseases. 626 twins have screened positive for PD and most pairs are discordant. To establish diagnosis, a physician will examine all potential cases and their co-twins and their medical records will be reviewed. Environmental factors will be studied through the use of discordant pairs, where genetic susceptibility to the disease can be controlled. Environmental exposures are being secured with telephone interviews and from a questionnaire collected 30 years ago. Recent results indicate that genetic factors play a very small role. A better understanding of the etiology of PD is important for the possibility of delaying onset or even preventing the disease, as well as for providing guidance for molecular biology studies. Types of samples * DNA Number of sample donors: 333 (sample collection completed)

• Resource
• SciCrunch
• 15 years ago - by Anonymous

OrChem is an extension for the Oracle 11G database that adds registration and indexing of chemical structures to support fast substructure and similarity searching. The cheminformatics functionality is provided by the Chemistry Development Kit. OrChem provides similarity searching with response times in the order of seconds for databases with millions of compounds, depending on a given similarity cut-off. For substructure searching, it can make use of multiple processor cores on today''s powerful database servers to provide fast response times in equally large data sets. OrChem is an Oracle chemistry plug-in using the Chemistry Development Kit (CDK). The CDK is an open source Java library for Chemoinformatics and Bioinformatics. OrChem is maintained by the chemoinformatics and metabolism team of the European Bioinformatics Institute. Oracle Data cartridges extend the capabilities of the Oracle server. For chemistry various commercial cartridges exist that facilitate searching and analyzing chemical data. OrChem also provides functionality like this, but is not a cartridge. It doesn''t need Oracle''s extensibility architecture because its Java components run as Java stored procedures inside the Oracle standard JVM (Aurora). OrChem is suitable for Oracle 11G and onwards. Starting with Oracle 11g release 1 (11.1) there is a just-in-time(JIT) compiler for Oracle JVM environment. A JIT compiler for Oracle JVM enables much faster execution because it manages the invalidation, recompilation, and storage of code without an external mechanism. This new Oracle feature makes Java classes perform better than before.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

Gene Ontology for Motifs (GOMO) is an alignment- and threshold-free comparative genomics approach for assigning functional roles to DNA regulatory motifs from DNA sequence. The algorithm detects associations between a user-specified DNA regulatory motif (expressed as a position weight matrix; PWM) and Gene Ontology terms. The original method for predicting the roles of transcription factors (TFs starts with a PWM motif describing the DNA-binding affinity of the TF. GOMO uses the PWM to score the promoter region of each gene in the genome for its likelihood to be bound by the TF. The resulting ''''affinity'''' scores are then used to test each term in the Gene Ontology for association with high-scoring genes. The algorithm was subsequently extended to leverage conserved signals using multiple, related species in a comparative approach, which greatly improves the resulting annotations. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

• Resource
• SciCrunch
• 15 years ago - by Anonymous

A tool for extraction and collaboration for data curation related to biology. CBioC runs as a web browser extension and allows unobtrusive use of the system during the regular course of research in PubMed. It can also be accessed directly (without having to install a plug-in). Automated text extraction is used as a starting point to bootstrap the database, but then it is up to biologists improve upon the extracted data, ironing out inconsistencies by subsequent edits on a massive scale. * After install, it loads when you visit PubMed. * Gets interactions from PubMed abstracts. * Allows you to vote and modify extracted data. * Also shows data from BIND, DIP, MINT, GRID, IntAct.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

CUDASW++ is a bioinformatics software for Smith-Waterman protein database searches that takes advantage of the massively parallel CUDA architecture of NVIDIA Tesla GPUs to perform sequence searches 10x-50x faster than NCBI BLAST. In this algorithm, we deeply explore the SIMT (Single Instruction, Multiple Thread) and virtualized SIMD (Single Instruction, Multiple Data) abstractions to achieve fast speed. This algorithm has been fully tested on Tesla C1060, Tesla C2050, GeForce GTX 280 and GTX 295 graphics cards, and has been incorporated to NVIDIA Tesla Bio Workbench. * Operating System: Linux * Programming language: CUDA and C * Other requirements: CUDA SDK and Toolkits 2.0 or higher

• Resource
• SciCrunch
• 15 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. A built-in toolbox for the tracing and analysis of neuroanatomy from nanoscale (high-resolution) imaging. It is a project under ongoing development. The name is originating by merging the words Neuron + reconstruct. The working concept is organized in filters applied successively on the image stack to be processed (pipeline). Currently, the focus of the software is the extraction of detailed neuroanatomical profiles from nanoscale imaging techniques, such as the Serial Block-Face Scanning Electron Microscopy (SBFSEM). The techniques applied, however, may be used to analyze data from various imaging methods and neuronal versatility. The underlying idea of Neurostruct is the use of slim interfaces/filters allowing an efficient use of new libraries and data streaming. The image processing follows in voxel pipelines by using the CUDA programming model and all filters are programmed in a datasize-independent fashion. Thus Neurostruct exploits efficiency and datasize-independence in an optimal way. Neurostruct is based on the following main principles: * Image processing in voxel pipelines using the general purpose graphics processing units (GPGPU) programming model. * Efficient implementation of these interfaces. Programming model and image streaming that guarantees a minimal performance penalty. * Datasize-independent programming model enabling independence from the processed image stack. * Management of the filters and IO data through shell scripts. The executables (filters) are currently managed through shell scripts. The application focuses currently in the tracing of single-biocytin filled cells using SBFSEM imaging. : * Extraction of neuroanatomical profiles: 3D reconstrution and 1D skeletons of the imaged neuronal structure. * Complete tracing: Recognition of the full neuronal structure using envelope techniques, thereby remedying the problem of spines with thin necks of an internal diameter approaching the SBFSEM resolution. * Separation (Coloring) of subcellular structures: Algorithms for the separation of spines from their root dendritic stem. * Evaluation and analysis of the imaged neuroanatomy: Calculation of the dendritic and spine membrane''s surface, spine density and variation, models of dendrites and spines

• Resource
• SciCrunch
• 15 years ago - by Anonymous

The Edwards lab conducts research in various aspects of computational biology and bioinformatics, particularly proteomics and mass spectrometry informatics and DNA and protein based signatures for pathogen detection. Some tools provided by Edwards Lab are the PepArML Meta-Search Engine, PeptideMapper Web-Service, Peptide Sequence Databases, Rapid Microorganism Identification Database (RMIDb), and GlycoPeptideSearch. Our primary area of research is the analysis of mass spectrometry experiments for proteomics. Proteomics, the qualitative and quantitative analysis of the expressed proteins of a cell, makes it possible to detect and compare the protein abundance profiles of different samples. Proteins observed to be under or over expressed in disease samples can lead to diagnostic markers or drug targets. The observation of mutated or alternatively spliced protein isoforms may provide domain experts with clues to the mechanisms by which a disease operates. The detection of proteins by mass spectrometry can even signal the presence of airborne microorganisms, such as anthrax, in the detect-to-protect time-frame. Recent research has focused on the discovery of novel peptides in proteomics datasets, improving the sensitivity and specificity of peptide identification using spectral matching with hidden Markov models, and unsupervised machine-learning based peptide identification result combining. Outside of proteomics, we work on computational tools for the design of highly specific oligonucleotides useful for pathogen signatures and PCR assay design. Recent research has focused on precomputing all human oligos of length 20 that are unique up to 4 string edits; and all bacterial 20-mer oligos that are species specific up to 4 string edits.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

The Frey Lab develops techniques that use large scale datasets to derive predictive models of how genes and many other genomic features act in combination to produce genetic messages that control cellular activities. We have most recently focused on how organisms use alternative splicing to generate a tremendous level of biological complexity that cannot be explained by gene expression alone (Nature, 2010). Some of the tools, software and databases provided by the Frey Lab are affinity propagation, splicing prediction, PTMClust - A Post-translational Modification Refinement Algorithm, the ''epitome'': A new model of patterns, transformation invariant clustering and subspaces, learning flexible sprites from images and videos, phase unwrapping by loopy belief propagation, useful Matlab scripts, bioinformatics links, and SeedSearcher: A motif finder.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

The Spotfire Gene Ontology Advantage Application integrates GO annotations with gene expression analysis in Spotfire DecisionSite for Functional Genomics. Researchers can select a subset of genes in DecisionSite visualizations and display their distribution in the Gene Ontology hierarchy. Similarly, selection of any process, function or cellular location in the Gene Ontology hierarchy automatically marks the corresponding genes in DecisionSite visualizations. Platform: Windows compatible

• Resource
• SciCrunch
• 15 years ago - by Anonymous

The UCSF Helen Diller Family Comprehensive Cancer Center combines basic science, clinical research, epidemiology/cancer control, and patient care throughout the University of California, San Francisco. UCSF''s long tradition of excellence in cancer research includes, notably, the Nobel Prize-winning work of J. Michael Bishop and Harold Varmus, who discovered cancer-causing oncogenes. Their work opened new doors for exploring genetic mistakes that cause cancer, and formed the basis for some of the most important cancer research happening today. * Basic Scientific Research: From understanding normal cellular processes and replication to discovering the underlying molecular and genetic causes of cancer when these processes go awry, UCSF researchers are committed to moving scientific insights beyond model systems and pursuing their relevance for clinical oncology and cancer prevention. * Clinical Research: Clinical scientists explore how greater understanding of fundamental biological events can be transformed into clinically relevant tools. New forms of cancer treatment, as well as innovations in diagnosis and prognosis, undergo rigorous evaluation for safety and efficacytranslating into improved patient outcomes and hope for the future. * Patient Care: The Helen Diller Family Comprehensive Cancer Center provides superlative cancer patient care at four San Francisco medical centers: UCSF Medical Center at Mount Zion; UCSF Medical Center at Parnassus; San Francisco General Hospital; and the San Francisco Veterans Affairs Medical Center. * Population Science: Cancer population sciences at UCSF includes a broad range of research on the causes of new cancers and the sickness and death due to the disease in order to develop ways to improve the prevention and early detection of cancer as well as the quality of life following diagnosis and treatment for all of Northern California''s diverse populations.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

Exploratory Gene Association Networks (EGAN) is a software tool that allows a bench biologist to visualize and interpret the results of high-throughput exploratory assays in an interactive hypergraph of genes, relationships (protein-protein interactions, literature co-occurrence, etc.) and meta-data (annotation, signaling pathways, etc.). EGAN provides comprehensive, automated calculation of meta-data coincidence (over-representation, enrichment) for user- and assay-defined gene lists, and provides direct links to web resources and literature (NCBI Entrez Gene, PubMed, KEGG, Gene Ontology, iHOP, Google, etc.). EGAN functions as a module for exploratory investigation of analysis results from multiple high-throughput assay technologies, including but not limited to: * Transcriptomics via expression microarrays or RNA-Seq * Genomics via SNP GWAS or array CGH * Proteomics via MS/MS peptide identifications * Epigenomics via DNA methylation, ChIP-on-Chip or ChIP-Seq * In-silico analysis of sequences or literature EGAN has been built using Cytoscape libraries for graph visualization and layout, and is comparable to DAVID, GSEA, Ingenuity IPA and Ariadne Pathway Studio. There are pre-collated EGAN networks available for human (Homo sapiens), mouse (Mus musculus), rat (Rattus norvegicus), chicken (Gallus gallus), zebrafish (Danio rerio), fruit fly (Drosophila melanogaster), nematode (Caenorhabditis elegans), mouse-ear cress (Arabidopsis thaliana), rice (Oryza sativa) and brewer's yeast (Saccharomyces cerevisiae). There is now an EGAN module available for GenePattern (human-only). Platform: Windows compatible, Mac OS X compatible, Linux compatible

• Resource
• SciCrunch
• 15 years ago - by Anonymous

Tk-GO is a GUI wrapping the basic functions of the GO AppHandle library from BDGP. GO terms are presented in an explorer-like browser, and behavior can be configured by altering Perl scripts. All available documentation is included in the download. Tk-GO uses the GO database (connects directly to the BDGP database by default) but is user-configurable. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

• Resource
• SciCrunch
• 15 years ago - by Anonymous

Onto-Express is a web-based tool in the Onto-Tools suite that performs automated function profiling for a list of differentially expressed genes. However, Onto-Express does not support functional profiling for the organisms that do not have annotations in public domain, or use of custom (i.e. user-defined) ontologies. This limitation is also true for most of the other existing tools for functional profiling, which means that researchers working with uncommon organisms and/or new annotations or ontologies may be forced to construct such profiles manually. Onto-Express To Go (OE2GO) is a new tool added to the Onto-Tools ensemble to address these issues. OE2GO is built on top of OE to leverage its existing functionality. In OE2GO, the users now have an option to use either the Onto-Tools database as a source of functional annotations or provide their own annotations in a separate file. Currently, OE2GO supports annotation file in the Gene Ontology format. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

• Resource
• SciCrunch
• 15 years ago - by Anonymous

We aim to facilitate the pathway for access, storage, use and transfer of human organs, cells and tissue between clinical centers within UCL Partners, academic groups in UCL, other universities, hospitals, medical researcher and biotechnology companies, to enhance the ability for researchers to access the materials they need. Alongside this, researchers will be able to exchange information and access guides on regulatory, ethics and practical issues concerning access, transfer and use of this type of material. These guides will be video and documents format, based on talks at organized events given by experts in the relevant fields. All of this information will be accessible on a website that seeks to link groups within UCL and attract attention from the wider world through social media and expansion of existing contacts. Our vision is to develop a centralized human tissue provision and utilization service for academic and commercial researchers UCL has the highest concentration of biomedical researchers in Europe. As part of this, UCL has numerous licensed biobanks and is associated with many research intensive hospitals in North London. The role of a biobank is to prepare and hold human tissue samples in for use by medical researchers to help delivery new treatments. Hospitals can also provide human tissue for research by utilizing waste human tissue taken as part of surgery or diagnostic procedures, but is normally incinerated. The researchers using the human tissue could be working within academic laboratories in UK universities and institutions or as part of commercial companies. Researchers currently cannot easily access human tissue to meet the demands of their research, often due to the long ethical, regulatory and contractual processes. However, with the enormous UCL biobanking and research Hospital resources, UCL could be a leading academic institution in providing human tissue for medical research within the UK and internationally. Our vision is to develop a centralized human tissue provision and utilization service for academic and commercial researchers. This relies on creating an overarching infrastructure, to consolidate information on disparate human tissue resources around UCL, and (where possible) gain centralized ethical and regulatory and contractual approval for use of the tissue. Funding the infrastructure will rely on a cost recovery model for a per sample basis. As a result the time needed to obtain tissue for research will be dramatically reduced, whilst providing a simple costing model for obtaining human tissue. This will make human tissue procurement much more efficient for end users.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

The Brain Molecular Anatomy Project is a trans-NIH project aimed at understanding gene expression and function in the nervous system. BMAP has two major scientific goals: # Gene discovery: to catalog of all the genes expressed in the nervous system, under both normal and abnormal conditions. # Gene expression analysis: to monitor gene expression patterns in the nervous system as a function of cell type, anatomical location, developmental stage, and physiological state, and thus gain insight into gene function. In pursuit of these goals, BMAP has launched several initiatives to provide resources and funding opportunities for the scientific community. These include several Requests for Applications and Requests for Proposals, descriptions of which can be found in this Web site. BMAP is also in the process of establishing physical and electronic resources for the community, including repositories of cDNA clones for nervous system genes, and databases of gene expression information for the nervous system. Most of the BMAP initiatives so far have focused on the mouse as a model species because of the ease of experimental and genetic manipulation of this organism, and because many models of human disease are available in the mouse. However, research in humans, other mammalian species, non-mammalian vertebrates, and invertebrates is also being funded through BMAP. For the convenience of interested investigators, we have established this Web site as a central information resource, focusing on major NIH-sponsored funding opportunities, initiatives, genomic resources available to the research community, courses and scientific meetings related to BMAP initiatives, and selected reports and publications. When appropriate, we will also post initiatives not directly sponsored by BMAP, but which are deemed relevant to its goals. Posting decisions are made by the Trans-NIH BMAP Committee

• Resource
• SciCrunch
• 15 years ago - by Anonymous

Database of blood-brain barrier properties of peptides including structure, method, responses, physicochemical properties and related literature. The database is linked to a manuscript entitled Brainpeps: the blood-brain barrier peptide database, in which the BBB methods and responses are clarified and correlated to each other. Data may be submitted for addition to the database.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

My laboratory has created a family of natural product-inspired anticancer agents. We have evaluated our compounds at the Yale Center for Chemical Genomics, and they are exhibiting IC50 values in the low nM range against K562, HeLa, LnCAP, and HCT-116 lines. Their mechanism of action is unknown, although the natural products have been shown to cleave DNA. An evaluation of the natural products at the NCI has shown that they have a toxicity profile that is distinct from other known DNA damaging agents. We can readily access gram-quantities of these agents for further studies. We are looking for researchers who might find these compounds useful in medicinal applications, for example, for treatment of a specific cancer. We are capable of synthesizing new analogs, such as those incorporating a specific recognition or targeting element, and would be excited to pursue this avenue of research.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

Open tools to support data management for biological light microscopy produced by a multi-site collaborative effort among academic laboratories and a number of commercial entities. Designed to interact with existing commercial software, all OME formats and software are free, and all OME source code is available under the GNU General public license or through commercial license from Glencoe Software. OME is developed as a joint project between research-active laboratories at the Dundee, NIA Baltimore, and Harvard Medical School and LOCI. In addition, OME has active collaborations with many imaging and informatics groups. While many other applications could use OME''s architecture and design, their specific implementation is focused on biological and biomedical imaging. Those interested in applying OME''s technology to other applications should contact the developers. OME work is divided into several different standards and software projects: * Bio-Formats: A Java-based library for reading and writing over 90 microscopy file formats. * OMERO Software: The Java-based OMERO software project, which currently includes tools for storing, visualizing, managing, and annotating microscopic images and metadata. * OME-XML & OME-TIFF: The OME-XML and OME-TIFF file format specifications, which are open file formats for sharing microscope image data. * OME Server: This was the original OME server project which has now ended and is a legacy product. It implements image-based analysis of cellular dynamics and image-based screening of cellular localization or phenotypes, and included a fully developed version of the 2003 version of OME-XML Schema language.

• Resource
• SciCrunch
• 15 years ago - by Anonymous