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Biomaterial supply resource which provides high quality and well-chracaterized brain tissue samples for MS research. Registered MS brain donors and their families are kept up to date on the latest progress in MS research.

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• SciCrunch
• 16 years ago - by Anonymous

A provider of bio-implants and organs for transplantation and tissue banking services.

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• SciCrunch
• 16 years ago - by Anonymous

A core laboratory facility provides high-volume, high-quality tissue and biospecimen preparation and processing in support of Mayo research. Mayo Validation Support Services utilizes these resources to deliver extensive validation capabilities specific to individualized Sponsor requirements. Medical scientific expertise at Mayo Clinic allows for unique collaborations combining quality biospecimens linked to comprehensive clinical outcomes. Biospecimens can be accessed via archives or prospectively collected via individualized standard operating procedures. Patient information for all biospecimens is protected through oversight by an Institutional Review Board (IRB). Specimens may be collected and/or processed in a variety of customized formats for individual collaborations. Typical formats include: * Specimen Processing: Tissue RNA/DNA extraction capabilities, Paraffin and frozen sectioning, Immunostaining, Digital imaging, Laser capture microdissection, Tissue microarray construction, Preparation of protocol-collected tissue (FFPE, OCT, Snap-frozen, PBS) * Blood: Circulating tumor cells, Serum, Plasma, PBMC, Whole blood for FACS analysis, Blood smears * Other: Induced sputum, Saliva, Buccal swabs, Lip biopsies, Colonoscopy biopsies, Synovium, Stool, Urine Biospecimens located within archives are well-characterized and associated with phenotypic information. Multiple types and formats of biospecimens are available for customized validation purposes.

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• SciCrunch
• 16 years ago - by Anonymous

Not yet vetted by NIF curator

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• SciCrunch
• 16 years ago - by Anonymous

MEFIT is a Microarray Experiment Functional Integration Technology. Given any amount of microarray data, it predicts the probability of pairwise functional relationship for any gene pair within individual biological functions. This web site makes the results of this evaluation available for download and provides an online view of the test set predictions based on hierarchical clustering. As a framework, MEFIT uses the results of many microarray experiments in combination with known biological process annotations (drawn from the Gene Ontology, KEGG, MIPS, or a biologist''s own pathways of interest) to predict new gene pair functional relationships within the given biological functions. Or in other words, MEFIT is a system that takes microarray results and known functional annotations as inputs and produces predicted gene pair functional relationships as output. To make these predictions, MEFIT uses a Bayesian network that consumes microarray data as input observations and produces predicted functional relationships through a single unobserved (except during training) node. Furthermore, to make predictions within the context of individual biological functions, a single Bayesian network structure is replicated once per function of interest. These networks with identical structure are then trained using known functional annotations such that each function''s network learns its own set of conditional probabilities. These probabilities encode how predictive each microarray experiment is of a particular function; for example, a sporulation time course might be very predictive of meiosis, but not much help in determining which genes perform ATP synthesis. We''ve evaluated this system using a collection of 40 microarray data sets and 200 biological processes. Select a biological function from the menu to view the results of clustering the S. cerevisiae genome using MEFIT''s test set predictions within that function as a similarity metric. Alternatively, enter a gene name or ORF identifier to list only functions in which that gene is predicted to be active. We are currently able to offer for download: The MEFIT README file, a Windows version and Linux version.

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• SciCrunch
• 16 years ago - by Anonymous

Software tool as whole genome shotgun assembler that can generate high quality genome assemblies using short reads (~100bp) such as those produced by the new generation of sequencers.

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• SciCrunch
• 13 years ago - by Anonymous

Commercial tissue bank for human tissues.

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• SciCrunch
• 16 years ago - by Anonymous

Database of case reports of adverse reactions to vaccinations. There are 806 reports (May 2013). If you would like to report a case, please go to report your own vaccine reaction. The user may search by keywords or sort by vaccine, country, age, outcome, gender and hospital admission.

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• SciCrunch
• 16 years ago - by Anonymous

TESS is a web tool for predicting transcription factor binding sites in DNA sequences. It can identify binding sites using site or consensus strings and positional weight matrices from the TRANSFAC, JASPAR, IMD, and our CBIL-GibbsMat database. You can use TESS to search a few of your own sequences or for user-defined CRMs genome-wide near genes throughout genomes of interest. Search for CRMs Genome-wide: TESS now has the ability to search whole genomes for user defined CRMs. Try a search in the AnGEL CRM Searches section of the navigation bar.. You can search for combinations of consensus site sequences and/or PWMs from TRANSFAC or JASPAR. Search DNA for Binding Sites: TESS also lets you search through your own sequence for TFBS. You can include your own site or consensus strings and/or weight matrices in the search. Use the Combined Search under ''Site Searches'' in the menu or use the box for a quick search. TESS assigns a TESS job number to all sequence search jobs. The job results are stored on our server for a period of time specified in the search submit form. During this time you may recall the search results using the form on this page. TESS can also email results to you as a tab-delimited file suitable for loading into a spreadsheet program. Query for Transcription Factor Info: TESS also has data browsing and querying capabilities to help you learn about the factors that were predicted to bind to your sequence. Use the Query TRANSFAC or Query Matrices links above or use the search interface provided from the home page.

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• SciCrunch
• 16 years ago - by Anonymous

We are an interdisciplinary team dedicated to annotating gene function related to human fetal development. We are contributing new functional annotation to the Gene Ontology, curating and mining gene sets suitable for the interpretation of developmental genomic data, and creating the computational tools needed to apply genomics for better understanding the molecular mechanisms of human development. Our GO annotation is in the process of being incorporated into the GOA public release. The GONE (Gene Ontology Non-Eligible) database is where we store annotations relevant to our research but that don''t quite meet GOA''s standards. Usually an annotation falls into this category because either the gene/protein described is a family of genes/proteins rather than a specific one, there is no UniProt ID to identify the gene/protein in the system, a GO term does not yet exist to describe the particular function, process, or location of the gene/protein, the species is not clearly identifiable in the paper, or the evidence is not as reliable (GO evidence codes TAS and NAS). As individual annotations these are more suspect than current GO annotation. However, for functional analysis of expression data, these gene sets can be valuable even with a certain amount of noise. We also include here a link to the supplementary data from our forthcoming PSB 2011 paper on gene set mining.

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• SciCrunch
• 16 years ago - by Anonymous

A database of 250 recordings of 3-lead ECGs, ABP, PAP, CVP, respiration, and airway CO2 signals from patients in critical care units; some recordings include intra-cranial, left atrial, ventricular and intra-aortic pressure waveforms.

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• SciCrunch
• 16 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. The EBI SRS server is a primary gateway to major databases in the field of molecular biology produced and supported at EBI as well as European public access point to the MEDLINE database provided by US National Library of Medicine (NLM). It is a reference server for latest developments in data and application integration. Features include: concept of virtual databases, integration of XML databases like the Integrated Resource of Protein Domains and Functional Sites (InterPro), Gene Ontology (GO), MEDLINE, Metabolic pathways, etc., user friendly data representation in ''Nice views'', SRSQuickSearch bookmarklets. Quick Searches allow users to make a number of searches without needing to learn how to use SRS in depth. The searches query some of the common databanks without having to go and select them explicitly and without the need to understand the SRS Query Forms. Quick Searches can be performed from either the Start page (when you first open SRS) or the SRS Quick Search page (when you are already in a project). SRS also has the ability to search for links between your current results and related information in other databanks. Additionally, it is able to analyze the results of your search using many bioinformatics analysis tools or applications. This enables you to seek out further information that may be relevant to your initial search.

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• SciCrunch
• 16 years ago - by Anonymous

Not yet vetted by NIF curator

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• SciCrunch
• 16 years ago - by Anonymous

Data and information collection and repository for biological activities of small molecules and small interfering RNAs (siRNAs) hosted by the US National Institutes of Health (NIH). Used to select and summarize the bioactivities of tested substances.

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• SciCrunch
• 16 years ago - by Anonymous

A quality-value guided de novo short read assembler.

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• SciCrunch
• 13 years ago - by Anonymous

Software providing a method that eschews the traditional graph-based approach in favor of a simple 3'' extension approach that has potential to be massively parallelized.

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• SciCrunch
• 13 years ago - by Anonymous

Sequence assembler and mapper for whole genome shotgun and EST/RNASeq sequencing data.

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• SciCrunch
• 13 years ago - by Anonymous

Overall aim of the LifeLines Study is to unravel the interaction between genetic and environmental factors in the development of multifactorial diseases, their concurrent development in individuals and their complications as a complex trait. The LifeLines database contains questionnaire data, physical measurements and biological samples from different health examinations. Collaboration is encouraged as it helps to maximize the scientific value of the wealth of epidemiologic data made possible by the participation of more than 165,000 individuals in the LifeLines Cohort Study. Primary objectives of the LifeLines Cohort Study are: a. Which are the disease overriding risk factors which predict the development of a multifactorial disease during lifetime? b. How are these universal risk factors modified, or what determines the effect of a universal risk factor in an individual? Specific research questions will focus on risk factors and modifiers (genetic, environmental and combined or complex factors) for single and multiple diseases. In addition to co-morbidity, LifeLines focuses on co-determinants. The primary endpoints include measures of aging, metabolic and endocrine diseases, cardiovascular and renal diseases, pulmonary and musculoskeletal diseases, and psychopathology. Secondary aims include the assessment of the prevalence and incidence of multifactorial diseases, their risk factors and their treatment in individuals as well as in families. The burden of disease for the society will be quantified in terms of care needed, and total costs of care. Until November 3, 2011, almost 68,000 subjects have been included in the study. The 60,000th participant was screened in the beginning of September 2011. Recruitment rate at present is between 700 and 800 subjects per week. The laboratory measurements which are performed has changed. As of October 2011, LifeLines will continue to measure: hematologic parameters, including hemoglobin, white blood cells, platelets, WBC differentiation, blood glucose, cholesterol, HDL-cholesterol, triglycerides, serum creatinin and sodium/potassium. Liver enzymes, thyroid hormones, calcium, phosphate, albumin, uric acid and microalbuminuria will not be measured routinely. The samples that are available for almost all participants, are: # serum (taken either with or without gel separator) # EDTA plasma # citrate plasma # DNA # early morning urine sample # urine samples of 24-hour urine collection Any researcher who is member of an internationally recognized academic institution and who is interested in utilizing the research possibilities, data and materials of LifeLines may apply for access. The applicant who is acting as Principal Investigator must be connected to a department or institution with the competence to carry out the research project to term. A contract will give the right to use the data for a pre-determined period of time. This contract also comprises the costs for the LifeLines Biobank which the investigator needs to reimburse. To apply for access, refer to the electronic application process.

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• SciCrunch
• 16 years ago - by Anonymous

OBIWarehouse (Open Biological Warehouse) aims at integrating several biological data sources into a unified database. Content includes 3D structural data, Protein-related data, Metabolic-related data, Genomic-related data, and Biological classification.

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• SciCrunch
• 16 years ago - by Anonymous

Not yet vetted by NIF curator

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• SciCrunch
• 16 years ago - by Anonymous