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http://www.decode.com/

A biopharmaceutical company applying its discoveries in human genetics to develop drugs and diagnostics for common diseases. They specialize in gene discovery - their population approach and resources have enabled them to isolate key genes contributing to major public health challenges from cardiovascular disease to cancer. The company's genotyping capacity is now one of the highest in the world. They have a large population-based biobank containing whole blood and DNA samples with extensive relevant phenotypic information from around 120.000 Icelanders. In the company's work in more than 50 disease projects, their statistical and informatics departments have established themselves in data processing and analysis. deCODE genetics is widely recognized as a center of excellence in genetic research.

Proper citation: deCODE genetics (RRID:SCR_003334) Copy   

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http://core.biotech.hawaii.edu/Bioinformatics.htm

THIS RESOURCE IS NO LONGER IN SERVCE, documented January 28, 2019. Core Facility provides the software and support for computer assisted protein and DNA sequence analysis and database access. The Genetics Computer Group GCG-Wisconsin package is currently available on PBRC's UNIX platform that is accessible via modem or direct connection. The package can be accessed via three interfaces: the command-line interface (UNIX C-shell), the web-based interface (SeqWeb) and the X-Windows based graphics interface (SeqLab). Applications in the package include sequence editing, alignment, comparison, primer design, restriction analysis, mapping, data presentation, database browsing, etc. In addition to local databases, access to remote databases (BLAST) is integrated into the package. The local databases are updated quarterly. Databases available include GenBank, EMBL, PIR-Protein, SWISS-PROT and Restriction Enzymes (REBASE).

Proper citation: GCG/SeqWeb (RRID:SCR_003454) Copy   

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http://rgd.mcw.edu/tools/ontology/ont_search.cgi

Ontology that defines hierarchical display of different rat strains as derived from parental strains. Ontology Browser allows to retrieve all genes, QTLs, strains and homologs annotated to particular term. Covers all types of biological pathways including altered and disease pathways, and to capture relationships between them within hierarchical structure. Five nodes of ontology include classic metabolic, regulatory, signaling, drug and disease pathways. Ontology allows for standardized annotation of rat. Serves as vehicle to connect between genes and ontology reports, between reports and interactive pathway diagrams, between pathways that directly connect to one another within diagram or between pathways that in some fashion are globally related in pathway suites and suite networks.

Proper citation: Rat Strain Ontology (RRID:SCR_003449) Copy   

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https://code.google.com/p/fade/

A software package designed to determine the methylation parameter at each cytosine or cytosine-guanine position in the human genome. FadE uses color reads produced by the SOLiD sequencer or nucleotide reads produced by the Illumina or 454 sequencing platforms.

Proper citation: FadE (RRID:SCR_003448) Copy   

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http://www.minituba.org

miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. miniTUBA represents in a network view possible influences that occur between time varying variables in your dataset. For these networks of influence, miniTUBA predicts time courses of disease progression or response to therapies. minTUBA offers a probabilistic framework that is suitable for medical inference in datasets that are noisy. It conducts simulations and learning processes for predictive outcomes. The DBN analysis conducted by miniTUBA describes from variables that you specify how multiple measures at different time points in various variables influence each other. The DBN analysis then finds the probability of the model that best fits the data. A DBN analysis runs every combination of all the data; it examines a large space of possible relationships between variables, including linear, non-linear, and multi-state relationships; and it creates chains of causation, suggesting a sequence of events required to produce a particular outcome. Such chains of causation networks - are difficult to extract using other machine learning techniques. DBN then scores the resulting networks and ranks them in terms of how much structured information they contain compared to all possible models of the data. Models that fit well have higher scores. Output of a miniTUBA analysis provides the ten top-scoring networks of interacting influences that may be predictive of both disease progression and the impact of clinical interventions and probability tables for interpreting results. The DBN analysis that miniTUBA provides is especially good for biomedical experiments or clinical studies in which you collect data different time intervals. Applications of miniTUBA to biomedical problems include analyses of biomarkers and clinical datasets and other cases described on the miniTUBA website. To run a DBN with miniTUBA, you can set a number of parameters and constrain results by modifying structural priors (i.e. forcing or forbidding certain connections so that direction of influence reflects actual biological relationships). You can specify how to group variables into bins for analysis (called discretizing) and set the DBN execution time. You can also set and re-set the time lag to use in the analysis between the start of an event and the observation of its effect, and you can select to analyze only particular subsets of variables.

Proper citation: miniTUBA (RRID:SCR_003447) Copy   

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http://www.violinet.org/ovae/

A biomedical ontology in the area of vaccine adverse events aimed to represent and analyze various vaccine-specific adverse events. OVAE is an extension of the Ontology of Adverse Events (OAE) and the Vaccine Ontology (VO).

Proper citation: Ontology of Vaccine Adverse Events (RRID:SCR_003442) Copy   

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http://ncit.nci.nih.gov/

A reference terminology and core biomedical ontology for NCI that covers approximately 100,000 key biomedical concepts with terms, codes, definitions, and more than 200,000 inter-concept relationships. It is the reference terminology for NCI, NCI Metathesaurus and NCI informatics infrastructure covering vocabulary for clinical care, translational and basic research, and public information and administrative activities. It includes broad coverage of the cancer domain, including cancer related diseases, findings and abnormalities; anatomy; agents, drugs and chemicals; genes and gene products and so on. In certain areas, like cancer diseases and combination chemotherapies, it provides the most granular and consistent terminology available. It combines terminology from numerous cancer research related domains, and provides a way to integrate or link these kinds of information together through semantic relationships. NCIt features: * Stable, unique codes for biomedical concepts; * Preferred terms, synonyms, definitions, research codes, external source codes, and other information; * Links to NCI Metathesaurus and other information sources; * Over 200,000 cross-links between concepts, providing formal logic-based definition of many concepts; * Extensive content integrated from NCI and other partners, much available as separate NCIt subsets * Updated frequently by a team of subject matter experts. NCIt is a widely recognized standard for biomedical coding and reference, used by a broad variety of public and private partners both nationally and internationally including the Clinical Data Interchange Standards Consortium Terminology (CDISC), the U.S. Food and Drug Administration (FDA), the Federal Medication Terminologies (FMT), and the National Council for Prescription Drug Programs (NCPDP).

Proper citation: NCI Thesaurus (RRID:SCR_003563) Copy   

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http://cumc.columbia.edu/dept/gsas/pharm/index.html

The spirit of the Department of Pharmacology is one of collaborative and synergistic science. As such, interests of the members of the Department span such diverse problems as the identification of molecular signals that determine whether a cell lives or dies to the discovery of new drugs that control cardiac rhythm in inherited and non-inherited heart disease. We at Columbia in general, and within the Department of Pharmacology in particular, are proud to be an integral part of the intellectual and cultural life of New York. In many ways the diversity of scientific interests within the Department reflect the diversity of the rich scientific environment of Columbia University and, in addition, the unique cultural and urban environment of New York City. Within the Graduate Programs in Mechanisms of Health and Disease, the Department of Pharmacology offers a Pharmacology and Molecular Signaling Program leading to the Ph.D. degree. Training is focused on both classical principles of pharmacology and more modern biophysical, genetic and computational approaches to the development of new and more specific therapeutic agents to manage human disease. The interdisciplinary training program in the molecular and genetic basis of cardiac arrhythmias is designed to provide training to post doctoral fellows with either M.D. or Ph.D. degrees to enable them to become independent researchers and leaders in the field as we enter a period of post genomic medical science. The overall aim of the program is to train researchers who will be well-grounded in molecular and cellular biology, but who also are trained in cardiovascular systems physiology/pharmacology in order to integrate the cellular and subcellular mechanisms (genotype) with the basis of human disease expressed at the systems level (phenotype).

Proper citation: Columbia University College of Physicians and Surgeons Department of Pharmacology (RRID:SCR_003320) Copy   

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https://code.google.com/p/proteomecommons-tranche/

A distributed file storage system that you can upload files to and download files from. All files uploaded to the repository are replicated several times to protect against their accidental loss. Files uploaded to the repository can be of any size, can be of any file type, and can be encrypted with a passphrase of your choosing. The Proteome Commons Tranche repository is the first instance of a Tranche repository. Tranche, was created so that anybody can take it and make their own Tranche repository. This is the first implementation of the Tranche software, and is useful as a test bed for the software. This repository relies on educational institutions to provide the hardware and facilities for Tranche servers. While we maintain a set of servers, the continued growth of this public resource will rely on the generosity of the institutions that use the repository most.

Proper citation: Proteome Commons Tranche repository (RRID:SCR_003441) Copy   

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http://shendurelab.github.io/MIPGEN/

Software for a fast, simple way to generate designs for MIP assays targeting hundreds or thousands of genomic loci in parallel. Packaged with MIPgen are scripts that aid in visualization of MIP designs and processing of MIP sequence reads to SAM files that can then be passed through any standard variant calling pipeline.

Proper citation: MIPgen (RRID:SCR_003325) Copy   

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http://neuroscience.med.cornell.edu/

The Weill Medical College of Cornell University has a long tradition of neuroscience research. The faculty is large, and the research programs tackle the fundamental questions in the field at all levels - from genes to cells to systems to behavior. The neuroscience community at Weill Cornell spans two campuses. The main one, containing the medical school and associated hospitals, is located on the upper east side of Manhattan, adjacent to Rockefeller University and across the street from the Memorial Sloan-Kettering Cancer Center. The second campus, with the rapidly growing Burke-Cornell Research Institute, is located in White Plains. The following graduate programs are offered: Neuroscience PhD, Tri-Institutional MD-PhD, Physiology and Biophysics, and Tri-Institutional Graduate program in Computational Biology and Medicine.

Proper citation: Cornell University Neuroscience (RRID:SCR_003324) Copy   

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http://caties.cabig.upmc.edu/

The Cancer Text Information Extraction System (caTIES) provides tools for de-identification and automated coding of free-text structured pathology reports. It also has a client that can be used to search these coded reports. The client also supports Tissue Banking and Honest Broker operations. caTIES focuses on two important challenges of bioinformatics * Information extraction (IE) from free text * Access to tissue. Regarding the first challenge, information from free-text pathology documents represents a vital and often underutilized source of data for cancer researchers. Typically, extracting useful data from these documents is a slow and laborious manual process requiring significant domain expertise. Application of automated methods for IE provides a method for radically increasing the speed and scope with which this data can be accessed. Regarding the second challenge, there is a pressing need in the cancer research community to gain access to tissue specific to certain experimental criteria. Presently, there are vast quantities of frozen tissue and paraffin embedded tissue throughout the country, due to lack of annotation or lack of access to annotation these tissues are often unavailable to individual researchers. caTIES has three goals designed to solve these problems: * Extract coded information from free text Surgical Pathology Reports (SPRs), using controlled terminologies to populate caBIG-compliant data structures. * Provide researchers with the ability to query, browse and create orders for annotated tissue data and physical material across a network of federated sources. With caTIES the SPR acts as a locator to tissue resources. * Pioneer research for distributed text information extraction within the context of caBIG. caTIES focuses on IE from SPRs because they represent a high-dividend target for automated analysis. There are millions of SPRs in each major hospital system, and SPRs contain important information for researchers. SPRs act as tissue locators by indicating the presence of tissue blocks, frozen tissue and other resources, and by identifying the relationship of the tissue block to significant landmarks such as tumor margins. At present, nearly all important data within SPRs are embedded within loosely-structured free-text. For these reasons, SPRs were chosen to be coded through caTIES because facilitating access to information contained in SPRs will have a powerful impact on cancer research. Once SPR information has been run through the caTIES Pipeline, the data may be queried and inspected by the researcher. The goal of this search may be to extract and analyze data or to acquire slides of tissue for further study. caTIES provides two query interfaces, a simple query dashboard and an advanced diagram query builder. Both of these interfaces are capable of NCI Metathesaurus, concept-based searching as well as string searching. Additionally, the diagram interface is capable of advanced searching functionalities. An important aspect of the interface is the ability to manage queries and case sets. Users are able to vet query results and save them to case sets which can then be edited at a later time. These can be submitted as tissue orders or used to derive data extracts. Queries can also be saved, and modified at a later time. caTIES provides the following web services by default: MMTx Service, TIES Coder Service

Proper citation: caTIES - Cancer Text Information Extraction System (RRID:SCR_003444) Copy   

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http://www.compgen.org/tools/metagen

Software program providing a method for meta-analysis of case-control genetic association studies using random-effects logistic regression.

Proper citation: metagen (RRID:SCR_003443) Copy   

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http://www.curealzfund.org/

Cure Alzheimer's Fund is a 501(c)(3) public charity. At Cure Alzheimer's Fund, our mission is to fund research with the highest probability of slowing, stopping or reversing Alzheimer's disease. This topical portal has a lot of information including news and blog. Cure Alzheimer's Fund is governed by a board of directors; administered by a small, full-time staff; and guided scientifically by a Research Consortium. A Scientific Advisory Board audits the research program to make sure it is consistent with the objectives of the foundation. Cure Alzheimer's Fund is a doing business as name for the Alzheimer's Disease Research Foundation, federal tax ID # 52-2396428.

Proper citation: Cure Alzheimers Fund (RRID:SCR_003564) Copy   

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http://purl.bioontology.org/ontology/FB-CV

A structured controlled vocabulary used for various aspects of annotation by FlyBase. This ontology is maintained by FlyBase for various aspects of annotation not covered, or not yet covered, by other OBO ontologies. If and when community ontologies are available for the domains here covered FlyBase will use them.

Proper citation: FlyBase Controlled Vocabulary (RRID:SCR_003318) Copy   

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http://code.google.com/p/omrse/

An ontology covering the domain of social entities that are related to health care, such as demographic information (social entities for recording gender (but not sex) and marital status, for example) and the roles of various individuals and organizations (patient, hospital, etc.)

Proper citation: Ontology of Medically Related Social Entities (RRID:SCR_003439) Copy   

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http://www.brainbank.mclean.org/

Biomaterial supply resource that acquires, processes, stores, and distributes postmortem brain specimens for brain research. Various types of brain tissue are collected, including those with neurological and psychiatric disorders, along with their parents, siblings and offspring. The HBTRC maintains an extensive collection of postmortem human brains from individuals with Huntington's chorea, Alzheimer's disease, Parkinson's disease, and other neurological disorders. In addition, the HBTRC also has a collection of normal-control specimens.

Proper citation: Harvard Brain Tissue Resource Center (RRID:SCR_003316) Copy   

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http://bcs.mit.edu/index.html

MIT's Department of Brain and Cognitive Sciences stands at the nexus of neuroscience, biology and psychology. We combine these disciplines to study specific aspects of the brain and mind including: vision, movement systems, learning and memory, neural and cognitive development, language and reasoning. Together, MIT's Department of Brain and Cognitive Sciences offers extraordinary learning opportunities for undergraduate, graduate and postdoctoral students. Because the human brain is immensely complex in many different ways at once, we pursue every level of inquiry - from molecules to cells to circuits to the mystery of the mind itself. As we study its diseases and disorders, its development and daily feats, like vision, speech, movement and memory, we also integrate methods and insights from every area of brain research. This unusual diversity of expertise fosters an intensely creative atmosphere that sparks startling collaborations. Already known for remarkable contributions to the field, our faculty members continually stretch the limits of knowledge, and bring the same passion to educating our exceptional students. A key part of the BCS mission is to offer its graduate and undergraduate students an educational experience of the highest quality. Our graduate students benefit from the impressive range of our program, and from the ability to participate in research projects with faculty members who are leaders in their fields. The department's undergraduate program, which includes both neuroscience and cognitive science, is one of the fastest-growing majors at MIT.

Proper citation: Massachusetts Institute of Technology; Department of Brain and Cognitive Sciences (RRID:SCR_003437) Copy   

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http://purl.bioontology.org/ontology/FYPO

A formal ontology of phenotypes observed in fission yeast that is being developed to support the comprehensive and detailed representation of phenotypes in PomBase, the online fission yeast resource. Its scope is similar to that of the Ascomycete Phenotype Ontology (APO), but FYPO includes more detailed pre-composed terms as well as computable definitions.

Proper citation: Fission Yeast Phenotype Ontology (RRID:SCR_003315) Copy   

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https://github.com/outbig/DAFGA

A python script package which estimates the evolutionary rate of a particular functional gene in a standardized manner by relating its sequence divergence to that of the 16S rRNA gene. It provides gene-specific parameter sets for OTU clustering and taxonomic assignment at desired rank, and it can be implemented into the diversity measurements offered by QIIME or Mothur.

Proper citation: DAFGA (RRID:SCR_003319) Copy   

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