Tools   Select Another Resource Report Type

http://neuro.med.harvard.edu/

The mission of the Department of Neurobiology is to promote research and teaching that leads to a better understanding of the normal and diseased brain. The Department faculty are committed to training leaders of the next generation of neuroscientists, including graduate and medical students. Candidates for the Ph.D. in Neurobiology are admitted to the graduate Program in Neuroscience. This interdepartmental training program links the Department of Neurobiology with faculty in the Harvard affiliated hospitals and with faculty in other basic science departments. The Program, established in 1981, now includes about 90 investigators who participate in the training of Ph.D. candidates. Approximately fifteen students are accepted each year so that the steady state enrollment is usually about 80-90. This Program in Neuroscience attracts superb students with a broad range of interests from all areas of the globe. The goals of our training are to produce scientists who have explored one area and one level of analysis in great depth, but who are familiar with the full scope of neuroscience. They should be able to move from one level to another in a critical and creative manner. We also try to develop an appreciation for translational research that bears on human brain disease. The Department of Neurobiology, established in 1966 with Stephen W. Kuffler as Chair, was the first of its kind. The intent was to bring together members of traditional departments- physiologists, biochemists, and anatomists- in order to understand the principles governing communication between cells in the nervous system. This interdisciplinary approach was revolutionary at the time, and the interdisciplinary theme has continued to permeate the evolution of the field of neuroscience ever since. The Program in Neuroscience is one of four programs administered by the Division of Medical Sciences (DMS). DMS, located at the medical school, is a division of the Faculty of Arts and Sciences of Harvard University.

Proper citation: Harvard University Neurobiology (RRID:SCR_003368) Copy   

•   Source: SciCrunch Registry

https://osf.io/

Platform to support research and enable collaboration. Used to discover projects, data, materials, and collaborators helpful to your own research.

Proper citation: Open Science Framework (RRID:SCR_003238) Copy   

•   Source: SciCrunch Registry

http://www.elsevier.com/online-tools/pathway-studio/biological-database

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023. MedScan is a fast and flexible biomedical information extraction technology. It uses dictionaries to identify individual biomedical terms (proteins, cellular processes, small molecules, diseases, etc) referred to in literature articles, and applies advanced natural language processing techniques to detect the relationships within the article and extract these terms and the relationships; the overall process of detection, identification, extraction and assembling, is termed Information Harvesting. Information extracted by MedScan represents the multiple aspects of protein function, including protein modification, cellular localization, protein-protein interactions, gene expression regulation, molecular transport and synthesis, as well as association with diseases, and regulation of various cellular processes. This scope can be broadened by modifying information extraction rules and the dictionaries. Dictionaries can be assembled on any topic or area that is represented in the literature you wish to harvest. High-throughput data generation methodologies like microarray gene expression require new approaches for gathering information for data analysis. For the best results, computational approaches used for high-throughput data analysis require that biological information from the literature be a coherent and integrated part of the analysis software itself. Pathway Studio meets this challenge through its MedScan Technology and underlying ResNet database. All editions of Pathway Studio contain MedScan Technology to harvest information from the literature and to save this information in the Pathway Studio ResNet database ready for data analysis. MedScan is more than a web search engine. Indeed, the output of a Google search can be channeled into MedScan for example. Web searches, like Google, are excellent at finding items as a result of a query. A quick look at the output list usually locates the item for which you are looking. This approach however, is not well suited for information and knowledge gathering. Also, once information is gathered, where do you put it for later computational use? MedScan meets this challenge for the area of biomedical literature and biomedical online information. PubMed meets the needs for a central repository of biomedical literature. Researchers can go to PubMed and search for any topic and articles of interest, much like a web type of search. However, just like a web type of search, PubMed also provides a list of all the hits with a link to the articles. If a single article, or even just a few, are sought, this search approach is useful. Alternatively, MedScan will list all the articles of interest but additionally scans the text for relationships, highlights these relationships in the articles and then lists these relationships and the biological molecules and processes involved in the relationships in separate tables. The tables of relationships can be viewed graphically in Pathway Studio and can be saved into the ResNet database for use in experimental data analysis.

Proper citation: MedScan (RRID:SCR_003314) Copy   

•   Source: SciCrunch Registry

https://bitbucket.org/dranew/defuse

Software package for gene fusion discovery using RNA-Seq data. It uses clusters of discordant paired end alignments to inform a split read alignment analysis for finding fusion boundaries.

Proper citation: deFuse (RRID:SCR_003279) Copy   

•   Source: SciCrunch Registry

http://rostlab.org/services/nlsdb/

A database of nuclear localization signals (NLSs) and of nuclear proteins targeted to the nucleus by NLS motifs. NLSs are short stretches of residues mediating transport of nuclear proteins into the nucleus. The database contains 114 experimentally determined NLSs that were obtained through an extensive literature search. Using "in silico mutagenesis" this set was extended to 308 experimental and potential NLSs. This final set matched over 43% of all known nuclear proteins and matches no currently known non-nuclear protein. NLSdb contains over 6000 predicted nuclear proteins and their targeting signals from the PDB and SWISS-PROT/TrEMBL databases. The database also contains over 12 500 predicted nuclear proteins from six entirely sequenced eukaryotic proteomes (Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana and Saccharomyces cerevisiae). NLS motifs often co-localize with DNA-binding regions. This observation was used to also annotate over 1500 DNA-binding proteins. From this site you can: * Query NLSdb * Find out how to use NLSdb * Browse the entries in NLSdb * Find out if your protein has an NLS using PredictNLS * Predict subcellular localization of your protein using LOCtree

Proper citation: NLSdb: a database of nuclear localization signals (RRID:SCR_003273) Copy   

•   Source: SciCrunch Registry

http://vano.cellexplorer.org/

VANO is a Volume image object AnNOtation System for 3D multicolor image stacks, developed by Hanchuan Peng, Fuhui Long, and Gene Myers. VANO provides a well-coordinated way to annotate hundreds or thousands of 3D image objects. It combines 3D views of images and spread sheet neatly, and is just easy to manage 3D segmented image objects. It also lets you incorporate your segmentation priors, and lets you edit your segmentation results! This system has been used in building the first digital nuclei atlases of C. elegans at the post-embryonic stage (joint work with Stuart Kim lab, Stanford Univ), the single-neuron level fruit fly neuronal atlas of late embryos (with Chris Doe lab, Univ of Oregon, HHMI), and the compartment-level of digital map(s) of adult fruit fly brains (several labs at Janelia Farm, HHMI). VANO is cross-platform software. Currently the downloadable versions are for Windows (XP and Vista) and Mac (Intel-chip based, Leopard or Tiger OS). If you need VANO for different systems (such as 64bit or 32bit, Redhat Linux, Ubuntu, etc), you can either compile the software, or send an email to pengh (at) janelia.hhmi.org. VANO is Open-Source. You can download both the source code files and pre-complied versions at the Software Downloads page.

Proper citation: Volume image object AnNOtation System (RRID:SCR_003393) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/BIRN/Projects/Mouse/

Animal model data primarily focused on mice including high resolution MRI, light and electron microscopic data from normal and genetically modified mice. It also has atlases, and the Mouse BIRN Atlasing Toolkit (MBAT) which provides a 3D visual interface to spatially registered distributed brain data acquired across scales. The goal of the Mouse BIRN is to help scientists utilize model organism databases for analyzing experimental data. Mouse BIRN has ended. The next phase of this project is the Mouse Connectome Project (https://www.nitrc.org/projects/mcp/). The Mouse BIRN testbeds initially focused on mouse models of neurodegenerative diseases. Mouse BIRN testbed partners provide multi-modal, multi-scale reference image data of the mouse brain as well as genetic and genomic information linking genotype and brain phenotype. Researchers across six groups are pooling and analyzing multi-scale structural and functional data and integrating it with genomic and gene expression data acquired from the mouse brain. These correlated multi-scale analyses of data are providing a comprehensive basis upon which to interpret signals from the whole brain relative to the tissue and cellular alterations characteristic of the modeled disorder. BIRN's infrastructure is providing the collaborative tools to enable researchers with unique expertise and knowledge of the mouse an opportunity to work together on research relevant to pre-clinical mouse models of neurological disease. The Mouse BIRN also maintains a collaborative Web Wiki, which contains announcements, an FAQ, and much more.

Proper citation: Mouse Biomedical Informatics Research Network (RRID:SCR_003392) Copy   

•   Source: SciCrunch Registry

http://www.well.ox.ac.uk/

An international leader in genetics, genomics and structural biology, and research institute of the Nuffield Department of Medicine at the University of Oxford, whose objective is to extend our understanding on how genetic inheritance makes us who we are in order to gain a clearer insight into mechanisms of health and disease. Looking across all three billion letters of the human genetic code, they aim to pinpoint variant spellings and discover how they increase or decrease an individual's risk of falling ill. They collaborate with research teams across the world on a number of large-scale studies in these areas.

Proper citation: Wellcome Trust Centre for Human Genetics (RRID:SCR_003307) Copy   

•   Source: SciCrunch Registry

http://www.nematodes.org/

Nematode & Neglected Genomics (at) The Blaxter Lab is a nematode related portal including databases and services. Resources include genomic and transcriptomic databases for nematodes and other metazoan phyla and freely downloadable software tools for expressed sequence tag analysis, DNA barcode analysis and phylogenomics. Major categories include: * GenePool * 959 Nematode Genomes * Teaching * Research Projects * Bioinformatics Software Tools * Lab Personnel * Lab Wiki * Genomics Databases * NEMBASE4 * Tardigrada: Hypsibius dujardini * Earthworm: Lumbricus rubellus * MolluscDB * ArthropodDB * other Neglected Genomes

Proper citation: nematodes.org (RRID:SCR_003267) Copy   

•   Source: SciCrunch Registry

http://protege.stanford.edu

Protege is a free, open-source platform that provides a growing user community with a suite of tools to construct domain models and knowledge-based applications with ontologies. At its core, Protege implements a rich set of knowledge-modeling structures and actions that support the creation, visualization, and manipulation of ontologies in various representation formats. Protege can be customized to provide domain-friendly support for creating knowledge models and entering data. Further, Protege can be extended by way of a plug-in architecture and a Java-based Application Programming Interface (API) for building knowledge-based tools and applications. An ontology describes the concepts and relationships that are important in a particular domain, providing a vocabulary for that domain as well as a computerized specification of the meaning of terms used in the vocabulary. Ontologies range from taxonomies and classifications, database schemas, to fully axiomatized theories. In recent years, ontologies have been adopted in many business and scientific communities as a way to share, reuse and process domain knowledge. Ontologies are now central to many applications such as scientific knowledge portals, information management and integration systems, electronic commerce, and semantic web services. The Protege platform supports two main ways of modeling ontologies: * The Protege-Frames editor enables users to build and populate ontologies that are frame-based, in accordance with the Open Knowledge Base Connectivity protocol (OKBC). In this model, an ontology consists of a set of classes organized in a subsumption hierarchy to represent a domain's salient concepts, a set of slots associated to classes to describe their properties and relationships, and a set of instances of those classes - individual exemplars of the concepts that hold specific values for their properties. * The Protege-OWL editor enables users to build ontologies for the Semantic Web, in particular in the W3C's Web Ontology Language (OWL). An OWL ontology may include descriptions of classes, properties and their instances. Given such an ontology, the OWL formal semantics specifies how to derive its logical consequences, i.e. facts not literally present in the ontology, but entailed by the semantics. These entailments may be based on a single document or multiple distributed documents that have been combined using defined OWL mechanisms (see the OWL Web Ontology Language Guide). Protege is based on Java, is extensible, and provides a plug-and-play environment that makes it a flexible base for rapid prototyping and application development.

Proper citation: Protege (RRID:SCR_003299) Copy   

•   Source: SciCrunch Registry

http://www.genedata.com/products/expressionist/genomic-profiling.html

Software that provides data processing, analysis, management, and reporting of metabolomics, proteomics and biotherapeutics characterization studies based on mass spectrometry. It can process raw data from various MS instruments, serve MS processing, analysis and reporting needs, and ensure reproducibility and traceability of results.

Proper citation: Genedata Expressionist (RRID:SCR_003298) Copy   

•   Source: SciCrunch Registry

http://www.genome.gov/Glossary/

Glossary of Genetic Terms to help everyone understand the terms and concepts used in genetic research. In addition to definitions, specialists in the field of genetics share their descriptions of terms, and many terms include images, animation and links to related terms.

Proper citation: Talking Glossary of Genetic Terms (RRID:SCR_003215) Copy   

•   Source: SciCrunch Registry

http://edoctoring.ncl.ac.uk/Public_site/

Online educational tool that brings challenging clinical practice to your computer, providing medical education that is engaging, challenging and interactive. While there is no substitute for real-life direct contact with patients or colleagues, research has shown that interactive online education can be a highly effective and enjoyable method of learning many components of clinical medicine, including ethics, clinical management, epidemiology and communication skills. eDoctoring offers 25 simulated clinical cases, 15 interactive tutorials and a virtual library containing numerous articles, fast facts and video clips. Their learning material is arranged in the following content areas: * Ethical, Legal and Social Implications of Genetic Testing * Palliative and End-of-Life Care * Prostate Cancer Screening and Shared Decision-Making

Proper citation: eDoctoring (RRID:SCR_003336) Copy   

•   Source: SciCrunch Registry

http://phenome.jax.org/

Database enables integration of genomic and phenomic data by providing access to primary experimental data, data collection protocols and analysis tools. Data represent behavioral, morphological and physiological disease-related characteristics in naive mice and those exposed to drugs, environmental agents or other treatments. Collaborative standardized collection of measured data on laboratory mouse strains to characterize them in order to facilitate translational discoveries and to assist in selection of strains for experimental studies. Includes baseline phenotype data sets as well as studies of drug, diet, disease and aging effect., protocols, projects and publications, and SNP, variation and gene expression studies. Provides tools for online analysis. Data sets are voluntarily contributed by researchers from variety of institutions and settings, or retrieved by MPD staff from open public sources. MPD has three major types of strain-centric data sets: phenotype strain surveys, SNP and variation data, and gene expression strain surveys. MPD collects data on classical inbred strains as well as any fixed-genotype strains and derivatives that are openly acquirable by the research community. New panels include Collaborative Cross (CC) lines and Diversity Outbred (DO) populations. Phenotype data include measurements of behavior, hematology, bone mineral density, cholesterol levels, endocrine function, aging processes, addiction, neurosensory functions, and other biomedically relevant areas. Genotype data are primarily in the form of single-nucleotide polymorphisms (SNPs). MPD curates data into a common framework by standardizing mouse strain nomenclature, standardizing units (SI where feasible), evaluating data (completeness, statistical power, quality), categorizing phenotype data and linking to ontologies, conforming to internal style guides for titles, tags, and descriptions, and creating comprehensive protocol documentation including environmental parameters of the test animals. These elements are critical for experimental reproducibility.

Proper citation: Mouse Phenome Database (MPD) (RRID:SCR_003212) Copy   

•   Source: SciCrunch Registry

http://www.emdataresource.org/

Portal for deposition and retrieval of cryo electron microscopy (3DEM) density maps, atomic models, and associated metadata. Global resource for 3 Dimensional Electron Microscopy structure data archiving and retrieval, news, events, software tools, data standards, validation methods.

Proper citation: EMDataResource.org (RRID:SCR_003207) Copy   

•   Source: SciCrunch Registry

http://www.sysbio.se/piano/

Software R-package for running gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. The Piano package contains functions for combining the results of multiple runs of gene set analyses.

Proper citation: Piano (RRID:SCR_003200) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://cmb.molgen.mpg.de/2ndGenerationSequencing/Solas/

Software package for the statistical language R, devoted to the analysis of next generation short read data of RNA-seq transcripts. It provides predictions of alternative exons in a single condition/cell sample, predictions of differential alternative exons between two conditions/cell samples, and quantification of alternative splice forms in a single condition/cell sample.

Proper citation: Solas (RRID:SCR_003168) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/cancer/software/genepattern

A powerful genomic analysis platform that provides access to hundreds of tools for gene expression analysis, proteomics, SNP analysis, flow cytometry, RNA-seq analysis, and common data processing tasks. A web-based interface provides easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research.

Proper citation: GenePattern (RRID:SCR_003201) Copy   

•   Source: SciCrunch Registry