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http://www.jobtarget.com/corp/

JobTarget is the foundation upon which careers and companies are built. Our technology and services connect millions of job seekers to hundreds of thousands of companies through thousands of job boards. We facilitate faster, more meaningful connections by powering niche job sites that target specific audiences of talent, and providing technology and advertising solutions for employers to more quickly and cost-effectively locate the most qualified candidates for their openings. Explore our Products & Services to experience the JobTarget difference or check out Who We Serve to narrow in on the best solutions to serve you.

Proper citation: JobTarget (RRID:SCR_003365) Copy   

•   Source: SciCrunch Registry
  

https://github.com/ggloor/ALDEx2

Software tool to examine compositional high-throughput sequence data with Welch's t-test. A differential relative count abundance analysis for the comparison of two conditions. For example, single-organism and meta-rna-seq high-throughput sequencing assays, or of selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, that has been optimized for three or more experimental replicates. Infers sampling variation and calculates the expected Benjamini-Hochberg false discovery rate given the biological and sampling variation using several parametric and non-parametric tests. Can to glm and Kruskal-Wallace tests on one-way ANOVA style designs.

Proper citation: ALDEx2 (RRID:SCR_003364) Copy   

•   Source: SciCrunch Registry
  

http://www.reactome.org

Collection of pathways and pathway annotations. The core unit of the Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological interactions and are grouped into pathways (signaling, innate and acquired immune function, transcriptional regulation, translation, apoptosis and classical intermediary metabolism) . Provides website to navigate pathway knowledge and a suite of data analysis tools to support the pathway-based analysis of complex experimental and computational data sets.

Proper citation: Reactome (RRID:SCR_003485) Copy   

•   Source: SciCrunch Registry
  

https://neuro.georgetown.edu

Faculty of the Department of Neuroscience participate in the teaching of courses in the Interdisciplinary Program in Neuroscience and the School of Medicine. A Ph.D. in Neuroscience is offered through the Interdisciplinary Program in Neuroscience. Support for graduate training is offered through the Department, the research grants of individual faculty, as well as through three NIH training grants directed by Neuroscience faculty. * Training in Recovery of Function after CNS Injury. Program Director: Barbara S. Bregman, Ph.D. * Training Program in Drug Abuse. Program Director: Barbara S. Bayer, Ph.D. * Training in Neural Injury and Plasticity. Program Director: Jean R. Wrathall, Ph.D. Scientists in the Department of Neuroscience participate in a wide array of research activities with a focus on understanding both the normal and injured nervous system. The theme of neuroplasticity characterizes much of the research in the Department. We study neuroplasticity during normal development and in the adult in response to activity (e.g., learning) or drugs. Our research is also focused on studying the plasticity that ensues after traumatic (such as spinal cord injury) or ischemic damage to the nervous system and over the course of developmental or neurodegenerative diseases (such as Specific Language Impairment, autism, or Parkinson's and Alzheimer's Diseases). The specific research interests of each of the principal investigators falls under four broad subheadings: *CNS disorders ( Faden, Mocchetti, Rebeck, Riesenhuber,Ullman) *Cognitive/Computational (Riesenhuber, Ullman) *Development, Regeneration and recovery of function after injury (Bregman, Faden, Kromer, Ullman, Wrathall) *Neuroimmunology and Drugs of Abuse (Bayer, Faden, Kromer, Mocchetti) Under this common theme, a variety of diverse techniques and models are employed by the faculty. They range from molecular studies of gene function to studies on humans using Event-Related Potentials (ERPs) and functional MRI. Experimental models include cell culture systems, rodent genetic and experimental models of nervous system injury and disorders, as well as the use of computer simulations to understand higher cortical processing.

Proper citation: Georgetown, Neuroscience (RRID:SCR_003363) Copy   

•   Source: SciCrunch Registry
  

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

An application ontology to cover all aspects of malaria (clinical, epidemiological, biological, etc) as well as the intervention attempts to control it, extending the infectious disease ontology (IDO).

Proper citation: Malaria Ontology (RRID:SCR_003369) Copy   

•   Source: SciCrunch Registry
  

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
  

http://www.hsph.harvard.edu/skat/metaskat/

A R package for multiple marker meta-analysis.

Proper citation: MetaSKAT (RRID:SCR_003489) Copy   

•   Source: SciCrunch Registry
  

http://genomesavant.com/p/home/index

Next-generation genome browser software designed for the latest generation of genome data.

Proper citation: Savant (RRID:SCR_003488) Copy   

•   Source: SciCrunch Registry
  

http://cng.gmu.edu:8080/Lm

A freely available software tool available for the Windows and Linux platform, as well as the Online version Applet, for the analysis, comparison and search of digital reconstructions of neuronal morphologies. For the quantitative characterization of neuronal morphology, LM computes a large number of neuroanatomical parameters from 3D digital reconstruction files starting from and combining a set of core metrics. After more than six years of development and use in the neuroscience community, LM enables the execution of commonly adopted analyses as well as of more advanced functions, including: (i) extraction of basic morphological parameters, (ii) computation of frequency distributions, (iii) measurements from user-specified subregions of the neuronal arbors, (iv) statistical comparison between two groups of cells and (v) filtered selections and searches from collections of neurons based on any Boolean combination of the available morphometric measures. These functionalities are easily accessed and deployed through a user-friendly graphical interface and typically execute within few minutes on a set of 20 neurons. The tool is available for either online use on any Java-enabled browser and platform or may be downloaded for local execution under Windows and Linux.

Proper citation: L-Measure (RRID:SCR_003487) Copy   

•   Source: SciCrunch Registry
  

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

A Java interface to the mzTab data exchange format for reporting a summary of proteomics results.

Proper citation: jmzTab (RRID:SCR_003481) Copy   

•   Source: SciCrunch Registry
  

http://images.nigms.nih.gov/

Database of scientific photos, illustrations, and videos made available by the National Institute of General Medical Sciences.

Proper citation: National Institute of General Medical Sciences Image Gallery (RRID:SCR_003480) Copy   

•   Source: SciCrunch Registry
  

http://www.bioconductor.org/packages/release/bioc/html/ddCt.html

Software package providing an approximation method to determine relative gene expression with quantitative real-time PCR (qRT-PCR) experiments. It requires no standard curve for each primer-target pair, therefore reducing the working load and yet returning accurate enough results as long as the assumptions of the amplification efficiency hold. The package implements a pipeline to collect, analyze and visualize qRT-PCR results, for example those from TaqMan SDM software, mainly using the ddCt method. The pipeline can be either invoked by a script in command-line or through the API consisting of S4-Classes, methods and functions.

Proper citation: ddCt (RRID:SCR_003396) 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://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://moma.dk/normfinder-software

Software for identifying the optimal normalization gene among a set of candidates. It ranks the set of candidate normalization genes according to their expression stability in a given sample set and given experimental design. It can analyze expression data obtained through any quantitative method e.g. real time RT-PCR and microarray based expression analysis. NormFinder.xla adds the NormFinder functionality directly to Excel. A version for R is also available.

Proper citation: NormFinder (RRID:SCR_003387) Copy   

•   Source: SciCrunch Registry
  

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

An ontology of minimal set of terms for anatomy.

Proper citation: Minimal Anatomical Terminology (RRID:SCR_003385) 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.bioontology.org/wiki/index.php/CARO:Main_Page

An ontology developed to facilitate interoperability between existing anatomy ontologies for different species, and to provide a template for building new anatomy ontologies.

Proper citation: Common Anatomy Reference Ontology (RRID:SCR_003296) Copy   

•   Source: SciCrunch Registry