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http://www.bioinf.jku.at/software/farms/farms.html

Software using a model-based technique for summarizing high-density oligonucleotide array data at probe level for Affymetrix GeneChips. It is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise.

Proper citation: FARMS (RRID:SCR_001344) Copy   

•   Source: SciCrunch Registry

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

Software package that allows to characterize the operating characteristics of a microarray experiment, i.e. the trade-off between false discovery rate and the power to detect truly regulated genes. The package includes tools both for planned experiments (for sample size assessment) and for already collected data (identification of differentially expressed genes).

Proper citation: OCplus (RRID:SCR_001342) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages//2.11/bioc/html/bridge.html

Software package to test for differentially expressed genes with microarray data. It can be used with both cDNA microarrays or Affymetrix chip. The packge fits a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a $t$-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. The model can be used for testing for differentially expressed genes among multiple samples, and can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space.

Proper citation: bridge (RRID:SCR_001343) Copy   

•   Source: SciCrunch Registry

https://www.bioconductor.org/packages//2.12/bioc/html/CALIB.html

Software package that contains functions for normalizing spotted microarray data, based on a physically motivated calibration model. The model parameters and error distributions are estimated from external control spikes.

Proper citation: CALIB (RRID:SCR_001338) Copy   

•   Source: SciCrunch Registry

http://www.morpholinodatabase.org/

Central database to house data on morpholino screens currently containing over 700 morpholinos including control and multiple morpholinos against the same target. A publicly accessible sequence-based search opens this database for morpholinos against a particular target for the zebrafish community. Morpholino Screens: They set out to identify all cotranslationally translocated genes in the zebrafish genome (Secretome/CTT-ome). Morpholinos were designed against putative secreted/CTT targets and injected into 1-4 cell stage zebrafish embryos. The embryos were observed over a 5 day period for defects in several different systems. The first screen examined 184 gene targets of which 26 demonstrated defects of interest (Pickart et al. 2006). A collaboration with the Verfaillie laboratory examined the knockdown of targets identified in a comparative microarray analysis of hematopoietic stem cells demonstrating how microarray and morpholino technologies can be used in conjunction to enrich for defects in specific developmental processes. Currently, many collaborations are underway to identify genes involved in morphological, kidney, skin, eye, pigment, vascular and hematopoietic development, lipid metabolism and more. The screen types referred to in the search functions are the specific areas of development that were examined during the various screens, which include behavior, general morphology, pigmentation, toxicity, Pax2 expression, and development of the craniofacial structures, eyes, kidneys, pituitary, and skin. Only data pertaining to specific tests performed are presented. Due to the complexity of this international collaboration and time constraints, not all morpholinos were subjected to all screen types. They are currently expanding public access to the database. In the future we will provide: * Mortality curves and dose range for each morpholino * Preliminary data regarding the effectiveness of each morpholino * Expanded annotation for each morpholino * External linkage of our morpholino sequences to ZFIN and Ensembl. To submit morpholino-knockdown results to MODB please contact the administrator for a user name and password.

Proper citation: Morpholino Database (RRID:SCR_001378) Copy   

•   Source: SciCrunch Registry

http://neuro.imm.dtu.dk/wiki/Main_Page

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 10, 2025. Semantic wiki with structured information, primarily from functional and molecular neuroimaging papers, but there are also other types of papers, e.g., from personality genetics. It lists results from neuroimaging studies, such as Talairach coordinates and brain volume measurements, as well as software packages and brain regions. SQL dumps of the structured information in the wiki is available so complex queries can be formed. The Brede Wiki templates store the structured information from neuroscience papers and editors may add free format text. Template definitions format the data so it is presented as tables on the formatted wiki-page. From a given PMID a web-service can format information from PubMed for inclusion in the Brede Wiki. A Matlab script can extract coordinates from SPM5 and format them in the Talairach coordinate template format.

Proper citation: Brede Wiki (RRID:SCR_001411) Copy   

•   Source: SciCrunch Registry

http://www.g-node.org/projects/odml

Mark up language for collecting and exchanging metadata in an automated, computer-based fashion, developed for neuroscience, specifically, neurophysiology experiments. In odML arbitrary metadata information is stored as extended key-value pairs in a hierarchical structure. Central to odML is a clear separation of format and content, i.e., neither keys nor values are defined by the format. This makes odML flexible enough for storing all available metadata instantly without the necessity to submit new keys to an ontology or controlled terminology. Common standard keys can be defined in odML-terminologies for guaranteeing interoperability.

Proper citation: Open metadata mark up language (RRID:SCR_001376) Copy   

•   Source: SciCrunch Registry

http://ausweb.scu.edu.au/aw06/papers/refereed/sefton/paper.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The Integrated Content Environment (ICE) is a free Word-processor based system that allows authors to work individually or collaboratively on material for the Web, CD and print. This free web content management system, produced by the University of Southern Queensland, was initially developed by staff at the university to produce course content for online and print delivery. It has also been used for general web site development, and to manage documents in project intranets.

Proper citation: Integrated Content Environment (RRID:SCR_001369) Copy   

•   Source: SciCrunch Registry

http://scitools.idtdna.com/analyzer/Applications/OligoAnalyzer/

Web-based application for analyzing oligonucleotides. Analysis proceeds after the sequence has been entered and the calculations modified based on target type, oligo concentration, sodium ion concentration, magnesium ion concentration, and dNTP concentration.

Proper citation: Integrated DNA Technologies OligoAnalyzer (RRID:SCR_001363) Copy   

•   Source: SciCrunch Registry

https://www.unafold.org/

Software package for nucleic acid folding and hybridization prediction. It has capabilities to predict folding for single-stranded RNA or DNA through a combination of free energy minimization, partition function calculations and stochastic sampling. The program runs on Unix and Linux platforms as well as Mac OS X and Windows.

Proper citation: UNAFold (RRID:SCR_001360) Copy   

•   Source: SciCrunch Registry

http://isp.imm.dtu.dk/thor/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022.Center hosting a number of related projects concerning neural networks, functional neuroimaging, multimedia signal processing, and biomedical signal processing. Neuroinformatics is a research field rooted in classical disciplines like signal processing, biology, physics, computer science and engineering. Neuroinformatics combines learning from the brain and learning about the brain. By studying information processing in the brain neuroinformatics invents new computing paradigms (e.g., artificial neural networks) with the objective of understanding the dynamics of the conscious mind. Artificial neural networks is an active neuroinformatics research field, which combines many approaches to adaptive signal processing in solving real world problems. They began using neural networks for general nonlinear adaptive signal processing. Since 1991 the CONNECT groups have participated in the development of neural computing as an advanced, non-linear statistical tool, which has been applied to forecasting within dynamical systems, pattern recognition, and medical image analysis, particularly functional neuroimages. While neural computing has largely been viewed as a black box approach, they have initiated research aimed at opening this black box, using hypertext, multimedia, and interactivity. Their key objective is to convert abstract models into intuitive knowledge through interactive visualization.

Proper citation: THOR Center for Neuroinformatics (RRID:SCR_001400) Copy   

•   Source: SciCrunch Registry

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

Software library used to do significance analysis of microarray data with small number of replicates. It uses resampling based FDR adjustment, and gives less conservative results than traditional "BH" or "BY" procedures. Data accepted is raw data in txt format from MAS4, MAS5 or dChip. Data can also be supplied after normalization. LPE library is primarily used for analyzing data between two conditions.

Proper citation: LPE (RRID:SCR_001364) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software package for Multipoint QTL Mapping in Genetically Heterogeneous Animals (entry from Genetic Analysis Software) The method is implemented in a C-program and there is now an R version of HAPPY. You can run HAPPY remotely from their web server using your own data (or try it out on the data provided for download).

Proper citation: Happy (RRID:SCR_001395) Copy   

•   Source: SciCrunch Registry

http://www.retinalmaps.com.au/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. A database of over 700 retinal topography maps of a wide variety of species published in a diversity of journals. It has been assembled to assist vision and neuroscience researchers to locate and compare the distribution of retinal neurons within and across species. The maps can be searched by taxonomic or common name classification, cell type sampled, type of retinal specialization and staining/visualization method. Maps can be compared by selecting multiple maps and clicking the Compare Selected button. An interactive spreadsheet can be also downloaded.

Proper citation: Retinal Topography Maps Database (RRID:SCR_001399) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/aroma.light.html

Light-weight software package for normalization and visualization of microarray data using only basic R data types. Software can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes.

Proper citation: aroma.light (RRID:SCR_001312) Copy   

•   Source: SciCrunch Registry

http://www.utdallas.edu/~kilgard/lectures.htm

List of lectures, slides and videos concerning neuroscience and neurophysiology.

Proper citation: Online Neuroscience Lectures - Maintained by the Kilgard Lab (RRID:SCR_001397) Copy   

•   Source: SciCrunch Registry

http://www.nb.uw.edu/

Biomedical technology research center that provides state-of-the-art surface analysis expertise, instrumentation, experimental protocols, and data analysis methods to address surface-related biomedical problems. NESAC/BIO develops and applies surface science methodologies that produce a full understanding of the surface composition, structure, spatial distribution, and orientation of biomaterials and adsorbed biomolecules. The NESAC/BIO program identifies areas where surface science must evolve to keep pace with the growth in biochemical knowledge and biomaterial fabrication technology, and develops instrumentation, experimental protocols, and data analysis methods to achieve this evolution. NESAC/BIO provides state-of-the-art surface analysis tools to researchers in the biomedical community. You can gain access to the NESAC/BIO facilities in one of the following ways: * Collaborative: Propose a project to collaborate on with NESAC/BIO. The project should be rewarding for both groups, and the results should reflect the utility of surface analysis for biomedical research * Service: Ask NESAC/BIO to analyze your biomaterial specimens. The spectra obtained from the analyses will be interpreted for you. * Training: Visit the University of Washington to receive training in surface analysis and personally run experiments for your individual research projects. These experiments should have a high probability for yielding useful information and should not involve the development of new ESCA techniques or methodologies.

Proper citation: National ESCA and Surface Analysis Center for Biomedical Problems (RRID:SCR_001430) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/2.13/bioc/html/BeadDataPackR.html

Software that provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform.

Proper citation: BeadDataPackR (RRID:SCR_001310) Copy   

•   Source: SciCrunch Registry

https://www.mristudio.org/

An image processing program running under Windows suitable for such tasks as tensor calculation, color mapping, fiber tracking, and 3D visualization. Most of operations can be done with only a few clicks. This tool evolved from DTI Studio. Tools in the program can be grouped in the following way: * Image Viewer * Diffusion Tensor Calculations * Fiber Tracking and Editing * 3D Visualization * Image File Management * Region of Interesting (ROI) Drawing and Statistics * Image Registration

Proper citation: MRI Studio (RRID:SCR_001398) Copy   

•   Source: SciCrunch Registry

http://bmsr.usc.edu/software/pneuma/

A set of modules that are used to simulate the autoregulation of the cardiovascular and respiratory systems under conditions of changing sleep-wake state and a variety of physiological and pharmacological interventions. It models the dynamic interactions that take place among the various component mechanisms, including those involved in the chemical control of breathing, heart rate, and blood pressure, as well as the effects of changes in the sleep-wake state and arousal from sleep. PNEUMA includes the autonomic control of the cardiovascular system, chemoreflex and state-related control of breath-to-breath ventilation, state-related and chemoreflex control of upper airway potency, as well as respiratory and circulatory mechanics. The model is capable of simulating the cardiorespiratory responses to sleep onset, arousal, continuous positive airway pressure, the administration of inhaled carbon dioxide and oxygen, Valsalva and Mueller maneuvers, and Cheyne-Stokes respiration during sleep. In PNEUMA 3.0, we have extended the existing integrative model of respiratory, cardiovascular, and sleepwake state control, to incorporate a sub-model of glucoseinsulinfatty acid regulation. The extended model is capable of simulating the metabolic control of glucoseinsulin dynamics and its interactions with the autonomic nervous system. The interactions between autonomic and metabolic control include the circadian regulation of epinephrine secretion, epinephrine regulation on dynamic fluctuations in glucose and free fatty acids in plasma, metabolic coupling among tissues and organs mediated by insulin and epinephrine, as well as the effect of insulin on peripheral vascular sympathetic activity. This extended model represents a starting point from which further in silico investigations into the interaction between the autonomic nervous system and the metabolic control system can proceed. Features in PNEUMA 3.0 * Incorporates metabolic component based on prior models of glucose-insulin regulation and free fatty acid (FFA) regulation. * Changes in sympathetic activity from the autonomic portion of PNEUMA produce changes in epinephrine output, which in turn affects the metabolic sub-model. * Inputs from the dietary intake of glucose and external interventions, such as insulin injections, have also been incorporated. * Also incorporated is autonomic feedback from the metabolic component to the rest of PNEUMA: changes in insulin level lead to changes in sympathetic tone. System Requirements: PNEUMA requires Matlab R2007b or higher with the accompanying version of Simulink to be installed on your computer.

Proper citation: PNEUMA (RRID:SCR_001391) Copy   

•   Source: SciCrunch Registry