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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

http://www.idoimaging.com/program/280

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023.Comprised of a large array of sophisticated programs, this comprehensive software package with tools based around the MINC file format. Utilities are provided for conversion, viewing, editing, registering, segmentation, and a wide array of analysis. Many programs are in Perl. MINC software tools for neurological imaging are free. Input format: Analyze, DICOM, Minc

Proper citation: MINC Brain Imaging Toolbox (RRID:SCR_003519) Copy   

•   Source: SciCrunch Registry

http://www.nanostring.com/products/nSolver

Data analysis software program that offers nCounter users the ability to QC, normalize, and analyze data without having to purchase additional software packages.

Proper citation: nSolver Analysis Software (RRID:SCR_003420) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://www.nanoporetech.com/

Commercial organization developing a disruptive, proprietary technology platform for the direct, electronic analysis of single molecules. The instruments GridION and MinION are adaptable for the analysis of DNA, RNA, proteins, small molecules and other types of molecule. Consequently, the platform has a broad range of potential applications, including scientific research, personalized medicine, crop science and security / defence.

Proper citation: Oxford Nanopore Technologies (RRID:SCR_003756) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/src/contrib/Archive/iFad/

An R software package implementing a bayesian sparse factor model for the joint analysis of paired datasets, the gene expression and drug sensitivity profiles, measured across the same panel of samples, e.g. cell lines.

Proper citation: iFad (RRID:SCR_000271) Copy   

•   Source: SciCrunch Registry

https://www.qcif.edu.au/

Provides digital infrastructure capabilities for research and innovation across Queensland and Australia. Provides services, infrastructure and support for computation and data driven collaborative research and its application in industry. Members are six Queensland universities – The University of Queensland, Queensland University of Technology, Griffith University, James Cook University, CQUniversity, and the University of Southern Queensland. The University of the Sunshine Coast is an associate member. Member employees provide support and development services.

Proper citation: Queensland Cyber Infrastructure Foundation Ltd (RRID:SCR_000208) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/science/programs/genome-biology/computational-rd/computational-research-and-development

A software for genome assembly, and is specifically designed to analyze long Sanger-chemistry reads.

Proper citation: ARACHNE (RRID:SCR_000351) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/beckerjeremie/home/nucleofinder

A software for a statistical approach for the detection of nucleosome positions in a cell population. The software identifies important features of nucleosome organization such as the spacing downstream of active promoters and the enrichment and depletion of GC/AT dinucleotides of in vitro nucleosomes.

Proper citation: NucleoFinder (RRID:SCR_000368) Copy   

•   Source: SciCrunch Registry

http://graphpad.com/

A commercial graphing software company that offers scientific software for statistical analyses, curve fitting and data analysis. It offers four programs: Prism, InStat, StatMate and QuickCalcs.

Proper citation: GraphPad (RRID:SCR_000306) Copy   

•   Source: SciCrunch Registry

http://www.arb-home.de/

Software environment for maintaining databases of molecular sequences and additional information, and for analyzing the sequence data, with emphasis on phylogeny reconstruction. Programs have primarily been developed for ribosomal ribonucleic acid (rRNA) sequences and, therefore, contain special tools for alignment and analysis of these structures. However, other molecular sequence data can also be handled. Protein gene sequences and predicted protein primary structures as well as protein secondary structures can be stored in the same database. ARB package is designed for graphical user interface. Program control and data display are available in a hierarchical set of windows and subwindows. Majority of operations can be controlled using mouse for moving pointer and the left mouse button for initiating and performing operations.

Proper citation: ARB project (RRID:SCR_000515) Copy   

•   Source: SciCrunch Registry

http://www.megx.net

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. Set of databases and tools that handle genomic and metagenomic sequences in their environmental contexts.Includes geographic information system to systematically store and analyse marine genomic and metagenomic data in conjunction with contextual information; environmental genome browser with fast search functionalities; database with precomputed analyses for selected complete genomes; database and tool to classify metagenomic fragments based on oligonucleotide signatures.

Proper citation: MeGX (RRID:SCR_000738) Copy   

•   Source: SciCrunch Registry

http://franklin.imgen.bcm.tmc.edu/

The mission of the Baylor College of Medicine - Shaw Laboratory is to apply methods of statistics and bioinformatics to the analysis of large scale genomic data. Our vision is data integration to reveal the underlying connections between genes and processes in order to cure disease and improve healthcare.

Proper citation: Baylor College of Medicine - Shaw Laboratory (RRID:SCR_000604) Copy   

•   Source: SciCrunch Registry

http://harvard.eagle-i.net/i/0000012e-58c7-d44f-55da-381e80000000

Core to provide gene expression data analysis service. Activities range from the provision of services to fully collaborative grant funded investigations.

Proper citation: Harvard Partners HealthCare Center for Personalized Genetic Medicine Bioinformatics Core Facility (RRID:SCR_000882) Copy   

•   Source: SciCrunch Registry

http://www.rostlab.org/cms/

A lab organization which has bases in Munich, Germany and at Columbia University and focuses its research on protein structure and function using sequence and evolutionary information. They utilize machine learning and statistical methods to analyze genetic material and its gene products. Research goals of the lab involve using protein and DNA sequences along with evolutionary information to predict aspects of the proteins relevant to the advance of biomedical research.

Proper citation: ROSTLAB (RRID:SCR_000792) Copy   

•   Source: SciCrunch Registry

http://www.scienceexchange.com/facilities/macquarie-university

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 23,2023. Set of facilities based out of Macquarie University in New South Wales. Some facilities provide services such as proteome analysis or resources of various academic departments like engineering, biological sciences, and geography.

Proper citation: Macquarie University Labs and Facilities (RRID:SCR_000944) Copy   

•   Source: SciCrunch Registry

http://www.mevislab.de/index.php?id=6

Modular framework for the development of image processing algorithms and visualization and interaction methods, with a special focus on medical imaging. It includes advanced medical imaging modules for segmentation, registration, volumetry, and quantitative morphological and functional analysis. The platform allows fast integration and testing of new algorithms and the development of application prototypes that can be used in clinical environments. In MeVisLab, individual image processing, visualization and interaction modules can be combined to complex image processing networks using a graphical programming approach. The algorithms can easily be integrated using a modular, platform-independent C++ class library. An abstract, hierarchical definition language allows the design of efficient graphical user interfaces, hiding the complexity of the underlying module network to the end user. JavaScript components can be added to implement dynamic functionality on both the network and the user interface level. MeVisLab is based on the Qt application framework, the OpenInventor 3D visualization toolkit and OpenGL. Several clinical prototypes have been realized on the basis of MeVisLab, including software assistants for neuro-imaging, dynamic image analysis, surgery planning, and vessel analysis. Feature Overview: :- Basic image processing algorithms and advanced medical imaging modules :- Full featured, flexible 2D/3D visualization and interaction tools :- High performance for large datasets :- Modular, expandable C++ image processing library :- Graphical programming of complex, hierarchical module networks :- Object-oriented GUI definition and scripting :- Full scripting functionality using Python and JavaScript :- DICOM support and PACS integration :- Intuitive user interface :- Integrated movie and screenshot generation for demonstration purposes :- Generic integration of the Insight Toolkit (ITK) and the Visualization Toolkit (VTK) :- Cross-platform support for Windows, Linux, and MacOS X :- Available for 64-bit operating systems

Proper citation: Medical Image Processing and Visualization (RRID:SCR_002055) Copy   

•   Source: SciCrunch Registry

http://microarrays.curie.fr/publications/U900-RPPA_PLT/Normacurve/

Analysis methodology that allows simultaneous quantification and normalization of reverse phase protein array (RPPA) data.

Proper citation: NormaCurve (RRID:SCR_001995) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/voxbo

Software package for brain image manipulation and analysis, focusing on fMRI and lesion analysis. VoxBo can be used independently or in conjunction with other packages. It provides GLM-based statistical tools, an architecture for interoperability with other tools (they encourage users to incorporate SPM and FSL into their processing pipelines), an automation system, a system for parallel distributed computing, numerous stand-alone tools, decent wiki-based documentation, and lots more.

Proper citation: VoxBo (RRID:SCR_002166) Copy   

•   Source: SciCrunch Registry

https://phenogen.org

Website for analyzing microarray data. Software toolbox for storing, analyzing and integrating microarray data and related genotype and phenotype data. The site is particularly suited for combining QTL and microarray data to search for candidate genes contributing to complex traits. In addition, the site allows, if desired by the investigators, sharing of the data. Investigators can conduct in-silico microarray experiments using their own and/or shared data. There are five major sections of the site: Genome/Transcriptome Data Browser, Microarray Analysis Tools, Gene List Analysis Tools, QTL Tools, and Downloads. The genome/transcriptome data browser combines a genome browser with all the microarray, RNA-Seq, and Genomic Sequencing data. This provides an effective platform to view all of this data side by side. Source code is available on GitHub.

Proper citation: PhenoGen Informatics (RRID:SCR_001613) Copy   

•   Source: SciCrunch Registry