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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

http://ilyinlab.org/friend/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Friend is a bioinformatics application designed for simultaneous analysis and visualization of multiple structures and sequences of proteins and/or DNA/RNA. The application provides basic functionalities such as: structure visualization with different rendering and coloring, sequence alignment, and simple phylogeny analysis, along with a number of extended features to perform more complex analyses of sequence structure relationships, including: structural alignment of proteins, investigation of specific interaction motifs, studies of protein-protein and protein-DNA interactions, and protein super-families. Friend is also useful for the functional annotation of proteins, protein modeling, and protein folding studies. Friend provides three levels of usage; 1) an extensive GUI for a scientist with no programming experience, 2) a command line interface for scripting for a scientist with some programming experience, and 3) the ability to extend Friend with user written libraries for an experienced programmer. The application is linked and communicates with local and remote sequence and structure databases.

Proper citation: An Integrated Multiple Structure Visualization and Multiple Sequence Alignment Application (RRID:SCR_001646) Copy   

•   Source: SciCrunch Registry

http://surfer.nmr.mgh.harvard.edu/

Open source software suite for processing and analyzing human brain MRI images. Used for reconstruction of brain cortical surface from structural MRI data, and overlay of functional MRI data onto reconstructed surface. Contains automatic structural imaging stream for processing cross sectional and longitudinal data. Provides anatomical analysis tools, including: representation of cortical surface between white and gray matter, representation of the pial surface, segmentation of white matter from rest of brain, skull stripping, B1 bias field correction, nonlinear registration of cortical surface of individual with stereotaxic atlas, labeling of regions of cortical surface, statistical analysis of group morphometry differences, and labeling of subcortical brain structures.Operating System: Linux, macOS.

Proper citation: FreeSurfer (RRID:SCR_001847) Copy   

•   Source: SciCrunch Registry

http://www.nesys.uio.no/Atlas3D/

A multi-platform visualization tool which allows import and visualization of 3-D atlas structures in combination with tomographic and histological image data. The tool allows visualization and analysis of the reconstructed atlas framework, surface modeling and rotation of selected structures, user-defined slicing at any chosen angle, and import of data produced by the user for merging with the atlas framework. Tomographic image data in NIfTI (Neuroimaging Informatics Technology Initiative) file format, VRML and PNG files can be imported and visualized within the atlas framework. XYZ coordinate lists are also supported. Atlases that are available with the tool include mouse brain structures (3-D reconstructed from The Mouse Brain in Stereotaxic Coordinates by Paxinos and Franklin (2001)) and rat brain structures (3-D reconstructed from The Rat Brain in Stereotaxic Coordinates by Paxinos and Watson (2005)). Experimental data can be imported in Atlas3D and warped to atlas space, using manual linear registration, with the possibility to scale, rotate, and position the imported data. This facilitates assignment of location and comparative analysis of signal location in tomographic images.

Proper citation: Atlas3D (RRID:SCR_001808) 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://icahn.mssm.edu/research/resources/shared-resource-facilities/in-vivo-molecular-imaging

The In-Vivo Molecular Imaging Laboratory (IMIL) is a MSSM shared resource facility serving the research community of Mount Sinai with equipment and imaging expertise. State-of-the-art bioluminescent as well as fluorescent imaging modalities are supported for in-vivo monitoring of cellular and genetic activity. Investigators are provided with cutting edge imaging technologies as well as analysis techniques. The long-term goal is to establish a comprehensive SRF for in-vivo molecular imaging using micro-MRI, micro-PET and other modalities. IMIL houses a Xenogen IVIS-200 Series imaging system with the integrated fluorescent imaging options. Simultaneous dual reporter in-vivo imaging is possible with bioluminescence and fluorescence probes. The imaging chamber has a gas anesthesia manifold that can accommodate up to 5 mice for simultaneously image acquisition. Selectable field of views allow in-plane (X,Y) imaging resolutions of up to 60-microm. Integrated spectra filters allow for the determination of signal source depth (Z). IMIL will provide data acquisition services as well as analysis. IMIL has a dedicated imaging technologist for data acquisition. Investigators will bring their prepared animal to the lab and an IMIL imaging technologist will assist in sedating the animals and acquire imaging data. Typical imaging sessions last about an hour. Certified users who are trained in the use of the software will be able to perform their own analysis at the console. Usage of the imaging device is charged by the hour ($100/hour). Structural Imaging The IVIS-200 has the built-in capability of obtaining an image of the surface topography of the animal for 2D and 3D localization. If additional true 3D imaging data is required, micro MRI is available through the Imaging Science Laboratories (ISL). Image Analysis The IVIS-200 has an integrated image acquisition and analysis software (Living Image Software 2.50). Comprehensive data quantification is possible with this software. Raw data as well as analyzed results can be electronically transferred to the investigators. Support is also available for additional image analysis such as intermodality coregistration, 3D rendering, and group statistics. Additional software packages include MedX, SPM, Brainvoyager, Analyze, and in-house developed software.

Proper citation: Mount Sinai School of Medicine: In-Vivo Molecular Imaging Laboratory (RRID:SCR_001785) Copy   

•   Source: SciCrunch Registry

http://meme-suite.org/

Suite of motif-based sequence analysis tools to discover motifs using MEME, DREME (DNA only) or GLAM2 on groups of related DNA or protein sequences; search sequence databases with motifs using MAST, FIMO, MCAST or GLAM2SCAN; compare a motif to all motifs in a database of motifs; associate motifs with Gene Ontology terms via their putative target genes, and analyze motif enrichment using SpaMo or CentriMo. Source code, binaries and a web server are freely available for noncommercial use.

Proper citation: MEME Suite - Motif-based sequence analysis tools (RRID:SCR_001783) Copy   

•   Source: SciCrunch Registry

http://www.nesys.uio.no/Micro3D/

The Micro3D 2004 is a software for 3-D reconstruction, visualization, and analysis of neuronal populations and brain regions. Micro3D generates geometric models from line and point coded data sets, representing labeled objects such as cell bodies or axonal plexuses, and boundaries of brain regions in serial sections. Data are typically imported from image-combining computerized microscopy systems, such as Neurolucida (MicroBrightField, Colchester, VT). The models may be rotated and zoomed in real-time. Surfaces are re-synthesized on the basis of stacks of contour lines. Clipping is used for defining section-independent subdivisions of the model. Flattening of sheets of points in curved layers (e.g., neurons in a cortical lamina) facilitates inspection of complicated distribution patterns. Micro3D computes color-coded density maps, and allows production of mpeg videos. Micro3D 2004 runs on LINUX PCs equipped with Open Inventor. It performs operations similar to the Silicon Graphics based version that has been used in more than 25 investigations and in various species, ranging from insects to monkeys, at the LM- and EM-level. Sponsors:Micro 3D was developed with support from The Research Council of Norway and The Oslo Research Park / FORNY.

Proper citation: Neural Systems and Graphics Computing Laboratory: Micro3D Software (RRID:SCR_001811) 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

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://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.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.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://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.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

http://jcb-dataviewer.rupress.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. A web-based, multi-dimensional image data-viewing application for original microscopy image datasets associated with articles published in The Journal of Cell Biology, a peer-reviewed journal published by The Rockefeller University Press. The JCB DataViewer can host multidimensional fluorescence microscopy images, 3D tomogram data, very large (gigapixel) images, and high content imaging screens. Images are presented in an interactive viewer, and the scores from high content screens are presented in interactive graphs with data points linked to the relevant images. The JCB DataViewer uses the Bio-Formats library to read over 120 different imaging file formats and convert them to the OME-TIFF image data standard. Image data are archived by the Journal and may be freely accessed by readers using the JCB DataViewer. Download of author-provided image data and associated metadata in OME-TIFF format is also possible with author permission, allowing for independent analysis of image data irrespective of acquisition or viewing software. Although the JCB DataViewer is designed to host and facilitate sharing and analysis of original microscopy image data, authors may also upload other types of original image data as supplements to their manuscripts, including histology and electron micrographs and digital scans of gels or blots.

Proper citation: JCB DataViewer (RRID:SCR_002633) Copy   

•   Source: SciCrunch Registry

http://www.fmrib.ox.ac.uk/fsl/

Software library of image analysis and statistical tools for fMRI, MRI and DTI brain imaging data. Include registration, atlases, diffusion MRI tools for parameter reconstruction and probabilistic taractography, and viewer. Several brain atlases, integrated into FSLView and Featquery, allow viewing of structural and cytoarchitectonic standard space labels and probability maps for cortical and subcortical structures and white matter tracts. Includes Harvard-Oxford cortical and subcortical structural atlases, Julich histological atlas, JHU DTI-based white-matter atlases, Oxford thalamic connectivity atlas, Talairach atlas, MNI structural atlas, and Cerebellum atlas.

Proper citation: FSL (RRID:SCR_002823) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/bio-rainbow/

Software developed to provide an ultra-fast and memory-efficient solution to clustering and assembling short reads produced by RAD-seq.

Proper citation: Rainbow (RRID:SCR_002724) Copy   

•   Source: SciCrunch Registry