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https://plantcyc.org/content/plantcyc-15.2.0

Multi species reference database. Comprehensive plant biochemical pathway database, containing curated information from literature and computational analyses about genes, enzymes, compounds, reactions, and pathways involved in primary and secondary metabolism.

Proper citation: PlantCyc (RRID:SCR_002110) Copy   

•   Source: SciCrunch Registry

http://www.imagescience.org/meijering/software/neuronj/

NeuronJ is an ImageJ plugin to facilitate the tracing and quantification of elongated structures in two-dimensional (2D) images (8-bit gray-scale and indexed color), in particular neurites in fluorescence microscopy images. Sponsors: The development of NeuronJ started while the primary developer ( Dr. Erik Meijering, PhD) was with the Biomedical Imaging Group (collaborating with people from the Laboratory of Cellular Neurobiology) of the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, and was finished while Dr. Meijering was with the Biomedical Imaging Group Rotterdam in the Netherlands.

Proper citation: NeuronJ: An ImageJ Plugin for Neurite Tracing and Quantification (RRID:SCR_002074) Copy   

•   Source: SciCrunch Registry

http://www.fged.org/

Society that develop standards for biological research data quality, annotation and exchange. They facilitate the creation and use of software tools that build on these standards and allow researchers to annotate and share their data easily. They promote scientific discovery that is driven by genome wide and other biological research data integration and meta-analysis. Historically, FGED began with a focus on microarrays and gene expression data. However, the scope of FGED now includes data generated using any technology when applied to genome-scale studies of gene expression, binding, modification and other related applications.

Proper citation: FGED (RRID:SCR_001897) Copy   

•   Source: SciCrunch Registry

http://www.jainlab.org/downloads.html

Automatic software program for microarray image quantification.

Proper citation: UCSF Spot (RRID:SCR_002985) Copy   

•   Source: SciCrunch Registry

http://lab.rockefeller.edu/casanova/HGC

Data set containing a gene-specific connectome file for each human gene and computer programs for ranking lists of genes within a gene-specific connectome, clustering and plotting the genes by the functional genomic alignment (FGA) approach, and generating gene-specific connectomes. The programs were developed and tested on Mac and Linux systems. The external software required for running these programs is open-source and free of charge. The HGC is the set of all biologically plausible routes, distances, and degrees of separation between all pairs of human genes. A gene-specific connectome contains the set of all available human genes sorted on the basis of their predicted biological proximity to the specific gene of interest. The HGC is a powerful approach for human genotype-phenotype high-throughput studies, for which it can be used to rank any list of genes within a gene-specific connectome for an experimentally validated core gene. Functional genomic alignment (FGA) is equivalent to traditional multiple sequence alignment (MSA), except that it clusters genes in trees on the basis of the functional biological distance between them predicted by HGC, rather than on the basis of molecular evolutionary genetic distance. This method is therefore more suitable for disease and phenotypic studies.

Proper citation: Human Gene Connectome (RRID:SCR_002628) Copy   

•   Source: SciCrunch Registry

http://g2d2.ogic.ca

The Institute for Advanced Biosciences, Keio University, is an academic research institute pioneering the new life science field of Systems Biology, using both experimental and computational biology. There are several groups working in collaboration, focusing mainly on genome biology and engineering, genome design and synthetic biology, metabolic engineering, proteomics, metabolomics, RNA biology, bioinformatics and computational biology. Using cutting-edge technologies, intracellular components can be analyzed comprehensively to construct computer simulation models that can find numerous applications in fields such as biomedical, environmental, and agricultural science. Experimental and computational facilities are located in Tsuruoka, Yamagata prefecture, in northern Japan while the SFC campus, in the Tokyo area, hosts the bioinformatics laboratory and most undergraduate curricular activities. IAB has successfully attracted very significant funding for multiple research projects from major funding organizations including the New Energy and Industrial Technology Development Organization (NEDO) (2002-2006), for bioprocesses and cell modeling, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and its COE network, for biosimulation and systems biology (2003-2008), the Japan Science and Technology Agency (CREST, 2004-2009) for simulation and systems biology, the Ministry of Health, for cancer biology (2005), as well as from Yamagata prefecture and Tsuruoka city, 2001-2006 and 2006-2011). Over the past few years, IAB scientists have accumulated several awards including the 1st prize during the 5th Japan Biotechnology Business Competition (2005), the Minister of State for Science and Technology Policy award in recognition for industry-academia-government collaboration performance (2004), the IBM Shared University Research Award (2003), and the Nihon Kogyo Shimbunsha Award (2003) during the 17th Leading-edge Technology for Originality and Creativity. Sponsor. This study was supported by a grant from the Global COE Program entitled, Human Metabolomic Systems Biology and by a Grant-in-Aid for Scientific Research on Priority Areas Systems Genomes and on Lifesurveyor from the Ministry of Education, Culture, Sports, Science and Technology of Japan as well as research funds from the Yamagata prefectural government and the City of Tsuruoka.

Proper citation: Institute for Advanced Biosciences (RRID:SCR_008526) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.biol.rug.nl/standalone/fiva/

Functional Information Viewer and Analyzer (FIVA) aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software is able to assist in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes. Currently, seven different modules containing functional information have been implemented: (i) gene regulatory interactions, (ii) cluster of orthologous groups (COG) of proteins, (iii) gene ontologies (GO), (iv) metabolic pathways (v) Swiss Prot keywords, (vi) InterPro domains - and (vii) generic functional categories. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FIVA - Functional Information Viewer and Analyzer (RRID:SCR_005776) Copy   

•   Source: SciCrunch Registry

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

Pipeline developed for delineating the association between multiple diffusion properties along major white matter fiber bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these white matter tract properties in various diffusion tensor imaging studies. FADTTS can be used to facilitate understanding of normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. The advantages of FADTTS compared with the other existing approaches are that they are capable of modelling the structured inter-subject variability, testing the joint effects, and constructing their simultaneous confidence bands.

Proper citation: Functional Analysis of Diffusion Tensor (RRID:SCR_008888) Copy   

•   Source: SciCrunch Registry

http://galton.uchicago.edu/~junzhang/LAPSTRUCT.html

Software application to describe population structure using biomarker data ( typically SNPs, CNVs etc.) available in a population sample. The main features different from PCA are: (1) geometrically motivated and graphic model based; (2)robustness of outliers. (entry from Genetic Analysis Software)

Proper citation: LAPSTRUCT (RRID:SCR_007550) Copy   

•   Source: SciCrunch Registry

http://senselab.med.yale.edu

The SenseLab Project is a long-term effort to build integrated, multidisciplinary models of neurons and neural systems. It was founded in 1993 as part of the original Human Brain Project, which began the development of neuroinformatics tools in support of neuroscience research. It is now part of the Neuroscience Information Framework (NIF) and the International Neuroinformatics Coordinating Facility (INCF). The SenseLab project involves novel informatics approaches to constructing databases and database tools for collecting and analyzing neuroscience information, using the olfactory system as a model, with extension to other brain systems. SenseLab contains seven related databases that support experimental and theoretical research on the membrane properties: CellPropDB, NeuronDB, ModelDB, ORDB, OdorDB, OdorMapDB, BrainPharmA pilot Web portal that successfully integrates multidisciplinary neurocience data.

Proper citation: SenseLab (RRID:SCR_007276) Copy   

•   Source: SciCrunch Registry

http://ftp://ftp.geneontology.org/pub/go/www/GO.tools_by_type.term_enrichment.shtml#gobean

GoBean is a Java application for gene ontology enrichment analysis. It utilizes the NetBeans platform framework. Features * Graphical comparison of multiple enrichment analysis results * Versatile filter facility for focused analysis of enrichment results * Effective exploitation of the graphical/hierarchical structure of GO * Evidence code based association filtering * Supports local data files such as the ontology obo file and gene association files * Supports late enrichment methods and multiple testing corrections * Built-in ID conversion for common species using Ensembl biomart service Platform: Windows compatible, Mac OS X compatible, Linux compatible

Proper citation: GoBean - a Java application for Gene Ontology enrichment analysis (RRID:SCR_005808) Copy   

•   Source: SciCrunch Registry

http://brainsia.github.io/BRAINSTools/

Medical image processing software suite for brain analysis.

Proper citation: BRAINSTools (RRID:SCR_006618) Copy   

•   Source: SciCrunch Registry

http://www.brainresource.com/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 10th,2023. Commercial provider of cognitive assessments, including their proprietary database, the Brain Resource International Database (BRID) that allows users to quantify individual differences in brain function, compare individual performance against peers, and provide a robust frame of reference for clinical assessment and treatment decisions. Database provides evidence for brain behavior connection so important to reliably enabling optimal solutions for mental health and wellbeing. It powers all Brain Resource products.

Proper citation: Brain Resource (RRID:SCR_006172) Copy   

•   Source: SciCrunch Registry

http://noble.gs.washington.edu/proj/sdp-svm/

A statistical framework for genomic data fusion is a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each dataset is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion. Recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in a way that minimizes a statistical loss function. These methods exploit semidefinite programming techniques to reduce the problem of finding optimizing kernel combinations to a convex optimization problem. Computational experiments performed using yeast genome-wide datasets, including amino acid sequences, hydropathy profiles, gene expression data and known protein-protein interactions, demonstrate the utility of this approach. A statistical learning algorithm trained from all of these data to recognize particular classes of proteins--membrane proteins and ribosomal proteins--performs significantly better than the same algorithm trained on any single type of data. Matlab code to center a kernel matrix and Matlab code for normalization are available.

Proper citation: A statistical framework for genomic data fusion (RRID:SCR_007219) Copy   

•   Source: SciCrunch Registry

http://www.pedigree-draw.com/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 12,2024. Software application for pedigree drawing (entry from Genetic Analysis Software)

Proper citation: Pedigree-Draw (RRID:SCR_008302) Copy   

•   Source: SciCrunch Registry

http://www.enea.it/en/

Italian national agency for new technologies, energy and sustainable economic development with the following main research issues: * Energy Efficiency * Renewable Energy Sources * Nuclear Energy * Climate and the Environment * Safety and Health * New Technologies * Electric System Research The Agency''s activities are devoted to: * basic, mission-oriented, and industrial research exploiting wide-ranging expertise as well as experimental facilities, specialized laboratories, advanced equipment; * new technologies and advanced applications; * dissemination and transfer of research results, thus promoting their exploitation for production purposes; * provide public and private bodies with high-tech services, studies, measurements, tests and assessments; * training and information activities aimed at broadening sector expertise and public knowledge and awareness. The Agency''s multidisciplinary competences and great expertise in managing complex research projects are put at the disposal of the Country system.

Proper citation: ENEA (RRID:SCR_003929) Copy   

•   Source: SciCrunch Registry

http://amide.sourceforge.net/index.html

Software tool for viewing, analyzing, and registering volumetric medical imaging data sets. It has been written on top of GTK+ and runs on any system that supports this toolkit (Linux, Windows, Mac OS X, etc.). The program incorporates automatic non-orthogonal data reslicing, allowing multiple data set to be fused without imposed constraints on the dimensions, anisotrophy, or voxel sizes of the data. Additional features include 3D ROI (ellipses, cylinders, boxes, and isocontours), multi-slice viewing, volume rendering, and data importing through the (X)MedCon library.

Proper citation: amide (RRID:SCR_005940) Copy   

•   Source: SciCrunch Registry

http://gephi.org/

Open-source software for network visualization and analysis helping data analysts to intuitively reveal patterns and trends, highlight outliers and tells stories with their data. It uses a 3D render engine to display large graphs in real-time and to speed up the exploration. Gephi combines built-in functionalities and flexible architecture to: explore, analyze, spatialize, filter, cluterize, manipulate and export all types of networks. Gephi runs on Windows, Linux and Mac OS X. Gephi is based on a visualize-and-manipulate paradigm which allow any user to discover networks and data properties. Moreover, it is designed to follow the chain of a case study, from data file to nice printable maps. It is open-source and free (GNU General Public License). Applications: * Exploratory Data Analysis: intuition-oriented analysis by networks manipulations in real time. * Link Analysis: revealing the underlying structures of associations between objects, in particular in scale-free networks. * Social Network Analysis: easy creation of social data connectors to map community organizations and small-world networks. * Biological Network analysis: representing patterns of biological data. * Poster creation: scientific work promotion with hi-quality printable maps. Gephi 0.7 architecture is modular and therefore allows developers to add and extend functionalities with ease. New features like Metrics, Layout, Filters, Data sources and more can be easily packaged in plugins and shared. The built-in Plugins Center automatically gets the list of plugins available from the Gephi Plugin portal and takes care of all software updates. Download, comment, and rate plugins provided by community members and third-party companies, or post your own contributions!

Proper citation: Gephi (RRID:SCR_004293) Copy   

•   Source: SciCrunch Registry

http://ebirt.emory.edu/

Venue for research resource discovery offering resource providers a platform to advertise their services and products, as well as investigators a means to locate services for their use. Search results may be refined by resource type, research area or institution.

Proper citation: eBIRT (RRID:SCR_004172) Copy   

•   Source: SciCrunch Registry

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

This is a command line tool which allows the user to study the behavior of water diffusion (using DTI data) along the length of the white matter fiber-tracts. Various tract-oriented scalar diffusion measures obtained from DTI brain images, are treated as a continuous function of white matter fibers'' arc-length. To analyze the trend along a given fiber tract, a command line tool performs kernel regression on this data. The idea is to try out different noise models and maximum likelihood estimates within kernel windows (along the tract), such that they best represent the data and are robust to noise and Partial Volume effect. The package contains several command line based modules and an GUI based tool called DTIAtlasFiberAnalyzer to access most functions. The features available in the tool currently, its use and input / output formats and other relevant details are provided in the first draft of the documentation. (http://www.na-mic.org/Wiki/index.php/Projects:dtistatisticsfibers).

Proper citation: DTI Fiber Tract Statistics (RRID:SCR_009460) Copy   

•   Source: SciCrunch Registry