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http://www.thomaskoenig.ch/index.php/software/ragu

Software tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics.

Proper citation: Ragu (RRID:SCR_016851) Copy   

•   Source: SciCrunch Registry

https://networkx.github.io/

Software Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

Proper citation: NetworkX (RRID:SCR_016864) Copy   

•   Source: SciCrunch Registry

http://pbil.univ-lyon1.fr/acuts/ACUTS.html

THIS RESOURCE IS NO LONGER IN SERVICE, Documented on August 12, 2014. Database that identifies new regulatory elements in untranslated regions of protein-coding genes (5 prime flanks, 5 prime UTRs, introns, 3 prime UTRs and 3 prime flanks). The analyses is focused on genes from metazoan species (essentially vertebrates, insects and nematodes). Information on highly conserved regions (sequences, alignments, annotations, bibliographic references) are compiled. Currently 176 out of 326 detected highly conserved regions (HCRs) have been analyzed and incorporated in the database. You can also access the list of annotated conserved elements and the list of conserved elements that remain to be processed. Their approach is based on comparative sequence analysis, for the identification of phylogenetic footprints.

Proper citation: Ancient conserved untranslated sequences (RRID:SCR_008130) Copy   

•   Source: SciCrunch Registry

http://bioinfo-out.curie.fr/ittaca/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 6/12/25. ITTACA is a database created for Integrated Tumor Transcriptome Array and Clinical data Analysis. ITTACA centralizes public datasets containing both gene expression and clinical data and currently focuses on the types of cancer that are of particular interest to the Institut Curie: breast carcinoma, bladder carcinoma, and uveal melanoma. ITTACA is developed by the Institut Curie Bioinformatics group and the Molecular Oncology group of UMR144 CNRS/Institut Curie. A web interface allows users to carry out different class comparison analyses, including comparison of expression distribution profiles, tests for differential expression, patient survival analyses, and users can define their own patient groups according to clinical data or gene expression levels. The different functionalities implemented in ITTACA are: - To test if one or more gene, of your choice, is differentially expressed between two groups of samples exhibiting distinct phenotypes (Student and Wilcoxon tests). - The detection of genes differentially expressed (Significance Analysis of Microarrays) between two groups of samples. - The creation of histograms which represent the expression level according to a clinical parameter for each sample. - The computation of Kaplan Meier survival curves for each group. ITTACA has been developed to be a useful tool for comparing personal results to the existing results in the field of transcriptome studies with microarrays.

Proper citation: Integrated Tumor Transcriptome Array and Clinical data Analysis (RRID:SCR_008182) Copy   

•   Source: SciCrunch Registry

http://www.bh4.org/BH4DatabasesBiodef.asp

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 26, 2016. The BIODEF database have tabulated the most common clinical and laboratory data related to hyperphenylalaninaemia and tetrahydrobiopterin deficiencies. Additionally, there are data regarding treatment, outcome, and DNA analysis. Approximately 2% of newborns with hyperphenylalaninaemia are deficient in tetrahydrobiopterin. Selective screening must be performed in all instances where hyperphenylalaninaemia is detected by neonatal screening. In the last 20 years, 308 patients with tetrahydrobiopterin deficiencies have been recognized as a result of screening carried out, worldwide, in Departments of Paediatrics. Of these 308 patients, 181 suffered from 6-pyruvoyltetrahydropterin synthase deficiency, 92 from dihydropteridine reductase deficiency, 13 from pterin-4a-carbinolamine dehydratase deficiency, 12 from GTP cyclohydrolase I deficiency, and 10 are still unclassified. The BIODEF database have tabulated the most common clinical and laboratory data related to hyperphenylalaninaemia and tetrahydrobiopterin deficiencies. Additionally, there are data regarding treatment, outcome, and DNA analysis. Preliminary evaluation reveals that the degree of hyperphenylalaninaemia can vary from normal to 2500 mumol/L. Analyses of pterins in urine and measurement of dihydropteridine reductase activity from Guthrie cards are absolutely essential tests for accurate diagnosis. There is a regional (demographic) variation in the frequency of tetrahydrobiopterin deficiencies indicating the highest incidence in Saudi Arabia, probably a consequence of the high consanguinity rate.

Proper citation: International Database of Tetrahydrobiopterin Deficiencies (RRID:SCR_008171) Copy   

•   Source: SciCrunch Registry

https://www.danforthcenter.org/our-work/core-facilities/bioanalytical-chemistry-facility/

Facility provides tools for high quality plant and biological sample analysis in proteomics, mass spectrometry, metabolomics, ionomics, and related analytical fields. It works with academic and commercial institutions and has established collaborations with many principle investigators.

Proper citation: Donald Danforth Plant Science Center Bioanalytical Chemistry Facility (RRID:SCR_001047) Copy   

•   Source: SciCrunch Registry

http://www.albany.edu/genomics/proteomics/

A core facility that provides services in the identification of individual proteins, characterization of entire protein complexes or biosimilars, and global analysis to discover changes in the proteomes of different samples. The core also specializes in the development of custom assays for quantitation of specific molecules in complex clinical samples.

Proper citation: UAlbany Proteomics Core Facility (RRID:SCR_001048) Copy   

•   Source: SciCrunch Registry

http://www.vectorfriends.com/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 18,2025. Sequence analysis software for molecular biologists.

Proper citation: VectorFriends (RRID:SCR_001230) Copy   

•   Source: SciCrunch Registry

http://www.vanderbilt.edu/vinse/facilities/instruments/xps+phi+5000-versaprobe

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 13,2025.Lab with access to the PHI Versaprobe, a surface-sensitive analytical tool for identifying elemental composition and local bonding environment.

Proper citation: Vanderbilt X-Ray Photoelectron Spectroscopy Lab (RRID:SCR_000897) Copy   

•   Source: SciCrunch Registry

http://panomics.pnnl.gov/

Biomedical technology research center that develops and integrates new proteomic technologies for collaborative and service studies, disseminating the new technologies and training scientists in their use.

Proper citation: Proteomics Research Center for Integrative Biology (RRID:SCR_001098) Copy   

•   Source: SciCrunch Registry

http://www.cgl.ucsf.edu/

Biomedical technology resource center that develops software and web-based resources for the visualization and analysis of molecular structure, and related data, at scales ranging from the atomic to the supramolecular. They create tools for handling and integrating diverse types of biomolecular data, including atomic-resolution coordinates, density maps, sequences, annotations, and networks. Their primary efforts are in the visualization and analysis of structures of molecules and molecular assemblies, enzyme sequence-structure-function relationships, and network representations of protein similarity, binding interactions, and biological pathways. They provide technologies to enable identifying the molecular bases of disease and phenotypic variation, annotating proteins of unknown function, identifying targets for drug development, designing drugs, and engineering proteins with new functions. RBVI distributes software tools, including the popular UCSF Chimera visualization and analysis package, develops and hosts the Structure-Function Linkage Database, and provides access to state-of-the-art computational resources in support of research projects in these areas.

Proper citation: Resource for Biocomputing Visualization and Informatics (RRID:SCR_001374) Copy   

•   Source: SciCrunch Registry

http://www.nmr.mgh.harvard.edu/CFNT/index

Biomedical technology research center that develops and applies innovative neuroimaging technologies and techniques to enable closer examination of the human brain, and thereby contribute to better understanding of the brain in health and disease. They develop new techniques and advance existing technologies for acquisition and analysis of functionally specific images of the working brain, with unprecedented physiological precision and spatiotemporal resolution. The research and development aims to improve and extend existing methods for non-invasive magnetic resonance image analysis and acquisition, electromagnetic source imaging, optical neuroimaging, and most recently, combined MR-PET neuroimaging. The Resource provides an essential interactive environment, within which an interdisciplinary team of highly skilled scientists, engineers, and clinicians with diverse expertise in multiple modalities and disciplines. The resource supports service use of the Center's facilities by neuroscientists throughout the country, provide extensive training opportunities for students, fellows, and staff scientists, and seek to advance the field of brain mapping through active dissemination of new knowledge and technology.

Proper citation: Center for Functional Neuroimaging Technologies (RRID:SCR_001423) Copy   

•   Source: SciCrunch Registry

http://www.eecs.qmul.ac.uk/mmv/datasets/deap/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on December 12,2025. Multimodal dataset for emotion analysis using EEG, Physiological and Video Signals of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection was used, utilizing retrieval by affective tags from the last.fm website, video highlight detection and an online assessment tool. The dataset is made publicly available and other researchers are encouraged to use it for testing their own affective state estimation methods.

Proper citation: DEAPdataset (RRID:SCR_001586) Copy   

•   Source: SciCrunch Registry

https://github.com/atlab/TwoPhoton

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6,2023. Code used for analysis of TwoPhoton data.

Proper citation: TwoPhoton (RRID:SCR_003596) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/mpr/lung

Data set of a molecular taxonomy of lung carcinoma, the leading cause of cancer death in the United States and worldwide. Using oligonucleotide microarrays, researchers analyzed mRNA expression levels corresponding to 12,600 transcript sequences in 186 lung tumor samples, including 139 adenocarcinomas resected from the lung. Hierarchical and probabilistic clustering of expression data defined distinct sub-classes of lung adenocarcinoma. Among these were tumors with high relative expression of neuroendocrine genes and of type II pneumocyte genes, respectively. Retrospective analysis revealed a less favorable outcome for the adenocarcinomas with neuroendocrine gene expression. The diagnostic potential of expression profiling is emphasized by its ability to discriminate primary lung adenocarcinomas from metastases of extra-pulmonary origin. These results suggest that integration of expression profile data with clinical parameters could aid in diagnosis of lung cancer patients.

Proper citation: Classification of Human Lung Carcinomas by mRNA Expression Profiling Reveals Distinct Adenocarcinoma Sub-classes (RRID:SCR_003010) Copy   

•   Source: SciCrunch Registry

http://search.cpan.org/dist/ONTO-PERL/

ONTO-PERL is a collection of Perl modules to handle OBO-formatted ontologies (like the Gene Ontology). This code distribution gathers object-oriented modules (for dealing with ontology elements such as Term, Relationship and so forth), scripts (for typical tasks such as format conversions: obo2owl, owl2obo; besides, there are also many examples that can be easily adapted for specific applications), and a set of test files to ensure the suite''''s implementation quality. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: ONTO-PERL (RRID:SCR_005731) Copy   

•   Source: SciCrunch Registry

http://biodev.ece.ucsb.edu/projects/bisquik/wiki

A scalable web-based system for biological image analysis, management and exploration. The Bisque system incorporates many features useful to imaging researchers from image capture to extensible image analysis and querying. At the core, bisque maintains a flexible database of images and experimental metadata. Image analyses can be incorporated into the system and deployed on clusters and desktops. Search and comparison of datasets by image data and content is supported. Novel semantic analyses are integrated into the system allowing high level semantic queries and comparison of image content. New features and testing of Bisque version: 0.5.1, among many others are: # Parallel execution of datasets # Rich interfaces for autogenerated module UI # Abstracted storage system for local, irods, etc.. They are using Mercurial for their source control system. This should be installed before proceeding. Browse source on-line, http://biodev.ece.ucsb.edu/projects/bisquik/browser Bisque Installation, http://biodev.ece.ucsb.edu/projects/bisquik/wiki/InstallationInstructions05 Bisque DOWNLOAD, http://biodev.ece.ucsb.edu/projects/bisquik/wiki/download, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Bisque (RRID:SCR_005564) Copy   

•   Source: SciCrunch Registry

http://lab.rockefeller.edu/chait/

Biomedical technology research center that develops cutting-edge mass spectrometric tools for analyzing peptides and proteins. It makes its software tools developed for data analysis freely available.

Proper citation: National Resource for the Mass Spectrometric Analysis of Biological Macromolecules (RRID:SCR_009007) Copy   

•   Source: SciCrunch Registry

http://howard.eagle-i.net/i/00000134-a517-1426-bf4c-ca4080000000

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 27,2023. Core for bioinformatics consultation and software access. Laboratory of Molecular Computations and Bioinformatics (LMCB) is a resource facility dedicated to the support of computational biomedical research at Howard University. Provides molecular modeling, molecular dynamics, bioinformatics, and computational quantum chemistry capabilities and support to a variety of research projects at Howard University.

Proper citation: Howard University Center for Computational Biology and Bioinformatics Core Facility (RRID:SCR_009864) Copy   

•   Source: SciCrunch Registry

http://harvard.eagle-i.net/i/0000012e-5e87-861a-55da-381e80000000

Core for data driven projects related to basic, clinical and translational research, with a particular emphasis on diabetes. Aims to ensure that researchers take advantage of the most modern and robust methods available in the field of Bioinformatics and Biostatistics.

Proper citation: Harvard Bioinformatics Core at Joslin Diabetes Center (RRID:SCR_009827) Copy   

•   Source: SciCrunch Registry