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http://www.immuneprofiling.org/

Consortium established to capitalize on recent advances in immune profiling methods in order to create a novel public resource that characterizes diverse states of the human immune system following infection; prior to and following vaccination against an infectious disease; or prior to and following treatment with an immune adjuvant that targets a known innate immune receptor(s). Through this program, well-characterized human cohorts are studied using a variety of modern analytic tools, including multiplex transcriptional, cytokine, and proteomic assays; multiparameter phenotyping of leukocyte subsets; assessment of leukocyte functional status; and multiple computational methods. Centralized research resources and a comprehensive, centralized database will be constructed for use by the greater scientific community. The information gained from the program will provide a comprehensive understanding of the human immune system and its regulation, and will reveal novel associations between components of the immune system and other biological systems, identify novel immune mediators and pathways, establish predictors of vaccine safety in different populations, and enable the rapid evaluation of different vaccine formulations and administration regimens in human populations.

Proper citation: Human Immunology Project Consortium (RRID:SCR_001491) Copy   

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https://bioconductor.org/packages//2.13/bioc/html/virtualArray.html

Software package that permits the user to combine raw data of different microarray platforms into one virtual array. It consists of several functions that act subsequently in a semi-automatic way. Doing as much of the data combination and letting the user concentrate on analyzing the resulting virtual array.

Proper citation: virtualArray (RRID:SCR_001361) Copy   

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http://www.jaduniv.edu.in/

Public state university in West Bengal, India. There is a focus on interdisciplinary learning and research; the university features a variety of centers such as school of education technology, cognitive sciences, and natural product studies.

Proper citation: Jadavpur University; West Bengal; India (RRID:SCR_001366) Copy   

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https://bioinformatics.ca/people/experts/

Investigators interested in Bioinformatics in Canada including practitioners as well as developers of algorithms, databases or resources. Principal investigators and group leaders from academia, government labs and industry are welcome on this page. All present have an interest in the development of bioinformatics resources in Canada.

Proper citation: Bioinformatics Experts (RRID:SCR_001485) Copy   

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

Software for stepwise normalization functions for cDNA microarray data.

Proper citation: stepNorm (RRID:SCR_001359) Copy   

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

Software package for robust estimation of cDNA microarray intensities with replicates. It uses a Bayesian hierarchical model for the robust estimation. Outliers are modeled explicitly using a t-distribution, and the model also addresses classical issues such as design effects, normalization, transformation, and nonconstant variance.

Proper citation: RAMA (RRID:SCR_001358) Copy   

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

Software that infers biological processes which are active in individual gene sets from corresponding microarray measurements. It achieves this inference by combining a MCMC matrix decomposition algorithm (GAPS) with a novel statistic inferring activity on gene sets.

Proper citation: CoGAPS (RRID:SCR_001479) Copy   

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https://bioportal.bioontology.org/ontologies/NCBI_NMOsp_1_2_2

NeuroMorpho.Org light species ontology is a customized taxonomy for species available in NeuroMorpho.Org. This file is created from the existing resources like NCBI Taxonomy, NIF organism, Rat strain ontology, and Jax mice catalogue. The OBO file can be explored and downloaded from the NCBO bioportal service. The current NCBI_NMOsp_v1.2.2.obo file consists of 887 concepts of which 24 unique classes are mapped onto species & strains data in NeuroMorpho.Org database.

Proper citation: NeuroMorpho.Org light species ontology (RRID:SCR_001394) Copy   

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http://neuralensemble.org/

Community-based initiative to promote and co-ordinate open-source software development in neuroscience hosting a number of software projects for computational and systems neuroscience, including PyNN, NeuroTools, Brian, Neo, OpenElectrophy, libNeuroML and Sumatra. Some of the projects use their Trac installation, others are on GitHub. By grouping these projects together under the NeuralEnsemble umbrella, the aim is to maximize interoperability and build components that can easily be combined into powerful systems for brain simulations and advanced data analysis. An annual CodeJam workshop is organized, bringing together scientists, graduate students, and scientific programmers to share ideas, present their work, and write code together. These workshops have been hugely effective in catalyzing open-source neuroscience software development. There is a NeuralEnsemble Google group for discussion of collaborative neuroscience software development (mainly in Python, but users of other languages are welcome!) and to provide software support.

Proper citation: NeuralEnsemble (RRID:SCR_001382) Copy   

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http://clarityresourcecenter.org/

Protocols and other training materials related to the CLARITY protocol, a technique for the transformation of intact tissue into a nanoporous hydrogel-hybridized form (crosslinked to a three-dimensional network of hydrophilic polymers) that is fully assembled but optically transparent and macromolecule-permeable.

Proper citation: Clarity resources (RRID:SCR_001387) Copy   

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http://alfred.med.yale.edu

A public curated compilation of allele frequency data on anthropologically defined human population samples linked to the molecular genetics-human genome databases. Only data on well defined population samples that are large enough to yield reasonably accurate frequencies and for polymorphisms sufficiently defined to be replicable can be included in ALFRED. Researchers wishing to have their data entered into ALFRED should contact them. Initially, ALFRED contained primarily data generated in the laboratories of K.K. and J.R. Kidd in the Department of Genetics at Yale, including extensive unpublished data. Data from the published literature are being entered into ALFRED in a systematic way, with a focus on polymorphisms studied in many different populations. ALFRED is distinct from such databases as dbSNP, which catalogs sequence variation. ALFRED's focus is on allele frequencies in diverse anthropologically defined populations. It is not a compendium of human DNA polymorphisms but of frequencies of selected polymorphisms with an emphasis on those that have been studied in multiple populations. All of the data in ALFRED are considered to be in the public domain and available for use in research and teaching. ALFRED provides easy searching options including versatile "Keyword search" and also has numerous summary tables providing quick overviews of contents by chromosome, population, average heterozygosity, Fst and others, all available under various tabs from the ALFRED homepage.

Proper citation: ALFRED (RRID:SCR_001730) Copy   

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https://lcn.salk.edu/WSMain.html

The Salk Institute's Laboratory for Cognitive Neuroscience (LCN) is dedicated to the study of the neural and genetic underpinnings of language and cognition. The LCN organizes its resources into two research foci: Linking Gene, Brain, and Cognition, and Language, Modality and the Brain. Linking Gene, Brain, and Cognition: Behavioral Neurogenetics: - This research is designed to increase the understanding of genetically based disorders, to investigate the consequences of genetic alterations on the development of the brain, and to explore the resulting alteration of cognitive capabilities. Language, Modality, and the Brain: - The focus of this research is to obtain a greater understanding of how language and cognition are represented in the brain. Sponsors: This resource is supported by LCN.

Proper citation: Salk Institute for Medical Research: Laboratory for Cognitive Neuroscience (RRID:SCR_001851) Copy   

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https://www.genome.wisc.edu/tools/asap.htm

Database and web interface developed to store, update and distribute genome sequence data and gene expression data. ASAP was designed to facilitate ongoing community annotation of genomes and to grow with genome projects as they move from the preliminary data stage through post-sequencing functional analysis. The ASAP database includes multiple genome sequences at various stages of analysis, and gene expression data from preliminary experiments. Use of some of this preliminary data is conditional, and it is the users responsibility to read the data release policy and to verify that any use of specific data obtained through ASAP is consistent with this policy. There are four main routes to viewing the information in ASAP: # a summary page, # a form to query the genome annotations, # a form to query strain collections, and # a form to query the experimental data. Navigational buttons appear on every page allowing users to jump to any of these four points., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ASAP (RRID:SCR_001849) Copy   

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http://cran.r-project.org/web/packages/MCMC.qpcr/

Software package that implements generalized linear mixed model analysis of qRT-PCR data based on lognormal-Poisson model fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). Also implemented are the lognormal model for higher-abundance data and a classic model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results.

Proper citation: MCMC.qpcr (RRID:SCR_001721) Copy   

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http://matrixdb.univ-lyon1.fr/

Freely available database focused on interactions established by extracellular proteins and polysaccharides, taking into account the multimeric nature of the extracellular proteins (e.g. collagens, laminins and thrombospondins are multimers). MatrixDB is an active member of the International Molecular Exchange (IMEx) consortium and has adopted the PSI-MI standards for annotating and exchanging interaction data. It includes interaction data extracted from the literature by manual curation, and offers access to relevant data involving extracellular proteins provided by the IMEx partner databases through the PSICQUIC webservice, as well as data from the Human Protein Reference Database. The database reports mammalian protein-protein and protein-carbohydrate interactions involving extracellular molecules. Interactions with lipids and cations are also reported. MatrixDB is focused on mammalian interactions, but aims to integrate interaction datasets of model organisms when available. MatrixDB provides direct links to databases recapitulating mutations in genes encoding extracellular proteins, to UniGene and to the Human Protein Atlas that shows expression and localization of proteins in a large variety of normal human tissues and cells. MatrixDB allows researchers to perform customized queries and to build tissue- and disease-specific interaction networks that can be visualized and analyzed with Cytoscape or Medusa. Statistics (2013): 2283 extracellular matrix interactions including 2095 protein-protein and 169 protein-glycosaminoglycan interactions.

Proper citation: MatrixDB (RRID:SCR_001727) Copy   

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http://medmole.cineca.it/

MedMOLE improves the comprehension of microarray experimental results by grouping co-regulated genes on the basis of the informational content of MEDLINE documents. The tool relies on two components: a gene name extractor and a mining algorithm. The name extractor is based on existing dictionaries of gene names and aliases. The mining algorithm analyses the co-occurrences of words in the selected documents in order to automatically interpret the context, identify where the gene names appear, and map documents/genes into functional classes. DNA microarray technology is a high throughput method for gaining information on gene function. This large amount of data can be analyzed to identify groups of genes that share common expression characteristics, but the obtained results provide little information regarding the presence of functional biological correlations of genes within clusters. The published literature, on the other hand, provides a potential source of information to assist in interpretation of clustering results. We have developed a tool (MedMOLE) that improves the comprehension of microarray experimental results by grouping co-regulated genes on the basis of the informational content of MEDLINE documents. The tool relies on two components: a gene name extractor and a mining algorithm. The name extractor is based on existing dictionaries of gene names and aliases. The mining algorithm analyses the co-occurrences of words in the selected documents in order to automatically interpret the context, identify where the gene names appear, and map documents/genes into functional classes. Microarray transcriptional profiling is a powerful tool used in the study of transcriptional control mechanisms. An important point in the analysis of microarray data is the identification of hidden correlations between the differentially expressed genes generated upon some kind of cell stimulus. Functional annotation is an important topic for microarray data mining, however this is quite limited for complex organisms (e.g. H. sapiens, M. musculus) where a limited number of genes are well characterized and annotated. However, functional data are rapidly accumulating in the scientific literature and most of them are collected by MEDLINE, a database that contains over 11,000,000 biomedical journal citations. A microarray analysis usually generates few hundred of differentially expressed genes and, after statistical validation of the data and transcription profiles clustering, biologists try to identify genes functionally correlated by scientific literature analysis. Even if some tools have been recently developed to simplify information extraction on the MEDLINE database, reading every article requires too much time and labor. Therefore, it is necessary to have some kind of intelligent information extracting system that recognizes gene names inside the texts. The analysis of text documents (e.g. MEDLINE abstracts) can be approached by two different points of view: text mining and information extraction (I.E.). The former aims at the automatic identification of groups of documents that share the same patterns of words, and thus refer to the same topic or theme. The latter aims at providing a structured representation of the textual information and requires a pre-definition of entities and relationships to be looked for inside texts. Thus while the text mining algorithms are general purpose, the information extraction algorithms are specific to the application. Furthermore, the text mining approach is explorative and enables the discovery of new concepts and relations while information extraction only extracts those elements that have already been defined. These two approaches can be integrated: information extraction tools generate databases that can be analyzed using data mining techniques, and, on the other side, text mining tools might take advantage of specific domain information extracted using I.E. techniques. MedMOLE takes advantage of text mining techniques, and simplifies the extraction of functional knowledge by literature abstracts directly/indirectly related to differentially expressed genes identified by microarray technology. Sponsors: This work was partially supported by PRIN 2001 and FIRB 2002 grants.

Proper citation: Mining On-Line Expert on MedLine (RRID:SCR_001848) Copy   

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http://www.columbia.edu/cu/biology/faculty/yuste/

Laboratory that aims to understand the function of the cortical microcircuit by reverse-engineering of the cortical microcircuit using the mouse neocortex in vitro and in vivo as their experimental preparations. The techniques applied are electrophysiology, anatomy, and a variety of optical methods, including infrared-DIC, voltage- and ion-sensitive dye imaging with confocal, two-photon and second harmonic microscopy. They also use laser uncaging, biolistics, electroporation, electron microscopy and numerical simulations, and make extensive use of genetically modified mouse strains. They focus is on two major questions: (1) What is the function of dendritic spines? (2) What are the multicellular patterns of activity under spontaneous or evoked activation of the circuit? Resources include: * Cell Reconstructions: Cell Database, PDF Images, .DAT Files * Circuit Diagrams: Full Circuit Diagram, Inhibitory Circuit Diagram, Excitatory Circuit Diagram, Simplified Circuit Diagram, Layer to Layer Simplified Circuit, Circuit diagram references

Proper citation: Rafael Yustes Laboratory (RRID:SCR_001845) Copy   

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http://tcag.ca/index.html

Service and training support for academic, government, and private sector scientists worldwide in genomics, including laboratory experimentation, statistical analysis, and comprehensive bioinformatics support, including large-scale genome comparisons, algorithm and tools development, and database curation, annotation and hosting. The Centre for Applied Genomics hosts a variety of databases related to ongoing supported projects: *Autism Chromosome Rearrangement Database *Cystic Fibrosis Mutation Database *The Lafora Progressive Myoclonus Epilepsy Mutation and Polymorphism Database *Database of Genomic Variants *The Chromosome 7 Annotation Project *Human Genome Segmental Duplication Database *Non-Human Segmental Duplication Database Healthy control DNA samples from the Ontario Population Genomics Platform are available. The Biobanking and Databasing Facility provides DNA extraction from lymphoblasts, fibroblasts and other cell types, archiving of white cell pellets, preparation and immortalization of cell lines, and comprehensive databasing and tracking of samples and/or cell lines within the facility.

Proper citation: TCAG (RRID:SCR_001840) Copy   

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http://mobile.ebiocenter.com/ebionews/

eBioNews specializes in online information services and resource exchanges in the fields of life sciences and biotechnology. By applying its knowledge database and content management system (CMS), eBioNews offers readers and customers the organized and comprehensive information. eBioNews also provides a membership-based service to assist our customers in information and data search, processing, storage, and sharing. Generally, eBioNews covers the following areas: - life science frontiers - news and discussions - features and specials - resources and sourcing - career development - academic and industry - training and education Additionally, eBioNews information is organized into the following two clusters: - News Center: 1. Headlights 2. Research Frontiers 3. General Research 4. Clinical Development 5. Enterprise & Industry 6. Products & Services 7. Investment & Financials 8. Features 9. Newsletter The News Center consists of the elements and mechanisms that enable collecting, organizing, displaying, and delivering life science related information, data, and knowledge. - Resource Center: 1. eBioResources 2. Cooperation 3. Events 4. Human Resources 5. Intellectual Property 6. Finance & Legal 7. Operations 8. Organization 9. Publication The Resource Center is a system that hosts and facilitates the resource-related information between and among multiple parties, especially for promoting cooperation, collaboration, consortium, partnering, joint venture, licensing, out-sourcing, and trading. Sponsors: This resource is supported by eBioCenter Corporation.

Proper citation: eBioNews - A Subsidiary of eBioCenter (RRID:SCR_001717) Copy   

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https://www.kent.edu/biomedical

Graduate school in biomedical sciences at Kent State University with programs in neurosciences, physiology, biological anthropology, cellular molecular, and pharmacology.

Proper citation: Kent State University School of Biomedical Sciences; Ohio; USA (RRID:SCR_001718) Copy   

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