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http://hypothesis.alzforum.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A community-driven knowledgebase of Alzheimer disease, in which researchers can annotate scientific claims, data, and information, putting these into the context of testable hypotheses and treatment discovery. This SWAN project adds a collection of hand-curated hypotheses to a research paper, which are then related through a set of discourse relationships. They can be browsed and relations between claims, as well as support networks for a specific claim, are made and visualized. AlzSWAN is where you explore scientific knowledge about Alzheimer disease and share your own ideas, comments and questions in a semantically structured system. AlzSWAN is enabled by Semantic Web technology, a new standard for knowledge organization and transfer on the Web. AlzSWAN organizes and manages knowledge using formal knowledge descriptions called ontologies. Using these formal knowledge descriptions, they can tie statements made in scientific publications or on the Web to scientific evidence, biological terminologies, and knowledgebases, and to claims and counterclaims made by other researchers.

Proper citation: AlzSWAN Knowledge Base (RRID:SCR_003017) Copy   

•   Source: SciCrunch Registry

http://www.genepaint.org

Digital atlas of gene expression patterns in developing and adult mouse. Several reference atlases are also available through this site. Expression patterns are determined by non-radioactive in situ hybridization on serial tissue sections. Sections are available from several developmental ages: E10.5, E14.5 (whole embryos), E15.5, P7 and P56 (brains only). To retrieve expression patterns, search by gene name, site of expression, GenBank accession number or sequence homology. For viewing expression patterns, GenePaint.org features virtual microscope tool that enables zooming into images down to cellular resolution.

Proper citation: GenePaint (RRID:SCR_003015) Copy   

•   Source: SciCrunch Registry

http://www.euroscarf.de/index.php?name=News

Archive of yeast strains and plasmids that were generated during various yeast functional analysis projects.

Proper citation: EUROpean Saccharomyces Cerevisiae ARchive for Functional Analysis (RRID:SCR_003093) Copy   

•   Source: SciCrunch Registry

http://igenomed.stanford.edu/~junhee/JETTA/rnaseq.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 6, 2017. Software to detect alternatively spliced exons between two conditions, for example, between two groups of treated and untreated patients in a typical clinical study.

Proper citation: JETTA (RRID:SCR_003091) Copy   

•   Source: SciCrunch Registry

http://www.uccs.edu/~faculty/lbecker/

Calculator for a variety of functions, including Cohen's d and the effect-size correlation, rYl, using means and standard deviations or independent groups t test values and df.

Proper citation: Effect Size Calculator (RRID:SCR_003094) Copy   

•   Source: SciCrunch Registry

http://www.bisti.nih.gov/

A consortium of representatives from each of the NIH institutes and centers. BISTI was established in May 2000 to serve as the focus of biomedical computing issues at the NIH. The mission of BISTI is to make optimal use of computer science and technology to address problems in biology and medicine by fostering new basic understandings, collaborations, and transdisciplinary initiatives between the computational and biomedical sciences. In support of this mission, the BISTI coordinates research grants, training opportunities, and scientific symposia associated with biomedical computing. Regular monthly meetings are conducted to discuss program status, future needs and directions, and topics of interest to the bioinformatics community.

Proper citation: Biomedical Information Science and Technology Initiative (RRID:SCR_003123) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/biocaster-ontology/

A multilingual application ontology aimed at the early detection of public health events in the media. It aims to describe the terms and relations necessary to detect and risk assess public health events in the grey literature at an early stage; and bridge the gap between the (multilingual) grey literature and existing standards in biomedicine. The BCO focuses on the usage of terms and relations within informal unstructured reports which are often made at a pre-diagnostic stage of a disease outbreak by non-medically trained reporters. This is done to provide monitoring and early warning about public health hazards from online media reports.

Proper citation: BioCaster Ontology (RRID:SCR_003122) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/web/packages/gap/

GAP is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, classic twin models, probability of familial disease aggregation, kinship calculation, some statistics in linkage analysis, and association analysis involving one or more genetic markers including haplotype analysis with or without environmental covariates.

Proper citation: Genetic Analysis Package (RRID:SCR_003006) Copy   

•   Source: SciCrunch Registry

http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi

A web interface to the Primer3 primer design program as an enhanced alternative for the CGI- scripts that come with Primer3.

Proper citation: Primer3Plus (RRID:SCR_003081) Copy   

•   Source: SciCrunch Registry

http://www.neuroml.org/

A XML-based description language that provides a common data format for defining and exchanging descriptions of neuronal cell and network models. It facilitates the exchange of complex neural models, allows for greater transparency and accessibility of models, enhances interoperability between simulators and other tools, and supports the development of new software and databases. Exchange of network models will aid the investigation of structure-function relationships in neuroscience including theoretical studies relating connectivity patterns to normal and neurodegenerative network states. NeuroML is a free and open community effort developed with input from many contributors. They will need your help as the standards and tools continue to evolve.

Proper citation: NeuroML (RRID:SCR_003083) Copy   

•   Source: SciCrunch Registry

https://www.ddbj.nig.ac.jp/jga/index-e.html

A service for permanent archiving and sharing of all types of personally identifiable genetic and phenotypic data resulting from biomedical research projects. The JGA contains exclusive data collected from individuals whose consent agreements authorize data release only for specific research use or to bona fide researchers. Strict protocols govern how information is managed, stored and distributed by the JGA. Once processed, all data are encrypted. The JGA accepts only de-identified data approved by JST-NBDC. The JGA implements access-granting policy whereby the decisions of who will be granted access to the data resides with the JST-NBDC. After data submission the JGA team will process the data into databases and archive the original data files. The accepted data types include manufacturer-specific raw data formats from the array-based and new sequencing platforms. The processed data such as the genotype and structural variants or any summary level statistical analyses from the original study authors are stored in databases. The JGA also accepts and distributes any phenotype data associated with the samples. For other human biological data, please contact the NBDC human data ethical committee.

Proper citation: Japanese Genotype-phenotype Archive (JGA) (RRID:SCR_003118) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/release/bioc/html/tweeDEseq.html

Software for differential expression analysis of RNA-seq using the Poisson-Tweedie family of distributions.

Proper citation: tweeDEseq (RRID:SCR_003038) Copy   

•   Source: SciCrunch Registry

http://www.mged.org/Workgroups/MAGE/mage-ml.html

A language / data exchange format designed to describe and communicate information about microarray based experiments that is based on XML and can describe microarray designs, microarray manufacturing information, microarray experiment setup and execution information, gene expression data and data analysis results. MAGE-ML has been automatically derived from Microarray Gene Expression Object Model (MAGE-OM), which is developed and described using the Unified Modelling Language (UML) -- a standard language for describing object models. Descriptions using UML have an advantage over direct XML document type definitions (DTDs), in many respects. First they use graphical representation depicting the relationships between different entities in a way which is much easier to follow than DTDs. Second, the UML diagrams are primarily meant for humans, while DTDs are meant for computers. Therefore MAGE-OM should be considered as the primary model, and MAGE-ML will be explained by providing simplified fragments of MAGE-OM, rather then XML DTD or XML Schema. (from the description by Ugis Sarkans) The field of gene expression experiments has several distinct technologies that a standard must include. These include single vs. dual channel experiments, cDNA vs. oligonucleotides. Because of these different technologies and different types of gene expression experiments, it is not expected that all aspects of the standard will be used by all organizations. Given the massive amount of data associated with a single set of experiments, it is felt that Extensible Markup Language (XML) is the best way to describe the data. The use of a Document Type Definition (DTD) allows a well-defined tag set, a vocabulary, to describe the domain of gene expression experiments. It also has the virtue of compressing very well so that files in an XML format compress to ten percent of their original size. XML is now widely accepted as a data exchange format across multiple platforms.

Proper citation: MicroArray and Gene Expression Markup Language (RRID:SCR_003023) Copy   

•   Source: SciCrunch Registry

http://nmf.jax.org/protocols.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. The Neuroscience Mutagenesis Facility of the Jackson Laboratory (NMF) was established to produce new neurological mouse models that could serve as experimental models for the exploration of basic neurobiological mechanisms and diseases. The protocols are available. The impetus for the program resulted from the recognition that * the value of genomic data would remain limited unless more information about the functionality of its individual components became available, and * the task of linking genes to specific behavior would best be accomplished by employing a combination of different approaches. In an effort to complement already existing programs, the Neuroscience Mutagenesis Facility decided to use: a random, genome-wide approach to mutagenesis, i.e. N-ethyl-N-nitrosourea (ENU) as the mutagen; a three-generation back-cross breeding scheme to focus on the detection of recessive mutations; behavioral screens selective for the detection of phenotypes deemed useful for the program goals. Protocols: * Genetics ** Production of Mice for a Genome-Wide ENU Mutagenesis Screen ** Production of Mice using Chemical Mutagenesis of Mouse ES Cells * Protocols ** Step by step procedures-- Mouse mutagenesis with ENU ** Step by step procedures-- ES Cell mutagenesis with EMS * Phenotyping: Overview * Protocols:(currently only screens marked * are in use) ** Acoustic startle response (ASR) ** Auditory brainstem response (ABR) ** CLAMSTM(former CCMS) ** Creatine kinase ** Developmental Screen * ** Eye and Vision * ** Gait Analysis ** Gustation ** Observation * ** Seizure threshold * ** Additional Background Information

Proper citation: JAX Neuroscience Mutagenesis Facility Protocols (RRID:SCR_003021) Copy   

•   Source: SciCrunch Registry

https://github.com/PacificBiosciences/R-pbh5

Software library for accessing data in HDF5 files produced by Pacific Biosciences sequencing machines. The R package supports accessing data from: cmp.h5, bas.h5, pls.h5, and trc.h5.

Proper citation: R-pbh5 (RRID:SCR_003026) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/web/packages/isopat/

Software function that calculates the isotopic pattern (fine structures) for a given chemical formula.

Proper citation: Isopat (RRID:SCR_003025) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/release/bioc/html/BRAIN.html

Software package for calculating aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S).

Proper citation: BRAIN (RRID:SCR_003018) Copy   

•   Source: SciCrunch Registry

http://www.dtp.nci.nih.gov

Portal for preclinical information and research materials, including web-accessible data and tools, NCI-60 Tumor Cell Line Screen, compounds in vials and plates, tumor cells, animals, and bulk drugs for investigational new drug (IND)-directed studies. DTP has been involved in the discovery or development of more than 70 percent of the anticancer therapeutics on the market today, and will continue helping the academic and private sectors to overcome various therapeutic development barriers, particularly through supporting high-risk projects and therapeutic development for rare cancers. Initially DTP made its drug discovery and development services and the results from the human tumor cell line assay publicly accessible to researchers worldwide. At first, the site offered in vitro human cell line data for a few thousand compounds and in vitro anti-HIV screening data for roughly 42,000 compounds. Today, visitors can find: * Downloadable in vitro human tumor cell line data for some 43,500 compounds and 15,000 natural product extracts * Results for 60,000 compounds evaluated in the yeast assay * In vivo animal model results for 30,000 compounds * 2-D and 3-D chemical structures for more than 200,000 compounds * Molecular target data, including characterizations for at least 1,200 targets, plus data from multiple cDNA microarray projects In addition to browsing DTP's databases and downloading data, researchers can request individual samples or sets of compounds on 96-well plates for research, or they can submit their own compounds for consideration for screening via DTP's online submission form. Once a compound is submitted for screening, researchers can follow its progress and retrieve data using a secure web interface. The NCI has collected information on almost half a million chemical structures in the past 50 years. DTP has made this information accessible and useful for investigators through its 3-D database, a collection of three-dimensional structures for more than 200,000 drugs. Investigators use the 3-D database to screen compounds for anticancer therapeutic activity. Also available on DTP's website are 127,000 connection tables for anticancer agents. A connection table is a convenient way of depicting molecular structures without relying on drawn chemical structures. As unique lists of atoms and their connections, the connection tables can be indexed and stored in computer databases where they can be used for patent searches, toxicology studies, and precursor searching, for example., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Developmental Therapeutics Program (RRID:SCR_003057) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/sasqpcr/

All-in-one computer program for robust and rapid analysis of quantitative reverse transcription real-time polymerase chain reaction (RT-qPCR) data in SAS. It incorporates all functions important for RT-qPCR data analysis including assessment of PCR efficiencies, validation of internal reference genes and normalizers, normalization of confounding variations across samples and statistical comparisons of target gene expression in parallel samples. The program is highly automatic in data analyses and result output. The input data have no limitations for the number of genes or cDNA samples. Users can simply change the macro variables to test various analytical strategies, optimize results and customize the analytical processes. The program is also extendable allowing advanced SAS users to develop particular statistical tests appropriate for their experimental designs. Thus users are the actual decision-makers controlling RT-qPCR data analyses. The program has to be used in SAS software; however, extensive SAS programming knowledge is not required.

Proper citation: SASqPCR (RRID:SCR_003056) Copy   

•   Source: SciCrunch Registry

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

R package providing functions to assess and to compare the performance of risk prediction (survival) models.

Proper citation: SurvComp (RRID:SCR_003054) Copy   

•   Source: SciCrunch Registry