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http://cgems.cancer.gov

The project began as a pilot study to identify inherited genetic susceptibility to prostate and breast cancer. CGEMS has developed into a robust research program involving genome-wide association studies (GWASs) for a number of cancers to identify common genetic variants that affect a person''s risk of developing cancer. In collaboration with extramural scientists, NCI''s Division of Cancer Epidemiology and Genetics (DCEG) has carried out genome-wide scans for breast, prostate, pancreatic, and lung cancers, while a GWAS of bladder cancer is currently underway. By making the data available to both intramural and extramural research scientists, as well as those in the private sector through rapid posting, NIH can leverage its resources to ensure that the dramatic advances in genomics are incorporated into rigorous population-based studies. Ultimately, findings from these studies may yield new preventive, diagnostic, and therapeutic interventions for cancer. Sponsors: This resource is supported by the U.S. National Institues Of Health.

Proper citation: CGEMS (RRID:SCR_008445) Copy   

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http://www.widetag.com/

Headquartered in Redwood City California, WideTag is a pioneer in architecting computing systems that integrate sensors, positioning devices and memory with social, Web 2.0-style services in applications that revolutionize business and push consumer technology.

Proper citation: Wide Tag (RRID:SCR_008566) Copy   

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http://www.biokin.com/dynafit/

Program DynaFit Analysis of (bio)chemical kinetics and equilibria Welcome to the DynaFit home page. Purpose Symbolic Notation Bibliographic Reference Numerical Methods Minimum System Requirements Purpose The main purpose of the program DynaFit is to perform nonlinear least-squares regression of chemical kinetic, enzyme kinetic, or ligand-receptor binding data. The experimental data can be either initial reaction velocities in dependence on the concentration of varied species (e.g., inhibitor concentration vs. velocity), or the reaction progress curves (e.g., time vs. absorbance). Symbolic Notation The main advantage in using the program DynaFit is in the ability to characterize the (bio)chemical reacting system in terms of symbolic, or stoichiometric, equations. For example, the ``slow, tight'''' inhibition of a dissociative dimeric enzyme is described by the following text: Monomer Monomer <==> Enzyme : k1 k2 Enzyme Inhibitor <==> Complex : k3 k4 Enzyme Substrate <==> ReactiveX : k5 k6 ReactiveX --> Product Enzyme : k7 k8 The names of chemical species (Monomer, Enzyme, etc.) are entirely arbitrary and can be freely chosen by the investigator. Bibliographic Reference If you publish any results obtained by using DYNAFIT, plase cite the following reference: Kuzmic, P. (1996) Anal. Biochem. 237, 260-273. Program DYNAFIT for the Analysis of Enzyme Kinetic Data: Application to HIV Proteinase ABSTRACT A computer program with the code name DYNAFIT was developed for fitting either the initial velocities, or the time-course of enzyme reactions, to an arbitrary molecular mechanism represented symbolically by a set of chemical equations. Seven numerical tests and five graphical tests are applied to judge the goodness of fit. Experimental data on the inhibition of the dissociative dimeric proteinase from HIV were used in four test examples. A set of initial velocities was analyzed to see if a tight-binding inhibitor could bind to the HIV proteinase monomer. Three different sets of progress curves were analyzed (i) to determine the kinetic properties of an irreversible inhibitor; (ii) to investigate the dissociation and denaturation mechanism for the protease dimer; and (iii) to investigate the inhibition mechanism for a transient inhibitor. See a MEDLINE abstract with related references concerning the kinetics of HIV-1 protease. Numerical Methods The nonlinear regression module uses the Levenberg-Marquardt algorithm [1]. The time-course of (bio)chemical reactions is computed by the numerical integration of simultaous first-order ordinary differential equations, using the Livermore Solver of ODe Systems (LSODE, [2]). The composition of complex mixtures at equilibrium (e.g., in the concentration jump experiment where a complex mixture is incubated prior to the addition of a reagent) is computed by solving simultaneous nonlinear algebraic equations, namely, the mass balance equations for the component species, by using the multidimensional Newton-Raphson method [3]. References G. A. F. Seber and C. J. Wild (1989) Nonlinear Regression, Wiley, New York, p. 624. A. C. Hindmarsh (1983) ODEPACK: a systematized collection of ODE solvers; in Scientific Computing, ed. R. S. Stepleman et al., North Holland, Amsterdam, pp. 55--64. E. Kreyszig (1993) Advanced Engineering Mathematics; 7th ed., John Wiley, New York, p. 929. Minimum System Requirements DynaFit for Windows Intel Pentium III or Celeron class 800 MHz or faster processor Microsoft Windows XP (SP1) or 2000 (SP2) 128 MB RAM 20 MB Hard Disk Space Ethernet Network Interface Card required for license activation(1) CD/DVD-ROM drive required for software installation(2) (1) The Network Interface Card is used to compute a unique Computer ID, tied to a particular DynaFit license. Essentially the Computer ID required for license activation is an encrypted Media Access Control (MAC address) associated with the given Network Card. (2) CD/DVD-ROM is not required if the software is being installed by using the downloadable installer file dynafit-install.zip. Sponsor. This work has been supported by the NIH, grant No. R43 AI52587-02 and the U.S. Department of Defense, U.S. Army Medical Research and Materials Command, Ft. Detrick, MD, administered by the Pacific Telehealth & Technology Hui, Honolulu, HI, contract No. V549P-6073.

Proper citation: Program DynaFit (RRID:SCR_008444) Copy   

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http://www.wheaton.com

Wheaton Industries Inc. is a leading marketer, manufacturer and re-packager of containers, laboratory ware, instrumentation and associated products and services sold principally to customers in the general laboratory, life sciences, and diagnostics and reagent / chemicals packaging markets. Our products and services are marketed and sold globally through two divisions. The laboratory research products are sold through Wheaton Science Products, and the packaging products are sold through Wheaton Science Packaging.

Proper citation: Wheaton Industry Inc (RRID:SCR_008565) Copy   

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http://www.phru.nhs.uk/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. Tools were developed by the Critical Appraisal Skills Programme (CASP) to help with the process of critically appraising articles of the following types of research. These are available and free to download for personal use.

Proper citation: Public Health Resources Unit (RRID:SCR_008564) Copy   

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http://www.randomizer.org

This site is designed for researchers and students who want a quick way to generate random numbers or assign participants to experimental conditions. Research Randomizer can be used in a wide variety of situations, including psychology experiments, medical trials, and survey research. The program uses a JavaScript random number generator to produce customized sets of random numbers. Since its release in 1997, Research Randomizer has been used to generate number sets over 10.7 million times. This service is part of Social Psychology Network and is fast, free, and runs with any recent web browser as long as JavaScript isn''t disabled. Research Randomizer is a free service offered to students and researchers interested in conducting random assignment and random sampling. By using this service, you agree to abide by the SPN User Policy and to hold Research Randomizer and its staff harmless in the event that you experience a problem with the program or its results. Although every effort has been made to develop a useful means of generating random numbers, Research Randomizer and its staff do not guarantee the quality or randomness of numbers generated. Any use to which these numbers are put remains the sole responsibility of the user who generated them. What are the system requirements needed to run Research Randomizer? This program works best with Firefox and other recent web browsers. If you''re using a browser that came with America Online, or older browsers made prior to 2003, you may experience some difficulties with Research Randomizer. You may also not be able to use Research Randomizer with some limited-function browsers that do not fully support JavaScript, such as the Opera broswer used on certain game consoles. We would suggest that you update to a fairly recent, fully- functional stand-alone browser. How do I know what browser I am using? The easiest way to find this out is to click Help on the pulldown menu at the top of the screen. One of the options should be About Mozilla Firefox, About Internet Explorer, About Netscape, or something similar. Selecting this option will open a window that displays the name, version number, and copyright date of your browser. How does Research Randomizer generate its numbers? Research Randomizer uses the Math.random method within the JavaScript programming language to generate its random numbers for all modern web browsers. If you are using an older version of Microsoft Internet Explorer or Netscape Navigator (that is prior to version 4.0 of either), Research Randomizer uses an adaptation of the Central Randomizer by Paul Houle. Note that Research Randomizer no longer supports much-older browsers by other vendors (e.g., Mosaic). Who designed Research Randomizer? The original idea and programming for Research Randomizer came from Geoffrey C. Urbaniak in 1997. Research Randomizer was then jointly developed with Scott Plous, webmaster of Social Psychology Network, and online tutorials were added to the main program. In 1999 the site was redesigned with the assistance of Mike Lestik, in 2003 Mike Lestik added the download function, and in 2007 Mike Lestik and Scott Plous redesigned the site and added new content.

Proper citation: Research Randomizer (RRID:SCR_008563) Copy   

•   Source: SciCrunch Registry

http://www.ariad.com/

Cambridge, Massachusetts-based biotechnology company focused on cancer. Focus areas are blood cancers and solid tumors. Compounds: ponatinib, AP26113, ridaforolimus and AP1903., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ARIAD (RRID:SCR_008559) Copy   

•   Source: SciCrunch Registry

http://lagan.stanford.edu

About the LAGAN Toolkit The LAGAN Tookit consists of four components: CHAOS CHAOS is a pairwise local aligner optimized for non-coding, and other poorly conserved regions of the genome. It uses both exact matching and degenerate seeds, and is able to find homology in the presence of gaps. LAGAN LAGAN is our highly parametrizable pairwise global alignment program. It takes local alignments generated by CHAOS as anchors, and limits the search area of the Needleman-Wunsch algorithm around these anchors; Multi-LAGAN Multi-LAGAN is a generalization of the pairwise algorithm to multiple sequence alignment. M-LAGAN performs progressive pairwise alignments, guided by a user-specified phylogenetic tree. Alignments are aligned to other alignments using the sum-of-pairs metric. Shuffle-LAGAN Shuffle-LAGAN is a novel glocal alignment algorithm that is able to find rearrangements (inversions, transpositions and some duplications) in a global alignment framework. It uses CHAOS local alignments to build a map of the rearrangements between the sequences, and LAGAN to align the regions of conserved synteny. The website uses scripts written by Alex Poliakov. The website was designed by Marina Sirota.

Proper citation: LAGAN (RRID:SCR_008558) Copy   

•   Source: SciCrunch Registry

http://www.lexisnexis.com

A commercial software provider designed for legal, risk management, corporate, government, law enforcement, accounting, and academic markets. Sponsors: This resource is Reed Elsevier, Inc. Keywords: Workflow, Professional, Legal, Risk, Management, Corporate, Government, Law, Enforcement, Accounting, Academic, Technology, Information,

Proper citation: LexisNexis (RRID:SCR_008433) Copy   

•   Source: SciCrunch Registry

http://safcsupplysolutions.com

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. A business division of Sigma-Aldrich Corporation, focusing on providing custom manufactured products and specialized services used in the industrial development and manufacturing, including processes, that bring new drugs and new electronic products to market., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SAFC (RRID:SCR_008554) Copy   

•   Source: SciCrunch Registry

http://molbiocore.ucsd.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 31, 2022. The UCSD CFAR/VMRF Molecular Biology Core (MBC) is a service core designed to facilitate and support HIV/AIDS research at the University of California San Diego (UCSD), the VA San Diego Healthcare System (VASDHCS), the Veterans Medical Research Foundation (VMRF), the UCSD Antiviral Research Center (AVRC), the Scripps Research Institute, and others in the San Diego HIV/AIDS research community. The MBC provides a variety of services, including DNA sequencing, viral DNA and RNA quantification, cDNA microarray analysis of herpesvirus expression, lentiviral vectors, RNAi design and synthesis, custom vector and plasmid design and construction, plasmids and other reagents of interest to HIV/AIDS research, shared access to computational biology software, and a variety of other services. The core is operated in association with the VMRF, the UCSD AIDS Research Institute (ARI), the VASDHCS, and the VA Research Center for AIDS and HIV Infection (RACHI). The VMRF/CFAR MBC is open to all UCSD, VA, and VMRF investigators as well as those from outside institutions. Keywords: Biology, Research, Medical, Molecular, DNA, Sequencing, Healthcare, RNA, DNA, cDNA, Microarray, Analysis, Herpesvirus, Expression, Lentiviral,

Proper citation: UCSD Center for AIDS Research Molecular Biology Core (RRID:SCR_008435) Copy   

•   Source: SciCrunch Registry

http://rocr.bioinf.mpi-sb.mpg.de/

ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. ROCR integrates tightly with R''s powerful graphics capabilities, thus allowing for highly adjustable plots. Being equipped with only three commands and reasonable default values for optional parameters, ROCR combines flexibility with ease of usage. Performance measures that ROCR knows: Accuracy, error rate, true positive rate, false positive rate, true negative rate, false negative rate, sensitivity, specificity, recall, positive predictive value, negative predictive value, precision, fallout, miss, phi correlation coefficient, Matthews correlation coefficient, mutual information, chi square statistic, odds ratio, lift value, precision/recall F measure, ROC convex hull, area under the ROC curve, precision/recall break-even point, calibration error, mean cross-entropy, root mean squared error, SAR measure, expected cost, explicit cost. ROCR features: ROC curves, precision/recall plots, lift charts, cost curves, custom curves by freely selecting one performance measure for the x axis and one for the y axis, handling of data from cross-validation or bootstrapping, curve averaging (vertically, horizontally, or by threshold), standard error bars, box plots, curves that are color-coded by cutoff, printing threshold values on the curve, tight integration with Rs plotting facilities (making it easy to adjust plots or to combine multiple plots), fully customizable, easy to use (only 3 commands). ROCR can be used under the terms of the GNU General Public License. Running within R, it is platform-independent.

Proper citation: Classifier Visualization in R (RRID:SCR_008551) Copy   

•   Source: SciCrunch Registry

http://dove.embl-heidelberg.de/Blast2/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. This portal let you search BLAST through the WU-BLAST2 Search Service provided by the Bork Group at EMBL. Sponsors: This resource is supported by EMBL., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Bork Group's WU-BLAST2 Search Service at EMBL (RRID:SCR_008431) Copy   

•   Source: SciCrunch Registry

http://grey.colorado.edu/emergent

emergent is a comprehensive, full-featured neural network simulator that allows for the creation and analysis of complex, sophisticated models of the brain in the world. With an emphasis on qualitative analysis and teaching, it also supports the workflow of professional neural network researchers. Its high level drag-and-drop programming interface, built on top of a scripting language that has full introspective access to all aspects of networks and the software itself, allows one to write programs that seamlessly weave together the training of a network and evolution of its environment without ever typing out a line of code. Networks and all of their state variables are visually inspected in 3d, allowing for a quick visual regression of network dynamics and robot behavior. This same 3d world sports a highly accurate Newtonian physics simulation, allowing you to create rich robotics simulations (for example, a car). As a direct descendant of PDP (1986) and PDP (1999), emergent has been in development for decades. In the most recent versions available strive to distill it down to its essential elements. Those that take the time to learn the best practices will be rewarded with the ability to create and understand the most complicated neural models ever published.

Proper citation: Emergent (RRID:SCR_008500) Copy   

•   Source: SciCrunch Registry

http://cython.org/

Cython is a language that makes writing C extensions for the Python language as easy as Python itself. Cython is based on the well-known Pyrex, but supports more cutting edge functionality and optimizations. The Cython language is very close to the Python language, but Cython additionally supports calling C functions and declaring C types on variables and class attributes. This allows the compiler to generate very efficient C code from Cython code. This makes Cython the ideal language for wrapping external C libraries, and for fast C modules that speed up the execution of Python code. Sponsor. Google and Enthought funded Dag Seljebotn to greatly improve Cython integration with NumPy. Kurt Smith and Danilo Freitas were funded through the Google Summer of Code program to work on improved Fortran and C support respectively.

Proper citation: Cython C-Extensions for Python (RRID:SCR_008466) Copy   

•   Source: SciCrunch Registry

http://www.unil.ch/dafl

The Lausanne Genomics Technologies Facility (GTF) is a genomic technologies core laboratory serving the Lausanne and Lemanic region research community. It is housed in and administered by the Center for Integrative Genomics. The GTF offers a range of microarrays services, including : providing access to the instrumentation and the consumables that are required for the use of the pre-printed oligonucleotide microarrays available from Affymetrix and Illumina as well as miRNA gene microarrays from Agilent Technologies providing access to and supporting applications using the Illumina Genome Analyzer 2 ultra high throughput DNA sequencing platform providing access to the instrumentation and the consumables that are required for performing quantitative real-time PCR analyses using the Applied Biosystems 7900HT Sequence Detection System. providing bioinformatics support and consultation services at the stages of experimental design, data collection and storage, image analysis and data analysis acting as a center of experience, expertise and training in microarray and quantitative PCR technologies and methodologies. Laboratory space and computer workstations are available to users wanting to perform the experiments and/or analyses in the facility. The GTF also acts as an information clearing house for the user community by providing a forum for the sharing of methods, protocols and experience generated by the GTF and community scientists using microarray and quantitative PCR technology investigating and implementing, when appropriate, microarray-based methods for applications other than gene expression monitoring (e.g. SNP detection) participating in the evaluation of new RNA expression profiling and nucleic hybridization detection technologies as they develop and incorporate the appropriate technologies into the services offered by the facility

Proper citation: Lausanne Genomic Technologies Facility (RRID:SCR_008468) Copy   

•   Source: SciCrunch Registry

http://amser.org./index.php?P=AMSER--About

AMSER is a portal of educational resources and services built specifically for use by those in Community and Technical Colleges but free for anyone to use. AMSER provides links to resources for faculty, staff, librarians, students and others for use in both educational settings or in their pursuit of life long learning. Sponsors: AMSER is funded by the National Science Foundation (NSF) as part of the National Science Digital Library, and is being created by a team of project partners led by Internet Scout.

Proper citation: Applied Math and Science Education Repository (RRID:SCR_008501) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/scientific-community/science/programs/cancer/ultrasome

An efficient methodology for detecting and delineating gains and losses of chromosomal material in DNA copy-number data.

Proper citation: Ultrasome (RRID:SCR_008465) Copy   

•   Source: SciCrunch Registry

http://www.cff.org/treatments/Pipeline/

The Cystic Fibrosis Foundation has built a dynamic pipeline for the development of more new potential cystic fibrosis (CF) therapies than ever before. To treat a complex disease like CF, therapies must target problems in the airways and the digestive system. In the CF drug development pipeline, there also are promising new therapies designed to rectify the cause of CF a faulty gene and/or its faulty protein product. Cystic fibrosis is an inherited chronic disease that affects the lungs and digestive system of about 30,000 children and adults in the United States (70,000 worldwide). A defective gene and its protein product cause the body to produce unusually thick, sticky mucus that: clogs the lungs and leads to life-threatening lung infections; and obstructs the pancreas and stops natural enzymes from helping the body break down and absorb food. In the 1950s, few children with cystic fibrosis lived to attend elementary school. Today, advances in research and medical treatments have further enhanced and extended life for children and adults with CF. Many people with the disease can now expect to live into their 30s, 40s and beyond.

Proper citation: Drug Development Pipeline (RRID:SCR_008464) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/cgi-bin/blast/submitblast/d_rerio

This Blast server offers searches against all D. rerio finished and unfinished clones in the Sanger sequencing pipeline. You can now also search the de novo assemblies generated from sequencing of one doubled haploid homozygous individual of each the AB and Tuebingen strain. Both fish were sequenced to ~40x coverage using Illumina GA sequencing technology and the sequences were assembled using Phusion2, resulting in a 1,33 Gb AB and a 1.48 Gb Tuebingen assembly. Due to the short reads and short inserts and no integration of physical or genetic map data, both assemblies are highly fragmented - with an N50 contig size of about 5kb. Mis-assembly errors may also be present in the contigs. Please note these assemblies are independent additions to the assemblies released by the zebrafish genome project and are intended to aid identification of polymorphisms between these two strains. Charity. Genome Research Limited is a charity registered in England with number 1021457

Proper citation: D. rerio Blast Server (RRID:SCR_008461) Copy   

•   Source: SciCrunch Registry