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http://rmaexpress.bmbolstad.com
RMAExpress is a standalone GUI program for Windows (and Linux) to compute gene expression summary values for Affymetrix Genechip data using the Robust Multichip Average expression summary and to carry out quality assessment using probe-level metrics. It does not require R nor is it dependent on any component of the BioConductor project. If focuses on processing 3'' IVT expression arrays, exon and WT gene arrays. What is RMA? RMA is the Robust Multichip Average. It consists of three steps: a background adjustment, quantile normalization (see the Bolstad et al reference) and finally summarization. Some references (currently published) for the RMA methodology are: Bolstad, B.M., Irizarry R. A., Astrand, M., and Speed, T.P. (2003), A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance. Bioinformatics 19(2):185-193 Supplemental information Rafael. A. Irizarry, Benjamin M. Bolstad, Francois Collin, Leslie M. Cope, Bridget Hobbs and Terence P. Speed (2003), Summaries of Affymetrix GeneChip probe level data Nucleic Acids Research 31(4):e15 Irizarry, RA, Hobbs, B, Collin, F, Beazer-Barclay, YD, Antonellis, KJ, Scherf, U, Speed, TP (2002) Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Accepted for publication in Biostatistics. [Abstract, PDF, PS, Complementary Color Figures-PDF, Software] What do I need? You will need the appropriate CDF and CEL files for your dataset. For Exon and WT Gene arrays, the PGF and CLF should be used instead of the CDF file to build a CDFRME file. The process for doing this is explained in the user manual. Some pre-built CDFRME files are also available. CDFRME files HuEx_CDFRME.zip (95.9MB) HuGene_CDFRME.zip (5.5MB) MoEx_CDFRME.zip (79.6MB) MoGene_CDFRME.zip (6.3MB) RaEx_CDFRME.zip (48.4MB) RaGene_CDFRME.zip (5.7MB) Can I use affy/BioConductor instead? Of course. Hypothetically you will get the same results from both places, provided you have consistent settings in affy/BioConductor and RMAExpress. Some people prefer the power and flexibility of R and others like the point and click simplicity of a GUI. RMAExpress caters to the second option. Since RMAExpress outputs the computed expression values to a text file, you may of course load the expression measures into R and use features of Bioconductor for the analysis of your gene expression values. You can of course open the results file in any other application that supports importing plain text files. Will I get the same results as I would using affy/Bioconductor? Yes. The results from RMAExpress should be consistent. What are the machine requirements? A good rule of thumb is the more RAM you have the better. I would recommend at least 1GB, though 512MB will work in most situations. At this point the program has been tested using Windows 2000, Windows XP, Windows Vista and Linux. Most recently I have had a report of over 10,000 arrays processed in a single session. Can I do any quality assessment? Yes, store the residuals when you compute the expression values. Then you may examine chip pseudo-images of the residuals. Note that high positive residuals are colored increasingly read and low negative residuals are colored increasingly blue. To better interpret these images and gain a better feel for what is typical you may visit the PLM Image Gallery where images for a number of different datasets are shown. Access to the NUSE and RLE quality assessment metrics is also provided. How do I download and install it? Click here for the current release Windows version. Use the installer to install the program. The current release version number is 1.0 (released June 29, 2008). A pre-built linux version is not currently available, but you may build it using the source code. You can download pre-release versions from the following table (the release versions will be more stable, the development versions may have features that are incomplete or will be removed or altered before the next release was supported by the PGA U01 HL66583.
Proper citation: RMA Express (RRID:SCR_008549) Copy
http://biq-analyzer.bioinf.mpi-sb.mpg.de
BiQ Analyzer is a software tool for easy visualization and quality control of DNA methylation data from bisulfite sequencing. Highlights: - End-to-end support of the analysis process: from raw sequence files to a comprehensive documentation and visualization. - Automatically generate publication-quality lollipop diagrams (show example.) - Integrated 1-click multiple sequence alignment. - Automated CpG highlighting- never spend your time highlighting CpGs by hand anymore. - Open electropherogram files to check for sequencing problems (requires an electropherogram viewer such as Chromas LITE.) - Generate MethDB-compatible DNA methylation files for database submission. - Factor 5 speedup of sequence analysis while at the same time achieving better data quality. Intended users: - Anyone who works with DNA methylation data from bisulfite sequencing. - Occasional users as well as experts (the former will benefit from the help that the program gives in order to achieve a good quality management whereas the latter will save hours and days of tedious work.) Sponsors: This resource is supported by the Max Planck Institute. Keywords: Software, Visualization, DNA, Methylation, Data, Bisulfite, Sequencing, Electropherogram, Analysis,
Proper citation: BiQ Analyzer: A Software Tool for DNA Methylation Analysis (RRID:SCR_008423) Copy
http://www.sanger.ac.uk/PostGenomics/S_pombe/
The laboratory studies global gene expression programs in fission yeast (S. pombe). They apply a wide range of integrated approaches to analyse regulatory networks during cell proliferation, differentiation and quiescence including genetic and environmental perturbations. They are also interested in genetic diversity, genome evolution, and the complex interactions between genotypes, phenotypes, and the environment. The relative simplicity of the yeast cell promises a deeply satisfying, systems-level understanding of its inner workings within our life time Sponsors: This research is mainly funded by Cancer Research UK and the EC FP7 PhenOxiGEn project. Keywords: Gene, Expression, S.pombe, Yeast, Cell, Proliferation, Differentiation, Environmental, Genetic, Diversity, Genome, Evolution, Genotype, Phenotype, Environment,
Proper citation: Bahler Laboratory: Genome Regulation (RRID:SCR_008422) Copy
http://statgen.ncsu.edu/qtlcart/
QTL Cartographer is a suite of programs to map quantitative traits using a map of molecular markers. The programs are available via an anonymous ftp server. See the README for more information. You will also want a copy of Gnuplot to display plots made by QTL Cartographer. Gnuplot is freely available on the web. Do a search to find the latest version for your operating system. Windows QTL Cartographer Windows QTL Cartographer is a user friendly version of QTL Cartographer. It has a GUI interface and runs under Microsoft Windows. Manual The manual for QTL Cartographer is written in LaTeX2e. An Adobe Portable document format (pdf) version is available with the distribution of the programs. Look in the doc/pdf folder for the manual.pdf file. This file can be printed or viewed using Acrobat Reader, available through the Adobe website. The manual has also been translated into html. It is available through the following link. Please note that the translator is not perfect: The pdf form of the manual is much more accurate. Specifically, latex2html failed to translate figure 2.4 and simply printed 2.3 twice. Man Pages In the UNIX world, it is comman to have man pages for programs. We have written such a set of man pages, and these are available with the UNIX distribution. The man pages are also a part of the manual.pdf file. Here is a list of the man pages. 1. Emap 2. Rmap 3. Rqtl 4. Rcross 5. Qstats 6. LRmapqtl 7. SRmapqtl 8. Zmapqtl 9. JZmapqtl 10. MImapqtl 11. MultiRegress 12. Prune 13. Preplot 14. Eqtl 15. QTLcart Perl scripts QTL Cartographer comes with some perl scripts to automate repetitive tasks and reformat output files. They are available in the doc/scripts subdirectory of the distribution. Here are the man pages that explain what the scripts can do. 1. Bootstrap.pl is a script for running a bootstrap analysis. 2. CWTupdate.pl is used with Permute.pl for the comparison-wise thresholds. 3. EWThreshold.pl is used with Permute.pl for the experiment-wise thresholds. 4. GetMaxLR.pl is used with Permute.pl for the experiment-wise thresholds. 5. Model8.pl iterates Zmapqtl to find a stable set of cofactors for composite interval mapping. 6. Permute.pl is a script for running a permutation test. 7. Prepraw.pl allows you to reformat and check a Mapmaker data file. 8. SRcompare.pl will compare the set of cofactors in two SRmapqtl output files. 9. SSupdate.pl is used with Bootstrap.pl to update the sum and sum of squares for the likelihoods and parameter estimates. 10. Vert.pl converts text file line endings between Unix, Macintosh and Windows. 11. Ztrim.pl redisplays Zmapqtl output so that it fits in a terminal window. Data We are now posting published data sets to our web site. A list of links to the ftp subdirectories follows. Each directory contains a set of text files of data. Please read the Readme file in the directory for information on the data. 1. Zeng et al provide data for their paper Genetic architecture of a morphological shape difference between two Drosophila species. If you have any data that you would like to make available via our server, contact Chris Basten. Presentations From time to time, Chris Basten gives presentations on how to use QTL Cartographer. These presentations are created in Microsoft Powerpoint. The source file for the presentation is available with the distribution of the programs. Look in the doc subdirectory. Binary Traits See this for more information on the BTmapqtl module. This is an add-on written in LaurenMcIntyre''s lab. BTmapqtl is in the binary directory of the distribution (and is created with a make for the UNIX version).
Proper citation: Bionformatics Research Center (RRID:SCR_008540) Copy
http://mothra.ornl.gov/cgi-bin/cat/cat.cgi
A repository of tools for analysis and annotation of CAZYmes (Carbohydrate Active enZYmes)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: CAT (RRID:SCR_008421) Copy
THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The brain is made of billions of neurons, which together form the world''s most powerful information-processing machine. Despite decades of research, the fundamental principle by which these cells work together is still unknown. Many theories for brain function have been proposed over the last century. But only in the last few years has it become possible to record simultaneously from large enough numbers of neurons to put these theories to the test experimentally. This is an unprecedented opportunity, but it opens up a new question: how do we go from the gigabytes of experimental data that we now have, to concise conclusions about the function of the brain? The data processing methods traditionally used in neuroscience are not sophisticated enough to exploit this new flood of information. Fortunately, modern statistics and machine learning theory is making great strides in precisely the type of techniques needed to process these large multivariate databases. By applying these methods to neuronal data, we can now test long-standing hypotheses about brain function. The Cell Assembly The main focus of our research is an experimental search for cell assemblies. Before describing what a cell assembly is, it will be useful to describe what it is not. The brain is often thought of as a feed-forward system. In this scheme, sensory information is processed by successive levels of cortical analyzers, each of which transforms the results of previous levels, until sensory information is in a suitable form to guide the animals behavior. In support of this idea, the pattern of connections in the cortex does appear to respect a hierarchical organization, with the output of low-level areas corresponding to a single sensory modality being integrated into high-level multi-modal areas. Responses in higher-level sensory areas appear to have more complex responses to sensory stimuli, in agreement with increased abstraction as the hierarchy is traversed. However, there are several levels at which this feed-forward picture is incomplete. At the circuit diagram level, there more connections projecting across and down the hierarchy, than there are feed-forward projections. What''s more, if information were processed in a strictly feed-forward manner, one would expect a neuron to respond identically to repeated presentations of the same sensory stimulus. Although this is a fairly good approximation in primary sensory areas of cortex, in high-level structures responses are often more variable than expected from strict sensory control. Finally, although feed-forward processing can describe how an animal could perform simple stimulus-response behaviors, it cannot explain more complex top-down behaviors such as memory or thought. An alternative point of view, put forward over 50 years ago by Canadian psychologist Donald Hebb, holds that recurrent and feedback connections play an essential role in brain function. The principal actor in this view is the cell assembly, an anatomically distributed subset of neurons, amongst which mutually excitatory connections have been strengthened by repeated co-activation, allowing the assembly to later maintain its activity through reverberation without direct sensory stimulation. This theory allows for sensory-response behavior, and also behavior resulting purely from internally generated cognitive activity, by the sequential activation of a series of assemblies, leading in turn to the production of motion. In our research, we search for signatures of assembly activity in simultaneous recordings from multiple neurons, and aim to characterize the properties of assembly activity in ways not possible from theory alone. Software for Automatic Clustering KlustaKwik is a program developed in the lab for automatic cluster analysis, specifically designed to run fast on large data sets. In order facilitate open-source development, it is now located at klustakwik.sourceforge.net. This study was supported by NIH grants MH073245 and DC009947; NSF grant SBE-0542013 to the Temporal Dynamics of Learning Center, an NSF Science of Learning Center; a National Institute on Deafness and Other Communication Disorders, NIH, grant DC-005787-01A1; and a Spanish grant FIS 2006-09294. K.D.H. is an Alfred P. Sloan fellow. We would like to dedicate this work to the memory of D. J. Amit.
Proper citation: Rutgers University Quantitative Neuroscience Laboratory (RRID:SCR_008541) Copy
The goals of this sequencing effort are to produce and publicly release a whole-genome assembly and auto-annotation of the Aedes genome representing 8X sequence coverage. In collaboration, these centers have delivered the target 8X draft coverage of the disease vector genome. Assembly of the genome was performed using the Broad''s whole genome assembly package ARACHNE (Batzoglou et al., 2002 and Jaffe et al., 2003). The Aedes genome will be annotated in a collaborative effort involving both MSCs and Vectorbase, which is a bioinformatics resource center at the University of Notre Dame. Sponsor: This resource is supported by the National Institute of Allergy and Infectious Diseases. Keywords: Genome, BLAST, Similarity, Search, Engine, Sequence, Bioinformatics, Resource,
Proper citation: BLAST Similarity Search (RRID:SCR_008419) Copy
http://www.broad.mit.edu/mpg/grail/
A tool to examine relationships between genes in different disease associated loci. Given several genomic regions or SNPs associated with a particular phenotype or disease, GRAIL looks for similarities in the published scientific text among the associated genes. As input, users can upload either (1) SNPs that have emerged from a genome-wide association study or (2) genomic regions that have emerged from a linkage scan or are associated common or rare copy number variants. SNPs should be listed according to their rs#''s and must be listed in HapMap. Genomic Regions are specified by a user-defined identifier, the chromosome that it is located on, and the start and end base-pair positions for the region. Grail can take two sets of inputs - Query regions and Seed regions. Seed regions are definitely associated SNPs or genomic regions, and Query regions are those regions that the user is attempting to evaluate agains them. In many applications the two sets are identical. Based on textual relationships between genes, GRAIL assigns a p-value to each region suggesting its degree of functional connectivity, and picks the best candidate gene. GRAIL is developed by Soumya Raychaudhuri in the labs of David Altshuler and Mark Daly at the Center for Human Genetic Research of Massachusetts General Hospital and Harvard Medical School, and the Broad Institute. GRAIL is described in manuscript, currently in preparation.
Proper citation: Gene Relationships Across Implicated Loci (RRID:SCR_008537) Copy
A commercial organization which provides assay technologies to isolate DNA, RNA, and proteins from any biological sample. Assay technologies are then used to make specific target biomolecules, such as the DNA of a specific virus, visible for subsequent analysis.
Proper citation: QIAGEN (RRID:SCR_008539) Copy
http://bioinf.uni-greifswald.de/augustus/
Software for gene prediction in eukaryotic genomic sequences. Serves as a basis for further steps in the analysis of sequenced and assembled eukaryotic genomes.
Proper citation: Augustus (RRID:SCR_008417) Copy
Features: * This software takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values. A point-and-click interface is now available! * The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. * A short tutorial on q-values and false discovery rates is provided with the manual. * Various plots are automatically generated, allowing one to make sensible significance cut-offs. * Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. * The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. This research was supported in part by a National Science Foundation graduate research fellowship.
Proper citation: Q-Value Software (RRID:SCR_008538) Copy
http://jakarta.apache.org/tomcat
Apache Tomcat is an open source software implementation of the Java Servlet and JavaServer Pages technologies. The Java Servlet and JavaServer Pages specifications are developed under the Java Community Process. Apache Tomcat is developed in an open and participatory environment and released under the Apache License version 2. Apache Tomcat is intended to be a collaboration of the best-of-breed developers from around the world. We invite you to participate in this open development project. Apache Tomcat powers numerous large-scale, mission-critical web applications across a diverse range of industries and organizations. Some of these users and their stories are listed on the PoweredBy wiki page.
Proper citation: Apache Tomcat (RRID:SCR_008411) Copy
Supports research in cellular analysis, genomics, proteomics, and drug discovery. It has merged with Thermo Fisher Scientific. One of several brands under Thermo Fisher Scientific corporation.
Proper citation: Invitrogen Antibodies (RRID:SCR_008410) Copy
https://apps.childrenshospital.org/clinical/research/ingber/GEDI/samples.htm
A program that opens a new perspective to the analysis of microarray data (e.g., gene expression profiling). Unlike traditional gene clustering software, GEDI is primarily sample-oriented rather than gene-oriented. By treating each high-dimensional sample, such as one microarray experiment, as an object, it accentuates the genome-wide response of a tissue or a patient and treats it as an integrated biological entity. Hence, GEDI honors the new spirit of a system-level approach in biology. Yet, it also allows the researcher to quickly zoom-in from global patterns onto individual genes that exhibit interesting expression behavior and retrieve gene-specific information. Therefore, GEDI unites a novel holistic perspective with the traditional gene-centered approach in molecular biology. GEDI allows experimental biologists or clinicians with no bioinformatics background to efficiently and intuitively navigate through a large number of expression profiles, each with a memorizable face, and inspect, group and collect them, like managing a stack of baseball cards. DYNAMIC ANALYSIS: The unique strength of GEDI, for which GEDI was originally developed, is that it can display the results of parallel monitoring of multiple high-dimensional time courses, such as the comparison of expression profile time evolution in response to a series of drugs. GEDI creates animated graphics showing how 10,000s of genes change their expression over time in response to 100s of separately tested drugs. STATIC ANAYLSIS: The signature graphical output of GEDI, the GEDI-mosaics provide a unique, one-glance visual engram that gives each microarray or other high-dimensional dataset a face. A characteristic of GEDI''s analysis is that it does not prejudicate any particular structure in the data (such as clusters or hierarchical organization). Thus, it allows the researcher to use human pattern recognition to perform a global first-level analysis of the data. Sponsor. The project was supported by the Air Force Office of Scientific Research and the National Health Institutes. It is distributed for free academic use by the Childrens Hospital, Boston., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: GEDI (RRID:SCR_008530) Copy
Latest publications: ELDERMET research has recently been published in the Proceedings of the National Academy of Sciences (USA). This work focuses on the composition and stability of the intestinal bacteria in older Irish adults. Read the paper here. Would you like to be part of ELDERMET? We are currently looking for people, aged 65 years or older, living in the community. All we ask is that you live in the Cork area, or are willing to travel to Cork, and have recently (within the last two/three weeks) taken any kind of antibiotic. It doesnt matter if you are still taking the antibiotic, as long as the finishing date isnt more than four weeks before your first visit to ELDERMET. ELDERMET Objectives To assess the composition of the faecal microbiota of elderly volunteers in the Irish population, using state-of-the-art molecular techniques. To correlate diversity, composition, and metabolic potential of the faecal microbial metagenome with health, diet and lifestyle indices that are a) likely to be influenced by the microbiota or b) to influence the microbiota. To develop recommendations for specific dietary ingredients, foodstuffs, functional foods and/or dietary supplements, that will improve the health of elderly consumers. To provide evidence-based recommendations for prospective studies to determine the molecular mechanisms for health improvements promoted by specific food ingredients that modulate components of the microbiota. ELDERMET Rationale The human intestinal microbiota is made up of approximately 1000 genetically unique organisms (phylotypes ) [1]. The bacteria present in the intestine make an important contribution to: metabolism executed in the gut [2] health, in diverse activites from pain perception [3] to cognitive function [4]. There is an increasing body of evidence linking alterations in the human gut microbiota with Inflammatory Bowel Disease [5, 6] and Irritable Bowel Syndrome [7]. The changing pattern of the gut microbiota in elderly subjects [8, 9] may be linked to host changes such as immunosenescence, increased susceptibility to disease and potentially systemic effects. The composition of the intestinal microbiota may be modulated by dietary components including prebiotics [10]. ELDERMET will determine the baseline composition of the gut microbiota of several hundred elderly Irish subjects using a combination of traditional culutre and molecular (culture-independent) methodologies. ELDERMET will explore potential correlations between microbiota composition and a range of health indices; cross-referencing data to dietary intake. Data will be analyzed in the context of the related FHRI projects in Nutrigenomics, Food Consumption, Food Safety, and Diet-Health. ELDERMET will provide recommendations to all stakeholders (including health practitioners and the health service, the food industry and the general public) on how to improve health based on defined modifications to dietary intake. Sponsor. This work is supported by the Goverment of Ireland Department of Agriculture Fisheries and Food/Health Research Board Food for Health Research Initiative award to the ELDERMET project as well as by a Science Foundation Ireland award to the Alimentary Pharmabiotic Centre. M.J.C. is now funded by a fellowship from the Health Research Board of Ireland.
Proper citation: ELDERMET Gut microbiota as an indicator and agent of nutritional health in elderly Irish subjects (RRID:SCR_008492) Copy
OMPC aims to enable reuse of the huge open and free code base of MATLAB on a free and faster growing Python platform. Running Python and MATLAB in a single interpreter avoids issues with running two separate applications. Python adds general purpose programming libraries to the convenient syntax of the language of technical computing. OMPC is not an interpreter, it lets Python to do the work. This means that if Python gets faster OMPC gets faster too. OMPC translates the m-files preserving the structure of the original programs as much as possible. Although OMPC comes with a library that emulates the features of numerical array of MATLAB there is nothing that will stop you from running the translated code the way you like it. This means that you could run the OMPC generated code on IronPython, Jython, PyPy or whatever else if you write your own numerical class. Sponsors: This resource is supported by RIKEN Brain Science Institute.
Proper citation: An Open-Source MATLAB-to-Python Compiler (RRID:SCR_008409) Copy
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
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
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
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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