We support boolean queries, use +,-,<,>,~,* to alter the weighting of terms
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. Database of high throughput insertional mutagenesis screening projects of retroviral and transposon insertional mutagenesis in mouse tumors. Information in the RTCGD is obtained from sequence comparison by using public databases UCSC genome mm9 browser. Data based on previous genome assembly mm8 is also available at RTCGD mm8. MCGP has developed three web search tools including Easy Search to query proviral integration sites using mouse gene symbol of gene name; Model Search to obtain RIS information based on tumor models and/or tumor types; Interaction Search to find gene-to-gene interaction. It displays the list of genes which reside in the same tumor to your gene of interest.
Software platform to explore, analyze and visualize data. SAS 9.4 is part of SAS Platform. Standardized data governance and management from statistical software company SAS.
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.<BR/>
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.
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.
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.
Welcome to the Department of Genome Sciences, which began in September 2001 by the fusion of the Departments of Genetics and Molecular Biotechnology. Our goal is to address leading edge questions in biology and medicine by developing and applying genetic, genomic and computational approaches that take advantage of genomic information now available for humans, model organisms and a host of other species. Our faculty study a broad range of topics, including the genetics of E. coli, yeast, C. elegans, Drosophila, and mouse; human and medical genetics; mathematical, statistical and computer methods for analyzing genomes, and theoretical and evolutionary genetics; and genome-wide studies by such approaches as sequencing, transcriptional and translational analysis, polymorphism detection and identification of protein interactions. Our chair, Dr. Robert Waterston, joined the department in January 2003. Our department includes both faculty with primary appointments in Genome Sciences, as well as adjuncts in other departments and Seattle institutions. Nine faculty are members of the National Academy of Sciences, including 2001 Nobel Prize winner Dr. Lee Hartwell, who conducted much of his groundbreaking work in the Department of Genetics. Five training faculty are Howard Hughes Medical Institute Investigators. Graduate research in the Department leads to a Ph.D. in Genome Sciences and students may also choose to participate in the Computational Molecular Biology or Molecular Medicine programs. Our department has around 55 - 60 graduate students at any given time and has moved into the new William H. Foege Building.
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on November 20, 2019. Development and implementation of algorithms to predict nucleic acid folding and hybridization by free energy minimization using empirically derived thermodynamic parameters. Modeling and algorithm development have been closely coupled with the derivation of nearest neighbor and related energy rules. Current work is focused on the computation of partition functions for systems containing two molecules in solution that can fold as well as hybridize with each other. Ensemble free energies, mole fractions of different monomer and dimer species and base pair probabilities are computed over a range of temperatures. These computations lead to the prediction of UV absorbance (optical density) and heat capacity (Cp) melting profiles that can be directly compared with experimental data. A related project is the development of an algorithm named FASTH that searches RNA or DNA sequence databases for optimal hybridization sites for nucleic acid query sequences. Unlike traditional search algorithms, such as BLASTN and FASTA, FASTH uses hybridization free energy as the criterion for selection
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Retrieve-ensembl-seq is included in the software suite regulatory sequence analysis tools (RSAT), allowing instant submission of retrieved sequences to further analysis tools. AVAILABILITY: retrieve-ensembl-seq is integrated in the RSAT suite: http://rsat.ulb.ac.be/rsat. Web site: http://rsat.ulb.ac.be/rsat/retrieve-ensembl-seq_form.cgi. Web services: http://rsat.ulb.ac.be/rsat/web_services/RSATWS.wsdl. Stand-alone distribution: freely available under an academic licence to download from the RSAT web site. The complete manual, a convenient tutorial and demos are available from the RSAT website. Additional help can be found on the RSAT public forum.
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.
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.
Our vision a world where regular physical activity, good nutrition, and healthy weight are part of everyone''s life. Our mission to lead strategic public health efforts to prevent and control obesity, chronic disease, and other health conditions though regular physical activity and good nutrition. Our goals: * Increase health-related physical activity through population-based approaches. * Improve those aspects of dietary quality most related to the population burden of chronic disease and unhealthy child development. * Decrease prevalence of obesity through preventing excess weight gain and maintenance of healthy weight loss. Our Work With fiscal year (FY) 2008 funding of 38 million, CDC''s DNPAO is working to reduce obesity and obesity-related diseases. This is done through state programs, research, surveillance, training, intervention development and evaluation, leadership, policy and environmental change, communication and social marketing, and partnership development. See At A Glance 2009 for more. Supporting State Programs The Nutrition, Physical Activity and Obesity Program (NPAO) is a cooperative agreement between the Centers for Disease Control and Prevention''s Division of Nutrition, Physical Activity and Obesity (DNPAO) and 23 state health departments. The program goal is to prevent and control obesity and other chronic diseases through healthful eating and physical activity. The state program will develop strategies to leverage resources and coordinate statewide efforts with multiple partners to address all of the following DNPAO principal target areas: 1. Increase physical activity. 2. Increase the consumption of fruits and vegetables. 3. Decrease the consumption of sugar sweetened beverages. 4. Increase breastfeeding initiation, duration and exclusivity. 5. Reduce the consumption of high energy dense foods. 6. Decrease television viewing. Our Research DNPAO supports research to enhance the effectiveness of physical activity and nutrition programs. Topics of these research activities include: * the effectiveness of parent-focused strategies to reduce the time children spend watching television * the influences of the home environment on sugar-sweetened beverage consumption * the use of policy interventions to promote physical activity * the effectiveness of breastfeeding interventions in various settings. Publications: http://www.cdc.gov/nccdphp/DNPAO/aboutus/manuscripts/index.html
Recombineering (recombination-mediated genetic engineering) is a powerful method for fast and efficient construction of vectors for subsequent manipulation of the mouse genome or for use in cell culture experiments. It is also an efficient way of manipulating the bacterial genome directly. Recombineering is a method based on homologous recombination in E. Coli using recombination proteins provided from ? phage. Our bacterial strains contain a defective ? prophage inserted into the bacterial genome. The phage genes of interest, exo, bet, and gam, are transcribed from the ?PL promoter. This promoter is repressed by the temperature-sensitive repressor cI857 at 32C and derepressed (the repressor is inactive) at 42C. When bacteria containing this prophage are kept at 32C no recombination proteins are produced. However, after a brief (15 minutes) heat-shock at 42C a sufficient amount of recombination proteins are produced. exo is a 5''-3'' exonuclease that creates single-stranded overhangs on introduced linear DNA. bet protects these overhangs and assists in the subsequent recombination process. gam prevents degradation of linear DNA by inhibiting E. Coli RecBCD protein. Linear DNA (PCR product, oligo, etc.) with sufficient homology in the 5'' and 3'' ends to a target DNA molecule already present in the bacteria (plasmid, BAC, or the bacterial genome itself) can be introduced into heat-shocked and electrocompetent bacteria using electroporation. The introduced DNA will now be modified by exo and bet and undergo homologous recombination with the target molecule. The method is so efficient that co-electroporation of a supercoiled plasmid and a linear piece of DNA into heat-shocked, electrocompetent bacteria will work as well.
Software application for a fast exact Hardy-Weinberg Equilibrium test for SNPs (entry from Genetic Analysis Software)
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.
Software automated tool for analysis and determination of Nuclear Localization Signals (NLS). Predicts that your protein is nuclear or finds out whether your potential NLS is found in our database. The program also compiles statistics on the number of nuclear/non-nuclear proteins in which your potential NLS is found. Finally, proteins with similar NLS motifs are reported, and the experimental paper describing the particular NLS are given.
LOCI is a biophotonics instrumentation laboratory stemming from the research activities of Kevin Eliceiri, Dave Beebe, Bill Bement, Paul Campagnola, Patti Keely, Brenda Ogle, Justin Williams and other LOCI investigators. Our mission is to develop advanced optical and computational techniques for imaging and experimentally manipulating living specimens. New and improved imaging instrumentation and optical-based experimental techniques are being developed. These projects are driven by demands arising from the scientific studies of external collaborators and the principal investigators and opportunities that arise with the emergence of new technology. Instrumentation development is undertaken in a form that is both accessible and beneficial to the scientific community. LOCI is directed by Kevin Eliceiri, and all inquiries about LOCI collaborations or general imaging questions may be directed to him.
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.
This server provides programs, web services, and databases, related to our work on RNA secondary structures. For general information and other offerings from our group see the main TBI web server. With the 1st of May 2009 we updated our servers to the Vienna RNA package version 1.8.2! The Vienna RNA Servers: * RNAfold server predicts minimum free energy structures and base pair probabilities from single RNA or DNA sequences. * RNAalifold server predicts consensus secondary structures from an alignment of several related RNA or DNA sequences. You need to upload an alignment. * RNAinverse server allows you to design RNA sequences for any desired target secondary structure. * RNAcofold server allows you to predict the secondary structure of a dimer. * RNAup server allows you to predict the accessibility of a target region. * LocARNA server generates structural alignments from a set of sequences. In collaboration with the Bioinformatics Group Freiburg. * barriers server allows you to get insights into RNA folding kinetics. * RNAz server will assist you in detecting thermodynamically stable and evolutionarily conserved RNA secondary structures in multiple sequence alignments. * Structure conservation analysis server will assist you in detecting evolutionarily conserved RNA secondary structures in multiple sequence alignments. * RNAstrand server allows you to predict the reading direction of evolutionarily conserved RNA secondary structures. * RNAxs server assists you in siRNA design. * Bcheck predicts rnpB genes Downloads Get the Source code for: * the Vienna RNA Package, our basic RNA secondary structure analysis software. * The ALIDOT package for finding conserved structure motifs (add-on) * The barriers program for analysis of RNA folding landscapes. Databases * Atlas of conserved Viral RNA Structures found by ALIDOT
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.