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http://www.theseed.org/DinsdaleSupplementalMaterial/

his table shows the metadata and links to sources of the data and citations associated with the publicly available metagenome sequences used in the Dinsdale, Edwards, et al., analysis of 87 different metagenomes. The links will take you to the annotated sequences in the metagenomics SEED, CAMERA, and the NCBI Short Read Archve. Please note that all metagenomes are currently available to download via the ftp links, some are available in the meta-RAST, and other links will be added as soon as they become available. Citations for individual metagenomes will also be added as and when they become available. DNA sequences for all metagenomes are avaialble via anonymous FTP. Sponsor. This project was supported by the Gordon and Betty Moore Foundation Marine Microbial Initiative, National Science Foundation grants (F.R. and D.L.V.), a Department of Commerce ATP grant (F.R.), a National Research Initiative Competitive Grant from the USDA Cooperative State Research, Education and Extension Service (B.W.), the National Institute of Allergy and Infectious Diseases, the National Institutes of Health and the Department of Health and Human Services (R.S.).

Proper citation: Metagenomes Used in The Statistical Analysis (RRID:SCR_008483) Copy   

•   Source: SciCrunch Registry

http://www.dialog.com

Dialog provides critical information from the world''s most authoritative publishers, combined with the tools to search every bit of it with speed and precision. With direct operations in 27 countries, Dialog products and services are a combination of highly accurate online research tools offering access to unique and relevant databases designed to meet the specific needs of a wide range of users. Information professionals and end-users at business, professional, scientific, academic and government organizations in more than 100 countries prize Dialog services to meet their searching needs. As part of the Deep Web, which is estimated to be 500 times larger than the content accessible via Web search engines, Dialog products offer unparalleled depth and breadth of content coupled with the ability to search with precision and speed. Our collection of over 900 databases handles more than 700,000 searches and delivers over 17 million document page views per month. Searchable content on Dialog services includes articles and reports from thousands of real-time news feeds, newspapers, broadcast transcripts and trade publications, plus market research reports and analyst notes providing support for financial decision-making, as well as in-depth repositories of scientific and technical data, patents, trademarks and other intellectual property data. Additional content areas include government regulations, social sciences, food and agriculture, reference, energy and environment, chemicals, pharmaceuticals and medicine.

Proper citation: Dialog (RRID:SCR_008482) Copy   

•   Source: SciCrunch Registry

http://www.flintbox.com/technology.asp?page=3716

Welcome to Flintbox, an application that revolutionizes the way the innovation community can share technologies, distribute new materials and software, and collaborate on research projects. Hundreds of research institutions are participating in the Flintbox open innovation network. Become a member to contribute and explore We are excited to present the Flintbox Application Programming Interface (API) to provide users with an easy way to upload data from any system to Flintbox. This freely available tool for synchronized information exchange will be a valuable resource to the open innovation community. The Flintbox API gives you the power to: Import existing technology postings directly into Flintbox Avoid duplicate data entry to market your technologies, ideas, and materials Create postings from any database Export project information to other websites For more information about using and implementing the Flintbox API, please see the About the Flintbox API page. Featured Project The New Flintbox offers expanded transactional capabilities: credit cards, purchase orders, purchase authorizations, donations, and much more The global Flintbox community is ideal for marketing new technologies, creative works, course materials, and innovative ideas. Flintbox enables universities to maximize their outreach to a broad spectrum of the innovation community. Plus, Wellspring provides expertise in assisting TTOs, inventors, and industrial liaison offices to promote their technologies, research programs, and partnering opportunities to Flintbox. Flintbox for Corporations Corporations turn to Flintbox to cultivate existing relationships and engage in new opportunities for collaborative research and solution sourcing from universities and other companies. For example, a pharmaceutical firm uses Flintbox to provide researchers and physicians software to assess patient outcomes. Flintbox for Technology Communities Flintbox empowers technology communities to connect effectively in a geographic region, building a Sphere of Innovation to truly recognize and capitalize on the valuable relationships and innovation assets in your community. For example, by creating or joining a community Group of members with common interests, say tissue engineering or artificial intelligence, you can link to other groups and explore common interests and complementary resources, for a dynamic collaborative effort and to efficiently share related technology projects. Flintbox is a registered trademark of Wellspring Worldwide, LLC

Proper citation: Flintbox (RRID:SCR_008519) Copy   

•   Source: SciCrunch Registry

http://tree.bio.ed.ac.uk/software/figtree

A graphical viewer of phylogenetic trees and a program for producing publication-ready figures. It is designed to display summarized and annotated trees produced by BEAST.

Proper citation: FigTree (RRID:SCR_008515) Copy   

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http://www.fltk.org

This document defines the processes and standards that all FLTK developers must follow when developing and documenting FLTK, and how trouble reports are handled and releases are generated. The purpose of defining formal processes and standards is to organize and focus our development efforts, ensure that all developers communicate and develop software with a common vocabulary/style, and make it possible for us to generate and release a high-quality GUI toolkit which can be used with a high degree of confidence. Much of this file describes the existing practices that have been used up through FLTK 1.1.x, however I have also added some new processes/standards to use for future code and releases. The fltk-dev mailing list and fltk.development newsgroup are the primary means of communication between developers. All major design changes must be discussed prior to implementation. Specific Goals The specific goals of the FLTK are as follows: Develop a C++ GUI toolkit based upon sound object-oriented design principles and experience. (*) Minimize CPU usage (fast). (*) Minimize memory usage (light). (*) Support multiple operating systems and windowing environments, including UNIX/Linux, MacOS X, Microsoft Windows, and X11, using the native graphics interfaces. (*) Support OpenGL rendering in environments that provide it. (*) Provide a graphical development environment for designing GUI interfaces, classes, and simple programs. (*) Support UTF-8 text. Support printer rendering in environments that provide it. Support schemes, styles, themes, skinning, etc. to alter the appearance of widgets in the toolkit easily and efficiently. The purpose is to allow applications to tailor their appearance to the underlying OS or based upon personal/user preferences. Support newer C++ language features, such as templating via the Standard Template Library (STL), and certain Standard C++ library interfaces, such as streams. However, FLTK will not depend upon such features and interfaces to minimize portability issues. Support intelligent layout of widgets. Many of these goals are satisfied by FLTK 1.1.x (*), and many complex applications have been written using FLTK on a wide range of platforms and devices. Development of the remaining features is proceding for FLTK 2.0 with a new, namespace-based API. While 2.0 offers some limited 1.x source compatibility, the changes to the underlying widget classes are significant enough to prevent full compatibility. Software Development Practices Documentation All widgets are documented using the Doxygen software; Doxygen comments are placed in the header file for the class comments and any inline methods, while non-inline methods should have their comments placed in the corresponding source file. The purpose of this separation is to place the comments near the implementation to reduce the possibility of the documentation getting out of sync with the code. All widgets must have a corresponding test program which exercises all widget functionality and can be used to generate image(s) for the documentation. Complex widgets must have a written tutorial, either as full text or an outline for later publication The final manuals are formatted using the HTMLDOC software. Sponsor. Easy Software Products

Proper citation: Fast Light Toolkit (RRID:SCR_008514) Copy   

•   Source: SciCrunch Registry

http://fluxus-technology.com/sharenet.htm

DNA software and consultancy: The DNA Alignment software and Network software is used by biologists, anthropologists, medical researchers and students world wide. We carry out phylogeographic consultancy for US, UK and German clients, including legal medical work. We were involved in the tv projects The Real Eve (Discovery Channel) and Motherland (BBC). Our biotechnological director Dr Peter Forster is on the editorial board of the International Journal of Legal Medicine since 1999. Technology and sales consultancy: Clients include multinational corporations, research institutions, and medium to small businesses. Client quotes: Vorbildlicher Einsatz (Dr Stephan Hitzel, EADS, in CADplus 1/2003 journal, Cover Story). We have had an effective business relationship with Fluxus Technology since 1999, and their experience of the German market has proved to be invaluable as part of our operations supplying high end engineering software and consultancy services right across the engineering supply chain (Andy Chinn, Business Development Manager, ITI TranscenData, February 2006). Abstract. Indo-European is the largest and best-documented language family in the world, yet the reconstruction of the Indo-European tree, first proposed in 1863, has remained controversial. Complications may include ascertainment bias when choosing the linguistic data, and disregard for the wave model of 1872 when attempting to reconstruct the tree. Essentially analogous problems were solved in evolutionary genetics by DNA sequencing and phylogenetic network methods, respectively. We now adapt these tools to linguistics, and analyze Indo-European language data, focusing on Celtic and in particular on the ancient Celtic language of Gaul (modern France), by using bilingual GaulishLatin inscriptions. Our phylogenetic network reveals an early split of Celtic within Indo-European. Interestingly, the next branching event separates Gaulish (Continental Celtic) from the British (Insular Celtic) languages, with Insular Celtic subsequently splitting into Brythonic (Welsh, Breton) and Goidelic (Irish and Scottish Gaelic). Taken together, the network thus suggests that the Celtic language arrived in the British Isles as a single wave (and then differentiated locally), rather than in the traditional two-wave scenario (P-Celtic to Britain and Q-Celtic to Ireland). The phylogenetic network furthermore permits the estimation of time in analogy to genetics, and we obtain tentative dates for Indo-European at 8100 BC 1,900 years, and for the arrival of Celtic in Britain at 3200 BC 1,500 years. The phylogenetic method is easily executed by hand and promises to be an informative approach for many problems in historical linguistics.

Proper citation: Fluxus (RRID:SCR_008517) Copy   

•   Source: SciCrunch Registry

http://www.daylight.com/dayhtml/doc/theory/

Daylight provides enterprise-level cheminformatics software technologies to life science companies. Our superior chemistry, high performance, and open architecture have earned Daylight a reputation for delivering the state-of-the-art in chemical information processing since 1987. Daylight Chemical Information Systems, Inc. is a privately held company with corporate offices in Aliso Viejo, CA and research offices in Santa Fe, NM and Cambridge, England. Support At Daylight, support means a wide array of services that are designed to empower users to make the most of Daylight software. We offer detailed administration documentation and guides through this website. Our User Group Meetings allow in-depth exploration of our technology. And, of course, our support staff is available to assist you whenever the need occurs. Download - Downloading current releases as well as contributed code, system requirements, installation directions, and release information Reference Guides - List of available documentation such as programming guides and user manuals. Cheminformatics - List of additional general resources including introductory materials, theory manual, tutorials and user meeting archives. Sponsor. Daylight

Proper citation: Daylight (RRID:SCR_008474) Copy   

•   Source: SciCrunch Registry

http://helixweb.nih.gov/dnaworks

DNAWorks automates the design of oligonucleotides for gene synthesis by PCR-based methods. The availability of sequences of entire genomes has dramatically increased the number of protein targets, many of which will need to be overexpressed in cells other than the original source of DNA. Gene synthesis often provides a fast and economically efficient approach. The synthetic gene can be optimized for expression and constructed for easy mutational manipulation without regard to the parent genome. DNAWorks accesses a computer program that automates the design of oligonucleotides for gene synthesis. The website provides forms for simple input information, i.e. amino acid sequence of the target protein and melting temperature (needed for the gene assembly) of synthetic oligonucleotides. The program outputs a series of oligonucleotide sequences with codons optimized for expression in an organism of choice. Those oligonucleotides are characterized by highly homogeneous melting temperatures and a minimized tendency for hairpin formation. The approach presented here simplifies the production of proteins from a wide variety of organisms for genomics-based studies.

Proper citation: DNAWorks at Helix Systems (RRID:SCR_008470) Copy   

•   Source: SciCrunch Registry

http://www.ebtox.org

This website is an invitation. an invitation to join scientists and stakeholders in an effort to review the scientific basis of traditional toxicological risk assessment, to provide the toxicological community with the tools it needs to efficiently and transparently judge risks of a diversifying nature and to make toxicology thus fit to meet the challenges of the 21st century. an invitation to participate in the inception and continuous implementation of a new movement in toxicology that aims at adopting an evidence-based approach. an invitation to help bridging the gap between modern toxicological science and risk assessment in order to exploit the wealth of information from new technologies in modern life sciences & toxicological research and to arrive at informed, transparent, judicious and conscientious decisions made on the basis of all evidence available. an invitation to develop a framework that allows combining precious expert insight grown over years of practical experience with structured approaches in basic science, in method assessment and in decision-making. Such evidence-based toxicology might help to make the best possible use of all sources of evidence in an efficient, productive, reliable, acceptable and transparent manner. Why do we need evidence-based toxicology (EBT)? Toxicology and the delivery of effective safety assessment critically relies on concepts and understanding generated by basic scientific research and must therefore adapt constantly to advances in knowledge. However, particularly from the perspective of regulatory toxicology, some of the assessment paradigms and methodologies were established decades ago and have changed little in response to scientific progress. At the same time, changes in our understanding of human disease, changes in the types of product now requiring safety assessment, and changes in the legislative landscape and public expectations pose significant challenges for industry, academia and regulators alike. It is necessary to challenge the status quo and ensure that as a matter of course best scientific practice and technical sophistication is reflected in safety assessment practices such that current and future challenges can be met. It is important therefore to ensure that structures are available that will encourage, facilitate and support a process of critical appraisal and renewal of the toxicological repertoire available for safety assessment. Part of this process is to embrace evidence-based toxicology such that the best possible scientific evidence is applied to judge product safety and likely risks to human health.

Proper citation: Evidence Based Toxicology (RRID:SCR_008507) Copy   

•   Source: SciCrunch Registry

http://www.biodas.org

The Distributed Annotation System (DAS) defines a communication protocol used to exchange annotations on genomic or protein sequences. It is motivated by the idea that such annotations should not be provided by single centralized databases, but should instead be spread over multiple sites. Data distribution, performed by DAS servers, is separated from visualization, which is done by DAS clients. The advantages of this system are that control over the data is retained by data providers, data is freed from the constraints of specific organisations and the normal issues of release cycles, API updates and data duplication are avoided. DAS is a client-server system in which a single client integrates information from multiple servers. It allows a single machine to gather up sequence annotation information from multiple distant web sites, collate the information, and display it to the user in a single view. Little coordination is needed among the various information providers. DAS is heavily used in the genome bioinformatics community. Over the last years we have also seen growing acceptance in the protein sequence and structure communities. A DAS-enabled website or application can aggregate complex and high-volume data from external providers in an efficient manner. For the biologist, this means the ability to plug in the latest data, possibly including a user''s own data. For the application developer, this means protection from data format changes and the ability to add new data with minimal development cost. Here are some examples of DAS-enabled applications or websites for end users: :- Dalliance Experimental Web/Javascript based Genome Viewer :- IGV Integrative Genome Viewer java based browser for many genomes :- Ensembl uses DAS to pull in genomic, gene and protein annotations. It also provides data via DAS. :- Gbrowse is a generic genome browser, and is both a consumer and provider of DAS. :- IGB is a desktop application for viewing genomic data. :- SPICE is an application for projecting protein annotations onto 3D structures. :- Dasty2 is a web-based viewer for protein annotations :- Jalview is a multiple alignment editor. :- PeppeR is a graphical viewer for 3D electron microscopy data. :- DASMI is an integration portal for protein interaction data. :- DASher is a Java-based viewer for protein annotations. :- EpiC presents structure-function summaries for antibody design. :- STRAP is a STRucture-based sequence Alignment Program. Hundreds of DAS servers are currently running worldwide, including those provided by the European Bioinformatics Institute, Ensembl, the Sanger Institute, UCSC, WormBase, FlyBase, TIGR, and UniProt. For a listing of all available DAS sources please visit the DasRegistry. Sponsors: The initial ideas for DAS were developed in conversations with LaDeana Hillier of the Washington University Genome Sequencing Center.

Proper citation: Distributed Annotation System (RRID:SCR_008427) Copy   

•   Source: SciCrunch Registry

http://seqpig.sourceforge.net/

A software library for Apache Pig for the distributed analysis of large sequencing datasets on Hadoop clusters.

Proper citation: SeqPig (RRID:SCR_008548) Copy   

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http://biosig.sourceforge.net/

Software library for processing of electroencephalogram (EEG) and other biomedical signals like electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), respiration, and so on. Biosig contains tools for quality control, artifact processing, time series analysis, feature extraction, classification and machine learning, and tools for statistical analysis. Many tools are able to handle data with missing values (statistics, time series analysis, machine learning). Another feature is that more then 40 different data formats are supported, and a number of converters for EEG,, ECG and polysomnography are provided. Biosig has been widely used for scientific research on EEG-based BraiN-Computer Interfaces (BCI), sleep research, and ECG and HRV analysis. It provides software interfaces several programming languages (C, C++, Matlab/Octave, Python), and it provides also an interactive viewing and scoring software for adding, and editing of annotations, markers and events.

Proper citation: BioSig: An Imaging Bioinformatics System for Phenotypic Analysis (RRID:SCR_008428) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://qneuro.rutgers.edu

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   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/cgi-bin/annotation/disease_vector/aedes_aegypti/blast_page.cgi, http://www.broadinstitute.org/cgi-bin/annotation/disease_vector/aedes_aegypti/blast_page.cgi

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry