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http://www.addgene.org/vector-database/

Vector database is a digital collection of vector backbones assembled from publications and commercially available sources. This is a free resource for the scientific community that is compiled by Addgene. Only the plasmids deposited at Addgene are available for purchase through this website.

Proper citation: Vector Database (RRID:SCR_005907) Copy   

•   Source: SciCrunch Registry

http://ki.se/ki/jsp/polopoly.jsp?d=29332&a=103566&l=en

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 3rd, 2023. The ICAP - Integrated analysis of prostate cancer study aims at identifying a set of biomarkers with high prognostic value for prostate cancer progression. These biomarkers will be used to customize treatment and to identify patients with high risk of recurrent disease. Sample types: * EDTA whole blood * DNA Number of sample donors: 505 (sample collection completed)

Proper citation: ICAP - Integrated analysis of prostate cancer (RRID:SCR_006035) Copy   

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http://ki.se/ki/jsp/polopoly.jsp?d=29332&a=103538&l=en

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. Libro-1 is a study with the overall aim to identify prognostic factors for breast cancer. The study comprise women in the Stockholm-Gotland region that were diagnosed with breast cancer between the years 2001-2008. Register data (tumor characteristics and treatment), lifestyle factors and blood samples have been collected from the participants.

Proper citation: LIBRO-1: Individualized prediction and prevention of breast cancer (RRID:SCR_006036) Copy   

•   Source: SciCrunch Registry

http://www.mousephenotype.org/

Center that produces knockout mice and carries out high-throughput phenotyping of each line in order to determine function of every gene in mouse genome. These mice will be preserved in repositories and made available to scientific community representing valuable resource for basic scientific research as well as generating new models for human diseases.

Proper citation: International Mouse Phenotyping Consortium (IMPC) (RRID:SCR_006158) Copy   

•   Source: SciCrunch Registry

http://www.procap.ki.se/procap_studie_info.htm

PROCAP is a study of the importance of lifestyle and genetic factors in the progression of localized cancer of the prostate. Our study hypothesis is that the likelihood of disease recurrence of prostate cancer is modified or determined by genetic variation in the human genome and/or lifestyle factors. To be able to test our hypothesis, we are using a large, population-based cohort of men with localized prostate cancer in Sweden, recruited in 1997-2002, from which detailed clinical information and data on progression already have been collected. From this cohort, we are collecting lifestyle data and blood samples from 8,500 men. If men with progressive prostate cancer could be identified on their genetic make-up, they could be given additional therapies targeted specifically at prostate cancer progression or monitored even more frequently so that progressions could be treated even earlier. If lifestyle factors are important, these results have an impact on recommendations given to men with newly diagnosed prostate cancer. In the study, we are asking the study persons to fill in an Internet-based questionnaire focusing on diet and physical activity and we ask them to leave 2 test tubes of blood at their local urologist/health care center. The pilot study has recently been completed and evaluated and the remaining 7,500 men in the cohort will be included during 2007 and 2008. So far, we have a response rate of approximately 85% on the blood samples. The response rate for the questionnaire is approximately 80% (both in the web based and paper based versions combined). Genotyping and analysis will begin in the fall of 2008. Sample types: * EDTA whole blood * Plasma * DNA Number of sample donors: 5492 (sample collection completed)

Proper citation: KI Biobank - PROCAP (RRID:SCR_006038) Copy   

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https://github.com/jiantao/Tangram

A C / C++ command line toolbox for structural variation (SV) detection that reports mobile element insertions (MEI). It takes advantage of both read-pair and split-read algorithms and is extremely fast and memory-efficient. Powered by the Bamtools API, it can call SV events on multiple BAM files (a population) simutaneously to increase the sensitivity on low-coverage dataset.

Proper citation: Tangram (RRID:SCR_006152) Copy   

•   Source: SciCrunch Registry

http://ki.se/en/meb/cancer-of-the-prostate-in-sweden-caps

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 3rd,2023. We have completed the recruitment of this population-based prostate cancer case-control study, one of the largest prostate cancer case-control study populations available so far. This study population was recruited in two phases. The inclusion and exclusion criteria were the same for the first (CAPS1) and second phase (CAPS2), except for the timeframe. In total, 3,030 cases and 1,960 controls participated in CAPS donating blood samples and answering a questionnaire during 2001-2003. In addition we have detailed clinical information on all 3,000 cases. With data generated from CAPS we have 25 published papers and 10 manuscripts since 2004. During 2006 we completed a record linkage to the Cause of Death Registry to determine the cause of death for all participants. We could conclude that 347 of the cases in CAPS had died of prostate cancer. The CAPS study has provided data to several new studies on markers on prostate cancer progression. Sample types: * EDTA whole blood * DNA Number of sample donors: 5015 (sample collection completed)

Proper citation: Cancer of the Prostate in Sweden (CAPS) (RRID:SCR_006033) Copy   

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http://ki.se/imm/cefalo-studien

Saliva taken from participants in a study investigating the association between environmental exposures and brain tumors in children aged 7-19 years and the interaction between these risk factors and genetic polymorphisms, which may confer susceptibility to effects of exogenous agents. Sample types: * Saliva Number of sample donors: 886 (sample collection completed)

Proper citation: KI Biobank - CEFALO (RRID:SCR_006034) Copy   

•   Source: SciCrunch Registry

http://www.cgat.org/~andreas/documentation/cgat/cgat.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 3, 2023. A collection of tools for the computational genomicist written in the python language to assist in the analysis of genome scale data from a range of standard file formats. The toolkit enables filtering, comparison, conversion, summarization and annotation of genomic intervals, gene sets and sequences. The tools can both be run from the Unix command line and installed into visual workflow builders, such as Galaxy. Please note that the tools are part of a larger code base also including genomics and NGS pipelines. Everyone who uses parts of the CGAT code collection is encouraged to contribute. Contributions can take many forms: bugreports, bugfixes, new scripts and pipelines, documentation, tests, etc. All contributions are welcome.

Proper citation: Computational Genomics Analysis Tools (RRID:SCR_006390) Copy   

•   Source: SciCrunch Registry

http://cmic.cs.ucl.ac.uk/mig/index.php?n=Tutorial.NODDImatlab

This MATLAB toolbox implements a data fitting routine for Neurite Orientation Dispersion and Density Imaging (NODDI). NODDI is a new diffusion MRI technique for imaging brain tissue microstructure. Compared to DTI, it has the advantage of providing measures of tissue microstructure that are much more direct and hence more specific. It achieves this by adopting the model-based strategy which relates the signals from diffusion MRI to geometric models of tissue microstructure. In contrast to typical model-based techniques, NODDI is much more clinically feasible and can be acquired on standard MR scanners with an imaging time comparable to DTI.

Proper citation: NODDI Matlab Toolbox (RRID:SCR_006826) Copy   

•   Source: SciCrunch Registry

http://lasurvey.rand.org/

A dataset of a panel study of a representative sample of all neighborhoods and households in Los Angeles County, with poor neighborhoods and families with children oversampled, for investigating the social and economic determinants of health and race and ethnic disparities. The study follows neighborhoods over time, as well as children and families. Two waves have been conducted to date, in 2000-2001 (L.A.FANS 1) and again beginning in 2006 through early 2009 (L.A. FANS 2). L.A.FANS-2 will significantly enhance the utility of the L.A.FANS data for studies of adult health disparities by: 1) Replicating self-reported health measures from L.A.FANS-1 and collecting new self-reports on treatment, health behaviors, functional limitations, quality and quantity of sleep, anxiety, health status vignettes, and changes in health status since the first interview; 2) Collecting physiological markers of disease and health status, including diabetes, hypertension, obesity, lung function, immune function, and cardiovascular disease; and 3) Expanding the data collected on adults'' work conditions, stressful experiences, and social ties. Wherever possible, L.A.FANS uses well-tested questions or sections from national surveys, such as the Health and Retirement Study (HRS), Panel Study of Income Dynamics (PSID), National Longitudinal Surveys (NLS), and National Health Interview Survey (NHIS), and other urban surveys, such as the Project on Human Development in Chicago Neighborhoods, to facilitate comparisons. Data Availability: Public use data, study design, and questionnaire content from L.A.FANS are available for downloading. Researchers can also apply for a restricted use version of the L.A.FANS-1 data that contain considerable contextual and geographically-referenced information. Application procedures are described at the project Website. L.A.FANS-2 fieldwork was completed at the end of 2008. The PIs anticipate L.A.FANS-2 public use data will be released in summer 2009. * Dates of Study: 2000-2008 * Study Features: Longitudinal, Minority Oversamples, Anthropometric Measures, Biospecimens * Sample Size: ** 2000-1: 2,548 (L.A.FANS 1) ** 2006-8: ~3,600 (L.A.FANS 2) Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00172

Proper citation: Los Angeles Family and Neighborhood Survey (RRID:SCR_008923) Copy   

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http://cedar.genetics.soton.ac.uk/pub/PROGRAMS/BETA

Software application for non-parametric linkage analysis using allele sharing in sib pairs (entry from Genetic Analysis Software)

Proper citation: BETA (RRID:SCR_007556) Copy   

•   Source: SciCrunch Registry

http://www.wesbarris.com/mapcreator/

Software application to create gene maps using either radiation hybrid data or linkage data (entry from Genetic Analysis Software)

Proper citation: MAPCREATOR (RRID:SCR_008001) Copy   

•   Source: SciCrunch Registry

http://www.mds.qmw.ac.uk/statgen/dcurtis/software.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documetned on May 12,2023. Software application (entry from Genetic Analysis Software)

Proper citation: FASTMAP (2) (RRID:SCR_008635) Copy   

•   Source: SciCrunch Registry

http://pubmatrix.grc.nia.nih.gov/

PubMatrix is a web-based tool that allows simple text based mining of the NCBI literature search service PubMed using any two lists of keywords terms, resulting in a frequency matrix of term co-occurrence. PubMatrix is a simple way to rapidly and systematically compare any list of terms against any other list of terms in PubMed. It reports back the frequency of co-occurrence between all pairwise comparisons between the two lists as a matrix table. Lists of terms can be anything; gene names, diseases, gene functions, authors, etc. The user can then quickly sort or browse the frequency matrix table to do individual searches independently. This allows the user to build up tables of word relationships in PubMed in the context of your experiments or your scientific interests. This is useful for analyzing combinatorial datasets, as found with multiplex experimental systems, such as cDNA microarrays, genomic, proteomic, or other multiplex comparisons. The PubMatrix database is an archive of previous searches on many topics. Sponsors: PubMatrix is supported by the National Institutes of Health.

Proper citation: PubMatrix (RRID:SCR_008236) Copy   

•   Source: SciCrunch Registry

http://pubcrawler.gen.tcd.ie/

PubCrawler is a free alerting service that scans daily updates to the NCBI Medline (PubMed) and GenBank databases. PubCrawler helps keeping scientists informed of the current contents of Medline and GenBank, by listing new database entries that match their research interests. The free PubCrawler web service has been operating for five years and so far has brought literature and sequence updates to over 22 000 users. It provides information on a personalized web page whenever new articles appear in PubMed or when new sequences are found in GenBank that are specific to customized queries. The server also acts as an automatic alerting system by sending out short notifications or emails with the latest updates as soon as they become available. PubCrawler searches the NCBI PubMed (Medline) and Entrez (GenBank) databases daily using search parameters (keywords, author names, etc.) specified by the user. There is no limit on the number of searches that can be carried out. Previous search hits are stored and only the newest PubMed or GenBank records are shown each day. The results are presented as an HTML Web page, similar to the results of an NCBI PubMed or Entrez query. This Web page can be located on our computer (the PubCrawler WWW-Service), on your computer (the stand-alone program), or you can receive it via e-mail (set this up using the PubCrawler WWW-Service). The Web page sorts the results into groups of PubMed/GenBank entries that are zero-days-old, 1-day-old, 2-days-old, etc., up to a user-specified age limit. Sponsors: Development of PubCrawler was supported by EMBnet

Proper citation: PubCrawler (RRID:SCR_008235) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/web/packages/stepwise/index.html

Software application that is a stepwise approach to identifying recombination breakpoints in a sequence alignment (entry from Genetic Analysis Software)

Proper citation: R/STEPWISE (RRID:SCR_007420) Copy   

•   Source: SciCrunch Registry

http://www.gaworkshop.org/

The Genetic Analysis Workshops (GAWs) are a collaborative effort among genetic epidemiologists to evaluate and compare statistical genetic methods. For each GAW, topics are chosen that are relevant to current analytical problems in genetic epidemiology, and sets of real or computer-simulated data are distributed to investigators worldwide. Results of analyses are discussed and compared at meetings held in even-numbered years. The GAWs began in 1982 were initially motivated by the development and publication of several new algorithms for statistical genetic analysis, as well as by reports in the literature in which different investigators, using different methods of analysis, had reached contradictory conclusions. The impetus was initially to determine the numerical accuracy of the algorithms, to examine the robustness of the methodologies to violations of assumptions, and finally, to compare the range of conclusions that could be drawn from a single set of data. The Workshops have evolved to include consideration of problems related to analyses of specific complex traits, but the focus has always been on analytical methods. The Workshops provide an opportunity for participants to interact in addressing methodological issues, to test novel methods on the same well-characterized data sets, to compare results and interpretations, and to discuss current problems in genetic analysis. The Workshop discussions are a forum for investigators who are evolving new methods of analysis as well as for those who wish to gain further experience with existing methods. The success of the Workshops is due at least in part to the focus on specific problems and data sets, the informality of sessions, and the requirement that everyone who attends must have made a contribution. Topics are chosen and a small group of organizers is selected by the GAW Advisory Committee. Data sets are assembled, and six or seven months before each GAW, a memo is sent to individuals on the GAW mailing list announcing the availability of the GAW data. Included with the memo is a short description of the data sets and a form for requesting data. The form contains a statement to be signed by any investigator requesting the data, acknowledging that the data are confidential and agreeing not to use them for any purpose other than the Genetic Analysis Workshop without written permission from the data provider(s). Data are distributed by the ftp or CD-ROM or, most recently, on the web, together with a more complete written description of the data sets. Investigators who wish to participate in GAW submit written contributions approximately 6-8 weeks before the Workshop. The GAW Advisory Committee reviews contributions for relevance to the GAW topics. Contributions are assembled and distributed to all participants approximately two weeks before the Workshop. Participation in the GAWs is limited to investigators who (1) submit results of their analyses for presentation at the Workshop, or (2) are data providers, invited speakers or discussants, or Workshop organizers. GAWs are held just before the meetings of the American Society of Human Genetics or the International Genetic Epidemiology Society, at a meeting site nearby. We choose a location that will encourage interaction among participants and permit an intense period of concentrated work. The proceedings of each GAW are published. Proceedings from GAW16 were published in part by Genetic Epidemiology 33(Suppl 1), S1-S110 (2009) and in part by Biomed Central (BMC Proceedings, Volume 3, Supplement 7, 2009). Sponsors: GAW is funded by the Southwest Foundation for Biomedical Research.

Proper citation: Genetic Analysis Workshop (RRID:SCR_008350) Copy   

•   Source: SciCrunch Registry

http://www.nhgri.nih.gov/DIR/IDRB/GASP/

Software tool for testing and investigating methods in statistical genetics by generating samples of family data based on user specified models. (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GASP (RRID:SCR_008703) Copy   

•   Source: SciCrunch Registry

http://www.mds.qmw.ac.uk/statgen/dcurtis/software.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5th,2023. Software application for TDT test on markers with more than two alleles using a logistic regression analysis. (entry from Genetic Analysis Software).

Proper citation: ETDT (RRID:SCR_007576) Copy   

•   Source: SciCrunch Registry