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http://sourceforge.net/projects/powermap/

Software tool specifically designed for neuroimaging data that implements theoretical power calculation algorithms based on non-central random field theory. It can also calculate power for statistical analyses with FDR (false discovery rate) corrections. This GUI (graphical user interface)-based tool enables neuroimaging researchers without advanced knowledge in imaging statistics to calculate power and sample size in the form of 3D images. This tool is currently under limited release for beta testing. At this time, only users that have been directed to this site by the PowerMap developers will receive support.

Proper citation: PowerMap (RRID:SCR_006721) Copy   

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http://p53.fr

The UMD TP53 Mutation Database is a novel web site exclusively dedicated to mutant TP53. The following datasets, analytical tools and software are available. * The TP53 UMD mutation database in human cancer (2012 release). This novel release (35,000 mutations, 3,600 publications) has been highly curated using an original and novel statistical procedure (See Edlung et al. PNAS 2012). * TP53MUTLOAD (MUTant Loss Of Activity Database), a novel database dedicated to detailed analysis of the properties of each TP53 mutant, ranging from transactivation to cell growth properties, change of conformation, localization or various gains of functions. The database contains more than 110,000 different entries. * TP53 Mut assessor, a novel stand-alone software available for both Windows and Mac users. Check your favorite TP53 mutants and get an instant identity card. Very useful to analyze any newly discovered TP53 mutants, as the software checks for every possible TP53 mutation. * MUT-TP53 2.0, an accurate and powerful tool that automatically manages p53 mutations and generate tables ready for publication, decreasing the risk of typing errors. MUT-TP53 2.0 also provides specific information for each TP53 mutation, allowing the user to assess the quality of the data. Up to 500 TP53 mutations can be managed simultaneously.

Proper citation: UMD p53 Mutation Database (RRID:SCR_006720) Copy   

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http://sourceforge.net/projects/samscope/

A lightweight SAM/BAM file viewer that makes visually exploring next generation sequencing data intuitive and maybe even fun! Quickly and easily generate aggregate statistics from SAM/BAM files like coverage, polarity, and minor allele frequencies, then scroll and explore freely with a simple mouse based interface. Multiple windows can be synchronized for careful comparison across multiple experiments.

Proper citation: Samscope (RRID:SCR_006715) Copy   

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http://btris.nih.gov

Provides NIH clinical investigators with access to identifiable data for the subjects on their own active protocols, while providing all NIH investigators with access to de-identified data across all protocols. BTRIS provides users with advanced search, filtering, and aggregation methods to create data sets to support ongoing studies and stimulate ideas for new research. BTRIS is two distinct but interrelated applications, BTRIS Data Access and BTRIS Preferences. * BTRIS Data Access is the data repository where principal investigators or their designee create reports on their active protocols with identified subject data. Reports include the IRB Inclusion Enrollment Report, demographics, patient lists, laboratory and microbiology results, vital signs, medication orders and administration, diagnoses, and radiology reports (with links to images in the CC PACS system). * BTRIS Preferences is a Web based application that allows principal investigators or their designees to verify subject enrollment in their protocol(s). This ensures that reports created in BTRIS Data Access include all subjects. It also allows the principal investigator to designate an alternate investigator from the protocol to manage subject enrollment and create reports in BTRIS Data Access. BTRIS contains subject data from CRIS/MIS (the Clinical Center Medical Information Systems) and research data from NIAID (Crimson), NIAAA, and NCI. Data are available from 1976 to the present.

Proper citation: BTRIS: NIH Biomedical Translational Research Information System (RRID:SCR_006838) Copy   

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http://www.proteinatlas.org/

Open access resource for human proteins. Used to search for specific genes or proteins or explore different resources, each focusing on particular aspect of the genome-wide analysis of the human proteins: Tissue, Brain, Single Cell, Subcellular, Cancer, Blood, Cell line, Structure and Interaction. Swedish-based program to map all human proteins in cells, tissues, and organs using integration of various omics technologies, including antibody-based imaging, mass spectrometry-based proteomics, transcriptomics, and systems biology. All the data in the knowledge resource is open access to allow scientists both in academia and industry to freely access the data for exploration of the human proteome.

Proper citation: The Human Protein Atlas (RRID:SCR_006710) Copy   

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http://bioconductor.org/packages/2.12/bioc/html/seqbias.html

Software package that implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence.

Proper citation: seqbias (RRID:SCR_006832) Copy   

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http://www.ncbi.nlm.nih.gov/books/NBK5330/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.Searchable book regarding molecular imaging and contrast agents (under development, in clinical trials or commercially available for medical applications) that have in vivo data (animal or human) published in peer-reviewed scientific journals prior to June 30 of 2013. 1444 agents are currently listed and there will be no more updates. Also available is a downloadable list of FDA approved contrast agents (Latest update: January 2013) and a Molecular Imaging Probes and Contrast Agents List (MIP & CA List) created by the MICAD staff by screening the PubMed / MedLine databases and other appropriate sources of such information. Only agents used in animal or human studies yielding in vivo data were selected for inclusion in the list. The list is by no means considered complete. No one imaging modality has been given preference over the others and the omission of any agent(s) or the introduction of any errors in the list is purely unintentional. The MIP & CA List is subject to the same copyright and disclaimers as the rest of the MICAD content. The database includes, but is not limited to, agents developed for positron emission tomography (PET), single photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), ultrasound (US), computed tomography (CT), optical imaging, planar radiography, and planar gamma imaging. The information on each agent is summarized in a book chapter format containing several sections such as Background, Synthesis, in vitro studies, Animal Studies (with sub-sections: rodents, other non-human primate animals, and human primates), Human Studies, and References. In addition, the references are linked to PubMed for retrieval of the publication abstract. Also, each chapter contains links to resources at the National Center for Biotechnology Information (NCBI) and other relevant databases regarding the target of the imaging probe or contrast agent.

Proper citation: Molecular Imaging and Contrast Agent Database (RRID:SCR_006712) Copy   

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http://www.bioconductor.org/packages/2.12/bioc/html/DeconRNASeq.html

An R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It modeled expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles.

Proper citation: DeconRNASeq (RRID:SCR_006713) Copy   

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http://rankprop.gs.washington.edu/svm-fold/

This web server makes predictions of family, superfamily and fold level classifications of proteins based on the Structural Classification of Proteins (SCOP) hierarchy using the Support Vector Machine (SVM) learning algorithm. SVM-FOLD detects subtle protein sequence similarities by learning from all available annotated proteins, as well as utilizing potential hits as identified by PSI-BLAST. Predictions of classes of proteins that do not have any known example with a significant pairwise PSI-BLAST E-value can still be found using SVMs.

Proper citation: SVM-fold: Protein Fold Prediction (RRID:SCR_006834) Copy   

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http://www.upr.edu/

Main public university system in the U.S. Commonwealth of Puerto Rico and a government-owned corporation.The oldest and largest higher learning institution in the Caribbean.

Proper citation: University of Puerto Rico; Puerto Rico; USA (RRID:SCR_006707) Copy   

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http://www.1000genomes.org/

International collaboration producing an extensive public catalog of human genetic variation, including SNPs and structural variants, and their haplotype contexts, in an effort to provide a foundation for investigating the relationship between genotype and phenotype. The genomes of about 2500 unidentified people from about 25 populations around the world were sequenced using next-generation sequencing technologies. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared. The results of the study are freely and publicly accessible to researchers worldwide. The consortium identified the following populations whose DNA will be sequenced: Yoruba in Ibadan, Nigeria; Japanese in Tokyo; Chinese in Beijing; Utah residents with ancestry from northern and western Europe; Luhya in Webuye, Kenya; Maasai in Kinyawa, Kenya; Toscani in Italy; Gujarati Indians in Houston; Chinese in metropolitan Denver; people of Mexican ancestry in Los Angeles; and people of African ancestry in the southwestern United States. The goal Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing. All samples from the 1000 genomes are available as lymphoblastoid cell lines (LCLs) and LCL derived DNA from the Coriell Cell Repository as part of the NHGRI Catalog. The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via their mirrored ftp sites. ftp://ftp.1000genomes.ebi.ac.uk ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes

Proper citation: 1000 Genomes: A Deep Catalog of Human Genetic Variation (RRID:SCR_006828) Copy   

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http://www.ecu.edu/pharmacology/

Department of Pharmacology and Toxicology in Brody School of Medicine at East Carolina University prepares students to begin career related to studying biological actions of chemicals. Department offers Ph.D. program and combined M.D./Ph.D. program available through School of Medicine.

Proper citation: East Carolina University Department of Pharmacology and Toxicology (RRID:SCR_006824) Copy   

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http://www.bioconductor.org/packages/2.12/bioc/html/methVisual.html

Software package that allows the visualization of DNA methylation data after bisulfite sequencing.

Proper citation: methVisual (RRID:SCR_006705) Copy   

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http://www.unavco.org/

A non-profit university-governed consortium that facilitates geoscience research and education using geodesy. It rovides access to and submission of Geodetic GPS / GNSS Data, Geodetic Imaging Data, Strain and Seismic Borehole Data, and Meteorological Data. Data access web services/API provides the ability to use a command line interface to query metadata and obtain URLs to data and products. UNAVCO also provides a variety of software, including web applications, and desktop utilities for scientists, instructors, students, and others. Web-based data visualization and mapping tools provide users with the ability to view postprocessed data while web-based geodetic utilities provide ancillary information. Downloadable stand-alone software utilities include applications for configuring instruments, managing data collection, download and transfer, and performing computations on the raw data, e.g., data pre-processing or processing. The UNAVCO Facility in Boulder, Colorado is the primary operational activity of UNAVCO and exists to support university and other research investigators in their use of geophysical sensor technology for Earth sciences research. The Facility performs this task in part by archiving GNSS/GPS data and data products for current and future applications. Other data types that scientists use for Earth deformation studies are also held in the UNAVCO Archive collections. UNAVCO operates a community Archive, which provides long-term secure storage and easy retrieval of GNSS data, strain data, various derived products and related metadata. The Archive primarily stores high-precision geodetic data used for research purposes, collected under National Science Foundation and NASA sponsored projects. UNAVCO provides many learning opportunities including: Short Courses and Workshops, Educational Resources, RESESS Research Student Internships, and Technical Training.

Proper citation: UNAVCO (RRID:SCR_006706) Copy   

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http://www.pediatricmri.nih.gov/

Data sets of clinical / behavioral and image data are available for download by qualified researchers from a seven year, multi-site, longitudinal study using magnetic resonance technologies to study brain maturation in healthy, typically-developing infants, children, and adolescents and to correlate brain development with cognitive and behavioral development. The information obtained in this study is expected to provide essential data for understanding the course of normal brain development as a basis for understanding atypical brain development associated with a variety of developmental, neurological, and neuropsychiatric disorders affecting children and adults. This study enrolled over 500 children, ranging from infancy to young adulthood. The goal was to study each participant at least three times over the course of the project at one of six Pediatric Centers across the United States. Brain MR and clinical/behavioral data have been compiled and analyzed at a Data Coordinating Center and Clinical Coordinating Center. Additionally, MR spectroscopy and DTI data are being analyzed. The study was organized around two objectives corresponding to two age ranges at the time of enrollment, each with its own protocols. * Objective 1 enrolled children ages 4 years, 6 months through 18 years (total N = 433). This sample was recruited across the six Pediatric Study Centers using community based sampling to reflect the demographics of the United States in terms of income, race, and ethnicity. The subjects were studied with both imaging and clinical/behavioral measures at two year intervals for three time points. * Objective 2 enrolled newborns, infants, toddlers, and preschoolers from birth through 4 years, 5 months, who were studied three or more times at two Pediatric Study Centers at intervals ranging from three months for the youngest subjects to one year as the children approach the Objective 1 age range. Both imaging and clinical/behavioral measures were collected at each time point. Participant recruitment used community based sampling that included hospital venues (e.g., maternity wards and nurseries, satellite physician offices, and well-child clinics), community organizations (e.g., day-care centers, schools, and churches), and siblings of children participating in other research at the Pediatric Study Centers. At timepoint 1, of those enrolled, 114 children had T1 scans that passed quality control checks. Staged data release plan: The first data release included structural MR images and clinical/behavioral data from the first assessments, Visit 1, for Objective 1. A second data release included structural MRI and clinical/behavioral data from the second visit for Objective 1. A third data release included structural MRI data for both Objective 1 and 2 and all time points, as well as preliminary spectroscopy data. A fourth data release added cortical thickness, gyrification and cortical surface data. Yet to be released are longitudinally registered anatomic MRI data and diffusion tensor data. A collaborative effort among the participating centers and NIH resulted in age-appropriate MR protocols and clinical/behavioral batteries of instruments. A summary of this protocol is available as a Protocol release document. Details of the project, such as study design, rationale, recruitment, instrument battery, MRI acquisition details, and quality controls can be found in the study protocol. Also available are the MRI procedure manual and Clinical/Behavioral procedure manuals for Objective 1 and Objective 2.

Proper citation: NIH MRI Study of Normal Brain Development (RRID:SCR_003394) Copy   

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http://www.hopkinsmedicine.org/pharmacology/

The Department of Pharmacology and Molecular Sciences is proud of its history. Founded in 1893 by John J. Abel, the "Father of Pharmacology" in the United States, the Johns Hopkins Pharmacology Department's advances over the years have included the first crystals of insulin, the first measurement of a drug level in a human, discovery of both the insulin and opiate receptors, and discovery of a cancer preventive principle in broccoli. Many of these and other key contributions have been made by students pursuing advanced degrees in our Department. We are also proud of our track record in student training. Many of our graduates have gone on to become academic and industrial leaders in biomedical research throughout the country and world. Not resting on our laurels, we are continuing to maintain our strong commitment to creative scholarship and education. Additionally, there is an Anti-Cancer Drug Development Training Program for predoctoral and postdoctoral students as well ad other Postdoctoral research opportunities. Each of our faculty is engaged in cutting-edge research spanning many areas including: chemical biology, immunology, virology, cancer, and neuroscience. We believe the opportunities for discovering new drug targets and developing novel therapeutics have never been brighter and will continue to be lustrous for the century ahead. The Johns Hopkins University and School of Medicine provide an excellent scientific environment, with a friendly and supportive atmosphere, filled with energetic students, faculty, fellows, and staff.

Proper citation: Johns Hopkins University Pharmacology (RRID:SCR_003391) Copy   

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https://bioportal.bioontology.org/ontologies/NEMO/?p=summary

Ontology that describes classes of event-related brain potentials (ERP) and their properties, including spatial, temporal, and functional (cognitive / behavioral) attributes, and data-level attributes (acquisition and analysis parameters). Its aim is to support data sharing, logic-based queries and mapping/integration of patterns across data from different labs, experiment paradigms, and modalities (EEG/MEG).

Proper citation: NEMO Ontology (RRID:SCR_003386) Copy   

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http://sagebase.org/

Non-profit biomedical research organization developing predictors of disease and accelerating health research through creation of open systems, incentives, and standards. Formed to coordinate and link academic and commercial biomedical researchers through Commons that represents new paradigm for genomics intellectual property, researcher cooperation, and contributor evolved resources.

Proper citation: Sage Bionetworks (RRID:SCR_003384) Copy   

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http://www.bioconductor.org/packages/release/bioc/html/NormqPCR.html

Software package providing functions for the selection of optimal reference genes and the normalization of real-time quantitative PCR data.

Proper citation: NormqPCR (RRID:SCR_003388) Copy   

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http://signalml.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023.An XML-based language designed for metadescription of formats, used for digital storage of biomedical time series. Using SignalML, information on the structure of binary data files can be simply and efficiently coded. Once written, this information can be used by any software, which - owing to this metadescription - can read data files in the original format. This eliminates the need for conversions and duplication of data. signalml.org provides the following resources for interchange of relevant information and ideas: * SignalML wiki * Newsgroup / mailing list The main current software project is Svarog - a SignalML-compliant signal viewer, annotator, analyzer and (future) recorder. Svarog is written in Java and is currently best fitted for display of EEG and MEG signals. Also open platform for implementing advanced signal processing methods in user-friendly environment, at the moment interfacs for Java code, standalone executables and Matlab code via Matlab Builder for Java.

Proper citation: signalml.org (RRID:SCR_003383) Copy   

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