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http://inparanoid.sbc.su.se/cgi-bin/index.cgi

Collection of pairwise comparisons between 100 whole genomes generated by a fully automatic method for finding orthologs and in-paralogs between TWO species. Ortholog clusters in the InParanoid are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for in-paralogs. The original data sets can be downloaded.

Proper citation: InParanoid: Eukaryotic Ortholog Groups (RRID:SCR_006801) Copy   

•   Source: SciCrunch Registry

http://theneuronetwork.com/

Professional networking site dedicated to those working, studying, or simply interested in the science of the brain. Those with personal and professional interests in neuroscience, neurology, psychology, and all related areas -- those studying neural activities in invertebrates up to the human brain, including brain-like systems and networks are welcome. The NeuroNetwork was built to allow students and researchers to find and interact with each other in a professional environment. You may create searchable profiles with your research interests; start a blog; upload poster presentations and videos; post and search job listings; form a virtual group based on your interests, geographical location, etc.; and view, post, and RSVP to upcoming meetings. And don''t just create a profile for yourself -- create one for your organization, institute, society, or even your lab as well.

Proper citation: NeuroNetwork (RRID:SCR_006761) Copy   

•   Source: SciCrunch Registry

https://www.fludb.org/brc/home.spg?decorator=influenza

The Influenza Research Database (IRD) serves as a public repository and analysis platform for flu sequence, experiment, surveillance and related data.

Proper citation: Influenza Research Database (IRD) (RRID:SCR_006641) Copy   

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http://scicrunch.org

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 27, 2019.

Database for those interested in the consequences of Factor VIII genetic variation at the DNA and protein level, it provides access to data on the molecular pathology of haemophilia A. The database presents a review of the structure and function of factor VIII and the molecular genetics of haemophilia A, a real time update of the biostatistics of each parameter in the database, a molecular model of the A1, A2 and A3 domains of the factor VIII protein (based on the crystal structure of caeruloplasmin) and a bulletin board for discussion of issues in the molecular biology of factor VIII. The database is completely updated with easy submission of point mutations, deletions and insertions via e-mail of custom-designed forms. A methods section devoted to mutation detection is available, highlighting issues such as choice of technique and PCR primer sequences. The FVIII structure section now includes a download of a FVIII A domain homology model in Protein Data Bank format and a multiple alignment of the FVIII amino-acid sequences from four species (human, murine, porcine and canine) in addition to the virtual reality simulations, secondary structural data and FVIII animation already available. Finally, to aid navigation across this site, a clickable roadmap of the main features provides easy access to the page desired. Their intention is that continued development and updating of the site shall provide workers in the fields of molecular and structural biology with a one-stop resource site to facilitate FVIII research and education. To submit your mutants to the Haemophilia A Mutation Database email the details. (Refer to Submission Guidelines)

Proper citation: HAMSTeRS - The Haemophilia A Mutation Structure Test and Resource Site (RRID:SCR_006883) Copy   

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https://genorm.cmgg.be/

Software to determine most stable reference (housekeeping) genes from set of tested candidate reference genes in given sample panel. From this, gene expression normalization factor can be calculated for each sample based geometric mean of user-defined number of reference genes.

Proper citation: geNORM (RRID:SCR_006763) Copy   

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http://sourceforge.net/p/fastsemsim/home/Home/

A package that implements several semantic similarity measures. It is both a library and an end-user application, featuring an intuitive graphical user interface (GUI). It has been implemented with the aim of being fast, expandable, and easy to use. It allows the user to work with the most updated version of GO database and customizable annotation corpora. It provides a set of logically-organized classes that can be easily exploited to both integrate semantic similarity into different analysis pipelines and extend the library with new measures. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FastSemSim (RRID:SCR_006919) Copy   

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http://rdp.cme.msu.edu

A database which provides ribosome related data services to the scientific community, including online data analysis, rRNA derived phylogenetic trees, and aligned and annotated rRNA sequences. It specifically contains information on quality-controlled, aligned and annotated bacterial and archaean 16S rRNA sequences, fungal 28S rRNA sequences, and a suite of analysis tools for the scientific community. Most of the RDP tools are now available as open source packages for users to incorporate in their local workflow.

Proper citation: Ribosomal Database Project (RRID:SCR_006633) Copy   

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http://diabetes.niddk.nih.gov/dm/pubs/america/

A compilation and assessment of epidemiologic, public health, and clinical data on diabetes and its complications in the United States. Published by the National Diabetes Data Group of the National Institute of Diabetes and Digestive and Kidney Diseases, the book contains 36 chapters organized in five areas: * the descriptive epidemiology of diabetes in the United States based on national surveys and community-based studies, including prevalence, incidence, sociodemographic and metabolic characteristics, risk factors for developing diabetes, and mortality * the myriad complications that affect patients with diabetes * characteristics of therapy and medical care for diabetes * economic aspects, including health insurance and health care costs * diabetes in special populations, including African Americans, Hispanics, Asian and Pacific Islanders, Native Americans, and pregnant women. Diabetes in America, 2nd Edition, has been designed to serve as a reliable scientific resource for assessing the scope and impact of diabetes and its complications, determining health policy and priorities in diabetes, and identifying areas of need in research. The intended audience includes health policy makers at the local and Federal levels who need a sound quantitative base of knowledge to use in decision making; clinicians who need to know the probability that their patients will develop diabetes and the prognosis of the disease for complications and premature mortality; persons with diabetes and their families who need sound information on which to make decisions about their life with diabetes; and the research community which needs to identify areas where important scientific knowledge is lacking.

Proper citation: Diabetes in America (RRID:SCR_006754) Copy   

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http://ligand-expo.rutgers.edu/

An integrated data resource for finding chemical and structural information about small molecules bound to proteins and nucleic acids within the structure entries of the Protein Data Bank. Tools are provided to search the PDB dictionary for chemical components, to identify structure entries containing particular small molecules, and to download the 3D structures of the small molecule components in the PDB entry. A sketch tool is also provided for building new chemical definitions from reported PDB chemical components.

Proper citation: Ligand Expo (RRID:SCR_006636) Copy   

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http://www.informatics.jax.org/mgihome/GXD/gxdgen.shtml

A unified resource that combines text-based and 3D graphical methods to store, display, and analyze mouse developmental gene expression information. The Mouse Gene Expression Information Resource resource will integrate the following components: * Gene Expression Database (GXD) - Integrates different types of expression data and provides links to many other resources to place the data into the larger biological and analytical context. * Anatomy Database - Provides the standard nomenclature for developmental anatomy. * 3D Atlas / Graphical Gene Expression Database - Provides a high-resolution digital representation of mouse anatomy reconstructed from serial sections of single embryos at each representative developmental stage enabling 3D graphical display and analysis of in situ expression data.

Proper citation: Mouse Genome Informatics: The Mouse Gene Expression Information Resource Project (RRID:SCR_006630) Copy   

•   Source: SciCrunch Registry

http://www.genomesunzipped.org/

A group blog providing expert, independent commentary on the personal genomics industry. The goal of the project is to provide genetic testing consumers with independent and informed analysis of developments in the field of genetics and the genetic testing industry. Members of Genomes Unzipped include active researchers in various fields of genetics, as well as specialists in the legal and public health issues surrounding new genomic technologies. Many of us have also been extensively involved in public communication about genetics. Members of the group have had their DNA tested with a variety of products. We have released all of these genetic data openly to the public, both as raw data and in a custom genome browser. As the project proceeds we plan to obtain more genetic tests ����?? up to and including whole genome sequencing ����?? and to continue to release these data to the world. The group is also performing analyses of our own raw genetic data to illustrate fundamental concepts in genetics, using software written both by group members and other collaborators; and we����??ll be releasing the code for that software in our new code repository. As the project expands, we����??ll be looking to add data from other volunteers to the project, as well as to collaborate with other ����??genome hackers����?? on the development of new tools for exploring genetic data.

Proper citation: Genomes Unzipped (RRID:SCR_006870) Copy   

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http://www.physionet.org/physiotools/

Growing library of software for physiologic signal processing and analysis, detection of physiologically significant events using both classical techniques and novel methods based on statistical physics and nonlinear dynamics, interactive display and characterization of signals, creation of new databases, simulation of physiologic and other signals, quantitative evaluation and comparison of analysis methods, and analysis of nonequilibrium and nonstationary processes. A unifying theme of the research projects that contribute software to PhysioToolkit is the extraction of hidden information from biomedical signals, information that may have diagnostic or prognostic value in medicine, or explanatory or predictive power in basic research. Contributions of software to PhysioToolkit are welcome, http://physionet.org/guidelines.shtml#software-contributions

Proper citation: PhysioToolkit (RRID:SCR_006868) Copy   

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http://seer.cancer.gov/

SEER collects cancer incidence data from population-based cancer registries covering approximately 47.9 percent of the U.S. population. The SEER registries collect data on patient demographics, primary tumor site, tumor morphology, stage at diagnosis, and first course of treatment, and they follow up with patients for vital status.There are two data products available: SEER Research and SEER Research Plus. This was motivated because of concerns about the increasing risk of re-identifiability of individuals. The Research Plus databases require more rigorous process for access that includes user authentication through Institutional Account or multiple-step request process for Non-Institutional users.

Proper citation: Surveillance Epidemiology and End Results (RRID:SCR_006902) Copy   

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http://www.molecularimaging.vcu.edu/

The Center for Molecular Imaging integrates molecular imaging and molecular medicine with systems biology approaches to understand disease complexity, promising to provide predictive, preventative and personalized medicine that will transform health care. The multi-modality molecular imaging program is composed of individual but overlapping research themes with specific projects under each theme. As all projects are underpinned by methodology development in the chemistry, biology, physics and bioengineering of imaging, there is always a strong overlap and cross feed in terms of methods and assays. The research themes include: * Biomarker pharmacodynamic imaging of metabolism, proliferation, cell death and vascular perfusion * Molecular genetic imaging in developing multi-modal reporter gene-based probes * Molecular imaging of angiogenesis and its relation to tumor hypoxia * Multi-modal nanoparticle probes for drug delivery and molecular imaging, including cell and immune-based therapies * Radiolabeled drug pharmacokinetics and studies of drug discovery and structural biology using molecular imaging strategies * Development of nanotechnology molecular imaging systems * Imaging proteomic mass spectrometry * Molecular imaging and molecular pathology diagnostic research The Center for Molecular Imaging fosters collaborative research, bringing together the advances being made in technology-driven research such as bioengineering, chemical genomics and nanotechnology with biomedical research groups studying cell and molecular biology and radiobiology, biologically targeted therapeutics, immuno-based mechanisms, and drug and biomarker discovery. The aim is to develop and validate multi-modality molecular imaging tools that will facilitate the advancement of translational medicine and clinical science research in oncology and neuroscience as well as other clinical research areas. The center is equipped with laboratories for radiochemistry, analytical chemistry, molecular biology, nanotechnology and animal imaging, as well as a dedicated facility for translational clinical imaging. Imaging technologies within the center include Positron Emission Tomography/Computed Tomography (preclinical and clinical), Single Photon Emission Computed Tomography, optical imaging, autoradiography and multi-modality nanotechnology imaging. Molecular probe developments underpin the biological and biomedical research conducted using the above modalities.

Proper citation: Virginia Commonwealth University Medical Center, Center for Molecular Imaging (RRID:SCR_006869) Copy   

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http://dally.nimh.nih.gov/matoff/matoff.html

An interactive analysis program that searches neurophysiological data and plots the results. MatOFF was developed especially for dealing with the complexities common to behavioral neurophysiological experiments. It runs under Windows 2000 or XP and relies on MATLAB version R11.1 (or above) for all operations. MatOFF searches a data file to locate and plot epochs (trials) of special interest to the investigator. Appropriate input data files have time-stamped event codes, usually including neuron action potential firing events (spikes), and digitized analog data. The user specifies a list of event code numbers that uniquely identify a sequence of events. MatOFF uses this sequence to search the raw data file, select the epochs that meet the criteria, time-shift the trials to align them on a common event, order the epochs based on user-selected criteria, and plot the results based on a collection of page formatting specifications. MatOFF will also save extracted data and some statistics to disk. Features: * Powerful, interactive searching tools for locating relevant experimental events * Compatible with Cortex data acquisition program * Compatible with Plexon data acquisition system * Flexible, publication-quality graphical display and printing * Comprehensive scripting language * Supports learning and other dynamic behavior * Integrated interface to MATLAB functions * Automatic alignment of trial data and generation of histograms * Large variety of options for selecting and ordering trial data * Descriptive and non-parametric statistics * XY analog displays * Data export with flexible format control * Up to 72 plots per page * Display templates can be saved and reloaded * Free for public or private use * Adaptable to almost any data file format

Proper citation: MatOFF (RRID:SCR_006821) Copy   

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http://sourceforge.net/projects/bmdexpress/

Bioinformatics tool used to analyze microarray dose-response data. The analysis provides benchmark dose estimates at which different cellular processes are altered in toxicogenomic experiments.

Proper citation: BMDExpress (RRID:SCR_006823) Copy   

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http://www.peptideatlas.org

Multi-organism, publicly accessible compendium of peptides identified in a large set of tandem mass spectrometry proteomics experiments. Mass spectrometer output files are collected for human, mouse, yeast, and several other organisms, and searched using the latest search engines and protein sequences. All results of sequence and spectral library searching are subsequently processed through the Trans Proteomic Pipeline to derive a probability of correct identification for all results in a uniform manner to insure a high quality database, along with false discovery rates at the whole atlas level. The raw data, search results, and full builds can be downloaded for other uses. All results of sequence searching are processed through PeptideProphet to derive a probability of correct identification for all results in a uniform manner ensuring a high quality database. All peptides are mapped to Ensembl and can be viewed as custom tracks on the Ensembl genome browser. The long term goal of the project is full annotation of eukaryotic genomes through a thorough validation of expressed proteins. The PeptideAtlas provides a method and a framework to accommodate proteome information coming from high-throughput proteomics technologies. The online database administers experimental data in the public domain. You are encouraged to contribute to the database.

Proper citation: PeptideAtlas (RRID:SCR_006783) Copy   

•   Source: SciCrunch Registry

http://rice.plantbiology.msu.edu/

Database and resource that provides sequence and annotation data for the rice genome. This website provides genome sequence from the Nipponbare subspecies of rice and annotation of the 12 rice chromosomes. All structural and functional annotation is viewable through our Rice Genome Browser which currently supports 75 tracks of annotation. Enhanced data access is available through web interfaces, FTP downloads and a Data Extractor tool developed in order to support discrete dataset downloads. Rice is a model species for the monocotyledonous plants and the cereals which are the greatest source of food for the world''s population. While rice genome sequence is available through multiple sequencing projects, high quality, uniform annotation is required in order for genome sequence data to be fully utilized by researchers. The existence of a common gene set and uniform annotation allows researchers within the rice community to work from a common resource so that their results can be more easily interpreted by other scientists. The objective of this project has always been to provide high quality annotation for the rice genome. They generated, refined and updated gene models for the estimated 40,000-60,000 total rice genes, provided standardized annotation for each model, linked each model to functional annotation including expression data, gene ontologies, and tagged lines. They have provided a resource to extend the annotation of the rice genome to other plant species by providing comparative alignments to other plant species. Analysis/Tools are available including: BLAST, Locus Name Search, Functional Term Search, Protein Domain Search, Anatomy Expression Viewer, Highly Expressed Genes

Proper citation: Rice Genome Annotation (RRID:SCR_006663) Copy   

•   Source: SciCrunch Registry

http://gtrnadb.ucsc.edu

This genomic tRNA database contains tRNA gene predictions made by the program tRNAscan-SE (Lowe & Eddy, Nucl Acids Res 25: 955-964, 1997) on complete or nearly complete genomes. Unless otherwise noted, all annotation is automated, and has not been inspected for agreement with published literature. Transfer RNAs (tRNAs) represent the single largest, best-understood class of non-protein coding RNA genes found in all living organisms. By far, the major source of new tRNAs is computational identification of genes within newly sequenced genomes. To organize the rapidly growing collection and enable systematic analyses, we created the Genomic tRNA Database (GtRNAdb). The web resource provides overview statistics of tRNA genes within each analyzed genome, including information by isotype and genetic locus, easily downloadable primary sequences, graphical secondary structures and multiple sequence alignments. Direct links for each gene to UCSC eukaryotic and microbial genome browsers provide graphical display of tRNA genes in the context of all other local genetic information. The database can be searched by primary sequence similarity, tRNA characteristics or phylogenetic group. Inevitably with automated sequence analysis, we find exceptions to general identification rules, isoacceptor type predictions (esp. due to variable post-transcriptional anticodon modification), and questionable tRNA identifications (due to pseudogenes, SINES, or other tRNA-derived elements). We attempt to document all cases we come across, and welcome feedback on new or unrecognized discrepancies.

Proper citation: GtRNAdb - Genomic tRNA Database (RRID:SCR_006939) Copy   

•   Source: SciCrunch Registry

http://www.europhenome.org

Open source software system for capturing, storing and analyzing raw phenotyping data from SOPs contained in EMPReSS, it provides access to raw and annotated mouse phenotyping data generated from primary pipelines such as EMPReSSlim and secondary procedures from specialist centers. Mutants of interest can be identified by searching the gene or the predicted phenotype. You can also access phenotype data from the EMPReSSlim Pipeline for inbred mouse strains. Initially EuroPhenome was developed within the EUMORPHIA programme to capture and store pilot phenotyping data obtained on four background strains (C57BL/6J, C3H/HeBFeJ, BALB/cByJ and 129/SvPas). EUMORPHIA (European Union Mouse Research for Public Health and Industrial Applications) was a large project comprising of 18 research centers in 8 European countries, with the main focus of the project being the development of novel approaches in phenotyping, mutagenesis and informatics to improve the characterization of mouse models for understanding human molecular physiology and pathology. The current version of EuroPhenome is capturing data from the EUMODIC project as well as the WTSI MGP, HMGU GMC pipeline and the CMHD. EUMODIC is undertaking a primary phenotype assessment of up to 500 mouse mutant lines derived from ES cells developed in the EUCOMM project as well as other lines. Lines showing an interesting phenotype will be subject to a more in depth assessment. EUMODIC is building upon the comprehensive database of standardized phenotyping protocols, called EMPReSS, developed by the EUMORPHIA project. EUMODIC has developed a selection of these screens, called EMPReSSslim, to enable comprehensive, high throughput, primary phenotyping of large numbers of mice. Phenovariants are annotated using a automated pipeline, which assigns a MP term if the mutant data is statistically different to the baseline data. This data is shown in the Phenomap and the mine for a mutant tool. Please note that a statistically significant result and the subsequent MP annotation does not necessarily mean a true phenovariant. There are other factors that could cause this result that have not been accounted for in the analysis. It is the responsibility of the user to download the data and use their expert knowledge or further analysis to decide whether they agree or not. EuroPhenome is primarily based in the bioinformatics group at MRC Harwell. The development of EuroPhenome is in collaboration with the Helmholtz Zentrum Munchen, Germany, the Wellcome Trust Sanger Institute, UK and the Institut Clinique de la Souris, France.

Proper citation: Europhenome Mouse Phenotyping Resource (RRID:SCR_006935) Copy   

•   Source: SciCrunch Registry