We support boolean queries, use +,-,<,>,~,* to alter the weighting of terms
Software for analysis and visualization of gene expression and SNP microarrays.
Software R package. Methods for Cluster analysis. Performs variety of types of cluster analysis and other types of processing on large microarray datasets.
Software for analysis and visualization of gene expression and SNP microarrays.
Software to graphically browse results of clustering and other analyses from Cluster.
Not yet vetted by NIF curator
A database for facilitating the search for drug Absorption, Distribution, Metabolism, Excretion (ADME) associated proteins. It contains information about known drug ADME associated proteins, functions, similarities, substrates / ligands, tissue distributions, and other properties of the targets. Associated references are also included. Drug absorption, distribution, metabolism and excretion (ADME) often involve interaction of a drug with specific proteins. Knowledge about these ADME-associated proteins is important in facilitating the study of the molecular mechanism of disposition and individual response as well as therapeutic action of drugs. It is also useful in the development and testing of pharmacokinetics prediction tools. Several databases describing specific classes of ADME-associated proteins have appeared. A new database, ADME-associated proteins (ADME-AP), is introduced to provide comprehensive information about all classes of ADME-associated proteins described in the literature including physiological function of each protein, pharmacokinetic effect, ADME classification, direction and driving force of disposition, location and tissue distribution, substrates, synonyms, gene name and protein availability in other species. Cross-links to other databases are also provided to facilitate the access of information about the sequence, 3D structure, function, polymorphisms, genetic disorders, nomenclature, ligand binding properties and related literatures of each protein. ADME-AP currently contains entries for 321 proteins and 964 substrates. ADME Class Based on their respective role of pharmacokinetics, ADME-associated proteins can be classified into four categories: A: This Category includes proteins involved in the absorption or re-absorption of drugs into systemic system. D: This category includes proteins responsible for facilitating the distribution of drugs from the systemic system to the target sites or away from the target sites back to the systemic system. Certain plasma proteins and intracellular binding proteins may alter free drug concentration by acting as drug storage depot. These proteins thus play a regulatory role in drug distribution and they are thus included in Category D. Based on their role in drug distribution, proteins in this category can be further divided into three groups D1, D2, and D3. The first group D1 includes transporters capable of transporting chemicals across membranes of various tissue barriers from the systemic system into the target sites. Blood-brain barrier and placenta barrier are examples of tissue barrier. Proteins in the second group D2 are responsible for transporting drugs back into the systemic system. Proteins in the third group D3 mainly function as drug storage depot. These include ligand binding proteins in plasma and intracellular proteins. M: Proteins in category M are drug-metabolizing enzymes. These enzymes can be further divided into two separate groups M1 and M2, according to whether the corresponding enzymatic reaction is phase I or phase II. E: This category E includes proteins that enable the excretion or presystemic elimination of drugs. Some proteins belong to more than one category: e.g. P-glycoprotein both limits intestinal absorption and excludes drugs from the brain back to the blood. It thus belongs to both Category E and D. For those proteins capable of transporting natural substrates without literature report of interaction with a drug, a postfix potential is attached to their respective classification to indicate that their specific role in ADME is yet to be confirmed. Use of ADME-AP for commercial purposes is not allowed.
A dataset generated longitudinal study that aims to explain the relationship between age and changes in the sense of control over one''''s life, over two follow-up periods. The main hypotheses are (a) over a period of time, the sense of control declines by an amount that increases with age; (b) the change in sense of control reflects an underlying change in biosocial function, which accelerates with age; (c) higher social status slows the decline in the sense of control, possibly by preserving biosocial function; and (d) changes in biosocial function and in the sense of control have deviation-amplifying reciprocal effects that accelerate age-dependent changes in the sense of control. This was a three-wave panel survey with fixed 3-year intervals and repeated assessments of the same variables. Questionnaire topics focused on: physical health (subjective health; activities of daily living; height and weight; health conditions; expected personal longevity); health behavior (exercise, smoking, diet, alcohol use); use of medical services (medical insurance coverage, prescription drug use); work status (current employment status; title of current job or occupation and job description; types of work, tasks, or activities; description of work or daily activity and interactions; supervisory status; management position and level; work history); sense of controlextent of agreement or disagreement with planning and responsibility versus luck and bad breaks; sense of victimhood versus control; social support and participation; personal and household demographics; marital and family relations; socioeconomic status; history of adversity. * Dates of Study: 1994-2001 * Sample Size: 2,593 (Waves 1-2); 1.144 (Wave 3) * Study Features: Longitudinal Data Archives: http://www.sscnet.ucla.edu/issr/da/da_catalog/da_catalog_titleRecord.php?studynumber=I3334V1
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 6,2023. EMMA (Extensible MATLAB Medical image Analysis) is a toolkit designed to ease the use of MATLAB in the analysis of medical imaging data. It provides functions for reading and writing MINC files, viewing images, performing ROI operations, and performing several popular analyses. Also, there are toolkits for performing kinetic analysis of dynamic PET rCBF (regional cerebral blood flow) and FDG data. The goal for this site is to provide a centrally available listing of all image analysis tools that are available to the neuroscience community in order to facilitate the development, identification, and sharing of tools that are of use to the general community.
The AARSs database is the collection of amino acid sequences of all published AARSs. Currently it contains 1047 primary structures of cytoplasmic and organellar AARSs from various organisms. The entries are grouped according to AARS amino acid specificity. They are based on EMBL/SWISS-PROT format. Each includes the AARS amino acid sequence, its SWISS-PROT name and the accession number, a short description of the sequence, its source (organism name with taxonomic classification) and bibliographic information. For the enzymes whose sequences were determined at the nucleotide level, the appropriate EMBL/GenBank or TIGR entries are included, and for those with already known 3D structure, the cross-references to the Brookhaven Protein Data Base are indicated. The partial sequences of AARSs are also included in the database. According to the original SWISS-PROT description, some of the entries have been marked as putative or probable.
DINO is a realtime 3D visualization program for structural biology data. It runs under X-Windows and uses OpenGL. Supported architectures are Linux-i586 and Mac OSX. Versions for IRIX, OSF1 and SunOS are made available sporadically, usually upon request. DINO is distributed in binary form only, the current DINO version is 0.9.1. Structural Biology is a multidisciplinary research area, including x-ray crystallography, structural NMR, electron microscopy, atomic-force microscopy and bioinformatics (molecular dynamics, structure predictions, surface calculations etc). The data produced by these different research areas is very diverse: atomic coordinates (models and predictions), electron density maps, surface topographs, trajectories, molecular surfaces, electrostatic potentials, sequence alignements etc... DINO aims to visualize all this structural data in a single program and to allow the user to explore relationships between the data. There are five data-types supported: structure (atomic coordinates and trajectories), surface (molecular surfaces), scalar fields (electron densities and electrostatic potentials), topographs (surface topography scans) and geom (geometric primitives such as lines). The number and size of the data the program can handle is only limited by the amount of RAM present in the system. No artifical limits are set. Supported input file formats are PDB (coordinates), X-PLOR/CNS (coordinates, electron densities and trajectories), CHARMM (coordinates, trajectories and scalar fields), CCP4 (electron densities), UHBD (el, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
A graphical tool designed for detection of errors in relationship specification in general pedigrees by use of genome scan marker data. (entry from Genetic Analysis Software)
EPSRC is the main UK government agency for funding research and training in engineering and the physical sciences, investing more than £850 million a year in a broad range of subjects from mathematics to materials science, and from information technology to structural engineering. We support research into engineering, mathematics, physics, chemistry, materials science, information and communications technologies. EPSRC is a non-departmental public body funded by the UK government through the Department for Universities, Innovation and Skills. We employ around 300 staff in Swindon. We manage our portfolio through programmes. Research base programmes focus on investigator-led research and training. Business innovation programmes deliver our priority research themes and maximise the economic and social impact of the research and training we fund.
Software program to systematically design gene specific long oligonucleotide probes for entire genomes, for the purpose of developing whole genome microarrays. For each open reading frame, the program optimizes the oligo selection based upon several parameters, including uniqueness in the genome, sequence complexity, lack of self-binding, GC content and proximity to the 3''end of the gene.
An Antibody supplier
An Antibody supplier
It is a comprehensive database of Gene Expression Profiles, which enable to compare the transcriptome of various tissues, organs and experiments. mRNA expression levels of thousands of genes are measured with oligo-nucleotide DNA microarray "GeneChip". All gene expression data in this database is produced by LSBM (Laboratory for Systems Biology and Medicine) and the collaborators. SBM DB provides two different databases: A reference database for fur expression analysis (RefEXA) and LSMB GeNet, a database of various organisms, tissues, and experiences. RefEXA provides a comprehensive gene expression database of Human normal tissues, normal cultured cells and cancer cell lines with GeneChip HG-U133A, can help investigation of Human disease. LSMB provides
Software application that selects high resolution mapping subsamples and performs bin mapping (entry from Genetic Analysis Software)
Voltage-gated potassium channel database (VKCDB) is designed to serve as a resource for research on voltage-gated potassium channels. Protein sequences, references, functional data and many other relevant data are included in this database. Mysql is used as the underlying database management system to meet the requirements of future development plans. VKCDB will allow you to browse and search using different annotation criteria, and search against the sequences in VKCDB with VKCBLAST. Displayed entries can be selected to download as multiple sequences in FASTA format for further analyses, such as ClustalW alignment. Several computational tools are being developed to help guide structure function analysis of voltage-gated potassium channels and other protein families in general. VKCDB currently stores 346 voltage-gated potassium channel entries, including some "unknown proteins" annotated by automatic genome annotation projects which share a high degree of sequence similarity with voltage-gated potassium channels. VKCDB was populated using automatic parsing of BLASTP search output. Entries were checked manually for redundancy, sequence conflicts, and isoforms, and they are hyperlinked to their variant entries as well as to entries with sequence conflicts. All entries contain GenBank annotations and Swissprot annotations if available. Current annotations were updated with Swissprot release 43.1 (Apr 2004) and GenBank as of Apr 2004. The snapshot version 3 of VKCDB in XML format as of Apr 2004 can be downloaded here. We collected available electrophysiological data and pharmacological data from over 200 journal articals and stored them in VKCDB. VKCDB is updated regularly from BLAST searches of new entries in GenBank and Swissprot. VKCBLAST are updated to include new sequences within a few days after new entries are added into VKCDB. Sequences of all VKCDB entries were sent to TMHMM and PHD for membrane domain prediction. Both results were parsed and linked to each VKCDB entry page. Multiple alignment of T1 and six transmembrane domains of Kv1-4 and KCNQ (Kv7) family are also presented here. VKCDB is a part of an ongoing high throughput voltage-gated potassium channel sequencing project in the laboratories of Dr. Warren J. Gallin and Dr. Andy N. Spencer at the University of Alberta, Canada.
A suit of algorithms and tools for the analysis of gene expression data and the discovery of cis-regulatory sequence elements.
DOLOP is an exclusive knowledge base for bacterial lipoproteins by processing information from 510 entries to provide a list of 199 distinct lipoproteins with relevant links to molecular details. Features include functional classification, predictive algorithm for query sequences, primary sequence analysis and lists of predicted lipoproteins from 43 completed bacterial genomes along with interactive information exchange facility. This website along will have additional information on the biosynthetic pathway, supplementary material and other related figures. DOLOP also contains information and links to molecular details for about 278 distinct lipoproteins and predicted lipoproteins from 234 completely sequenced bacterial genomes. Additionally, the website features a tool that applies a predictive algorithm to identify the presence or absence of the lipoprotein signal sequence in a user-given sequence. The experimentally verified lipoproteins have been classified into different functional classes and more importantly functional domain assignments using hidden Markov models from the SUPERFAMILY database that have been provided for the predicted lipoproteins. Other features include: primary sequence analysis, signal sequence analysis, and search facility and information exchange facility to allow researchers to exchange results on newly characterized lipoproteins.