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http://casp.sourceforge.net

CASP is a tool to image analysis in comet assay. CASP has been developed to work with either color, or gray-scale images of fluorescence-stained comets saved in TIF format. In its present version CASP does not control a video or CCD camera. Comets stained with silver (dark cells on white background) must be converted into negative images in order to be analysed correctly. An unlimited number of images can be marked, CASP will load them successively into a image view window (see screenshot). Only comets oriented from left (head) to right (tail) can be analysed correctly. The user can adjust various thresholds of sensitivity and save the adjustments for future use. A measurement frame is drawn on the screen and its size adjusted. The adjustments are frozen to prevent accidental modification. The frame is moved onto a cell and measurement is activated. An intensity profile shows up on a profile window together with selected result values (right window on figure 1) and the result can be saved. In addition to such parameter as head radius, tail length etc, the program calculates the tail moment (TM) and the Olive tail moment (OTM). If several cells are present on the same picture, the user can proceed with the measurement of another cell on the same picture or can load a new picture. The saved results can be visualized during the working session in a spreadsheet in view results window. When measurements are terminated, the results can be exported into a text file and imported into a commercial spreadsheet calculation program. CASP is optimized for a 600x800 resolution. Sponsors: This work has been supported by the University of Wroclaw. Keywords: Comet, Assay, Software, Laboratory, Camera, Negative, Cell, Analysis, Image,

Proper citation: CASPLab: Comet Assay Software Project Laboratory (RRID:SCR_007249) Copy   

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http://cardiogenomica.altervista.org/CARDIOGENOMICS/CardioGenomics%20Homepage.htm

The primary goal of the CardioGenomics PGA is to begin to link genes to structure, function, dysfunction and structural abnormalities of the cardiovascular system caused by clinically relevant genetic and environmental stimuli. The principal biological theme to be pursued is how the transcriptional network of the cardiovascular system responds to genetic and environmental stresses to maintain normal function and structure, and how this network is altered in disease. This PGA will generate a high quality, comprehensive data set for the functional genomics of structural and functional adaptation of the cardiovascular system by integrating expression data from animal models and human tissue samples, mutation screening of candidate genes in patients, and DNA polymorphisms in a well characterized general population. Such a data set will serve as a benchmark for future basic, clinical, and pharmacogenomic studies. Training and education are also a key focus of the CardioGenomics PGA. In addition to ongoing journal clubs and seminars, the PGA will be sponsoring symposia at major conferences, and developing workshops related to the areas of focus of this PGA. Information regarding upcoming events can be found in the Events section of this site, and information about training and education opportunities sponsored by CardioGenomics can be found on the Teaching and Education page. The CardioGenomics project came to a close in 2005. This server, cardiogenomics.med.harvard.edu, remains online in order to continue to distribute data that was generated by investigators under the auspices of the CardioGenomics Program for Genomic Applications (PGA). :Sponsors: This resource is supported by The National Heart, Lung and Blood Institute (NHLBI) of the NIH., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CardioGenomics (RRID:SCR_007248) Copy   

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http://www.mediacy.com/imageproplus

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 18,2023. Software package to capture, process, measure, analyze and share images and data.

Proper citation: Image Pro Plus (RRID:SCR_007369) Copy   

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http://ideas.repec.org/c/boc/bocode/s360702.html

COLELMS calculates LMS values, smoothed LMS, and growth reference centiles based in smoothed LMS values. df value is set to when calculating smoothed LMS values. You are responsible for setting an appropriate df for your data. This is version 0.2 of the software. Sponsors: This resource is supported by Boston College. Keywords: Software, LMS, Calculation, Growth, Data, Stata, Module,

Proper citation: COLELMS: Stata module to calculate Coles LMS values for growth data (RRID:SCR_007244) Copy   

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http://ekhidna.biocenter.helsinki.fi/sqgraph/pairsdb

This is a web interface for ADDA, an automatic algorithm for domain decomposition and clustering of all protein domain families. We use alignments derived from an all-on-all sequence comparison to define domains within protein sequences based on a global maximum likelihood model. ADDA is downloadable. There are three ways in which you can retrieve a protein sequence and its domains from ADDA. Sequences can be located using sequence identifiers and/or accession numbers, using a identical fragment lookup, or by running BLAST against all sequences in ADDA. ADDA is a protein sequence clustering algorithm. It takes a set of sequences and returns domain families. ADDA has two steps corresponding to the two aspects of the protein sequence clustering domain. First, ADDA splits protein sequences into domains. The idea behind ADDA is in principle the application of Occam''s razor; the goal is to describe the diversity of protein sequences with a minimal set of protein domains. The algorithm behind ADDA approximates this minimal set. In practice ADDA works by looking at where BLAST alignments are located on the sequence and splits the sequences, so that as few as possible alignments are cut by domain boundaries and that as many alignments as possible stretch over complete domains. Secondly, ADDA takes all the domains and then arranges them in a minimum spanning tree, where the similarity between two domains is determined by their relative overlap given a BLAST alignment. Each link in the tree is then checked by a pairwise profile-profile comparison and links below a threshold are removed. The remaining connected components are then taken to represent protein domain families.

Proper citation: ADDA - Automatic Domain Decomposition Algorithm (RRID:SCR_007546) Copy   

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http://www.nmpdr.org/FIG/wiki/view.cgi

The National Microbial Pathogen Data Resource provides curated annotations in an environment for comparative analysis of genomes and biological subsystems, with an emphasis on the food-borne pathogens Campylobacter, Listeria, Staphylococcus, Streptococcus, and Vibrio; as well as the STD pathogens Chlamydiaceae, Haemophilus, Mycoplasma, Neisseria, Treponema, and Ureaplasma. This edition of the NMPDR includes 47 archaeal, 725 bacterial, and 29 eukaryal genomes with 3,257,100 genetic features, of which 1,338,895 are in FIGfams curated using 616 active subsystems. ''''''Notice to NMPDR Users'''''' - The NMPDR BRC contract ended in December 2009. At that time we ceased maintenance of the NMPDR web resource and data. Bacterial data from NMPDR has been transferred to PATRIC (http://www.patricbrc.org), a new consolidated BRC for all NIAID category A-C priority pathogenic bacteria. NMPDR was a collaboration among researchers from the Computation Institute of the University of Chicago, the Fellowship for Interpretation of Genomes (FIG), Argonne National Laboratory, and the National Center for Supercomputing Applications (NCSA) at the University of Illinois.

Proper citation: NMPDR (RRID:SCR_007821) Copy   

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http://www.gene-regulation.com/pub/programs.html

In an effort to strongly support the collaborative nature of scientific research, BIOBASE offers access to their tools. Programs that are available through this portal are: * AliBaba 2.1: AliBaba2 is a program for predicting binding sites of transcription factor binding sites in an unknown DNA sequence. Therefore it uses the binding sites collected in TRANSFAC. AliBaba2 is currently the most specific tool for predicting sites. * Boxshade 3.3.1: Pretty Printing and Shading of Multiple-Alignment files. * ClustalW 1.8: ClustalW Multiple Sequence Alignment Program. * Dialign2.0: Multiple Sequence Alignment Program. * F-Match 1.0: F-MATCH is a program for identifying statistically overrepresented Transcription Factor Binding Sites (TFBS) in a set of sequences compared against a control set, assuming a binomial distribution of TFBS frequency. The program reads MATCH output files for the query and control sets. F-Match uses a library of mononucleotide weight matrices from TRANSFAC 6.0 * Match 1.0 Public: Match is designed for searching potential binding sites for transcription factors (TF binding sites) nucleotide sequences. MatchTM uses a library of mononucleotide weight matrices from TRANSFAC 6.0 * molwSearch 1.0: Search for transcription factors with a certain molecular weight. * P-Match 1.0: P-Match is a new tool for identifying transcription factor binding sites (TF binding sites) in DNA sequences. It combines pattern matching and weight matrix approaches thus providing higher accuracy of recognition than each of the methods alone. P-Match uses a library of mononucleotide weight matrices from TRANSFAC 6.0 along with the site alignments associated with these matrices. * Patch 1.0: Search for potential transcription factor binding sites in your own sequences with the pattern search program using TRANSFAC 6.0 public sites. * m2transfac 1.0: m2transfac is a PWM-PWM alignment interface for the TRANSFAC(R) database. For given user motifs, m2transfac reports all non-overlapping pairwise alignments to a TRANSFAC(R) matrix which satisfy a specified threshold. * MatrixCatch 2.7: The MatrixCatch tool is designed for searching potential composite elements (CEs) for transcription factors (TFs) in any DNA sequence, which may be of interest. MatrixCatch uses a library of CE matrix models, which were compiled on a basis of experimentally identified CEs collected in TRANSCOMPEL database and mononucleotide weight matrices for single TF-binding sites collected in TRANSFAC 6.0 public database. * Composite Module Analyst (CMA) 1.0: CMA reads output of Match program and applies a genetic algorithm in order to define promoter models based on the composition of transcription factor binding sites and their pairs. * PolyA Scan 0.000707: Scanning a Sequence for potential Polyadenylation Sites. * ReadSeq 2.0: ReadSeq reads and writes nucleic/protein sequences in various formats. * SignalScan: Analysis of DNA Sequences for known Eukaryotic Signals * SbBlast 1.0: Search Tool for Sequence Search in the S/MARt Binder Database. SbBlast makes use of the BLAST Sequence Similarity Search Tool - Version 2.0.13 (May-26-2000). * SnpFind 0.3: SNPFIND is a tool for searches in the Database of Single Nucleotide Polymorphisms. The search algorithm used for the database search is the BLAST algorithm. * TfBlast 0.1: Search Tool for Sequence Search in the TRANSFAC Factor Table. SbBlast makes use of the BLAST Sequence Similarity Search Tool - Version 2.0.13 (May-26-2000).

Proper citation: Gene Regulation Programs (RRID:SCR_007787) Copy   

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http://enhancer.lbl.gov/

Resource for experimentally validated human and mouse noncoding fragments with gene enhancer activity as assessed in transgenic mice. Most of these noncoding elements were selected for testing based on their extreme conservation in other vertebrates or epigenomic evidence (ChIP-Seq) of putative enhancer marks. Central public database of experimentally validated human and mouse noncoding fragments with gene enhancer activity as assessed in transgenic mice. Users can retrieve elements near single genes of interest, search for enhancers that target reporter gene expression to particular tissue, or download entire collections of enhancers with defined tissue specificity or conservation depth.

Proper citation: VISTA Enhancer Browser (RRID:SCR_007973) Copy   

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http://www.hpid.org

Database that provides human protein interaction information and integrated interaction and also finds proteins from databases that can potentially react with proteins submitted by users. The human protein interaction information was pre-computed by a statistical method from existing structural and experimental data, while the integrated human protein interactions are derived from BIND, DIP and HPRD. A score composed of three parts is assigned to the predicted interaction data, and those interactions with high scores were found reliable. HPID allows the user to use the protein IDs in EMBL, Ensembl, MIM, RefSeq, HPRD and NCBI to search protein interactions of interest. A set of web-based software tools has also been developed so that users can visualize and analyze protein interaction networks.

Proper citation: HPID - Human Protein Interaction database (RRID:SCR_007724) Copy   

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http://t1dbase.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 26,2019. In October 2016, T1DBase has merged with its sister site ImmunoBase (https://immunobase.org). Documented on March 2020, ImmunoBase ownership has been transferred to Open Targets (https://www.opentargets.org). Results for all studies can be explored using Open Targets Genetics (https://genetics.opentargets.org). Database focused on genetics and genomics of type 1 diabetes susceptibility providing a curated and integrated set of datasets and tools, across multiple species, to support and promote research in this area. The current data scope includes annotated genomic sequences for suspected T1D susceptibility regions; genetic data; microarray data; and global datasets, generally from the literature, that are useful for genetics and systems biology studies. The site also includes software tools for analyzing the data.

Proper citation: T1DBase (RRID:SCR_007959) Copy   

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http://www.louisville.edu/medschool/pharmacology/

The Department of Pharmacology & Toxicology in the School of Medicine at the University of Louisville focuses upon the interaction of drugs and other chemicals with biological systems ranging from individual molecules, to cells, to tissues, to organ systems or individuals. The two disciplines are a continuum incorporating the therapeutic to toxic effect of every drug and chemical. Our departmental programs incorporate pharmacology and/or toxicology, and graduates are well trained to accept employment in either or both disciplines. Our research and curriculum incorporate molecular biology, genetics, neuroscience, biochemistry, physiology and other biomedical sciences, providing maximum flexibility for our graduates.

Proper citation: University of Louisville, Department of Pharmacology (RRID:SCR_007510) Copy   

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https://rwjms.rutgers.edu/departments/pharmacology/message-from-the-chair

Department of Pharmacology is committed to fulfilling its roles in education, research and service, both locally and on a broader scale. Our faculty contributes to the education of medical, graduate and undergraduate students in the classroom and in the laboratory; carries out research at the forefront of biomedical science while training the next generation of research scientists; and serves the medical school, university, national and international scientific communities.

Proper citation: Rutgers University Robert Wood Johnson Medical School Department of Pharmacology (RRID:SCR_007470) Copy   

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http://vega.sanger.ac.uk/

Central repository for high quality frequently updated manual annotation of vertebrate finished genome sequence. Human, mouse and zebrafish are in the process of being completely annotated, whereas for other species the annotation is only of specific genomic regions of particular biological interest. The majority of the annotation is from the HAVANA group at the Welcome Trust Sanger Institute. Users can BLAST, search for specific text, export, and download data. Genomes and details of the projects for each species are available through the homepages for human mouse and zebrafish. The website is built upon code from the EnsEMBL (http://www.ensembl.org) project. Some Ensembl features are not available in Vega. From the users point of view perhaps the most significant of these is MartView. However due to their inclusion in Ensembl, Vega human and mouse data can be queried using Ensembl MartView. Vega contains annotation of the human MHC region in eight haplotypes, and the LRC region in three haplotypes. Vega also contains annotation on the Insulin Dependent Diabetes (IDD) regions on non-reference assemblies for mouse.

Proper citation: VEGA (RRID:SCR_007907) Copy   

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http://variation.osu.edu/rtcgd/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. Database of high throughput insertional mutagenesis screening projects of retroviral and transposon insertional mutagenesis in mouse tumors. Information in the RTCGD is obtained from sequence comparison by using public databases UCSC genome mm9 browser. Data based on previous genome assembly mm8 is also available at RTCGD mm8. MCGP has developed three web search tools including Easy Search to query proviral integration sites using mouse gene symbol of gene name; Model Search to obtain RIS information based on tumor models and/or tumor types; Interaction Search to find gene-to-gene interaction. It displays the list of genes which reside in the same tumor to your gene of interest.

Proper citation: Retroviral Tagged Cancer Gene Database (RRID:SCR_007908) Copy   

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

Software package for TDT with extended haplotypes in the R language. R is the public domain dialect of S. It should be possible to port this library to the commercial Splus product. The main problem would be translation of the help files. (entry from Genetic Analysis Software)

Proper citation: R/TDTHAP (RRID:SCR_007625) Copy   

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http://www.mcmp.purdue.edu/

The Department of Medicinal Chemistry and Molecular Pharmacology is one of the three departments comprising the School of Pharmacy and Pharmaceutical Sciences. There are currently over 80 graduate students enrolled in the Department, the vast majority of whom are engaged in studies leading to the Ph.D. degree. The presence of approximately 30 postdoctoral associates and other research staff professionals further enriches the intellectual atmosphere. Graduate students in the Department have the opportunity to interact with researchers in a wide variety of fields, including many who are associated with other departments at Purdue. Various research groups actively collaborate with groups from departments such as biochemistry, biological sciences, chemical engineering, chemistry, foods and nutrition, horticulture, and physics. In addition, approximately half of our faculty belong to the Purdue Cancer Center, the Neuroscience Program, the Graduate Program in Virology and/or the Purdue University Biochemistry and Molecular Biology Program (BMB), leading to extensive, formalized interactions campus-wide. Graduate students attend research seminars across campus, -affording the opportunity to observe firsthand many of the world's foremost researchers.

Proper citation: Purdue University School of Pharmacy and Pharmaceutical Sciences Department of Medicinal Chemistry and Molecular Pharmacology (RRID:SCR_007468) Copy   

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http://purl.bioontology.org/ontology/CHD

An ontology that describes the Congenital Heart Defects data.

Proper citation: Congenital Heart Defects Ontology (RRID:SCR_007584) Copy   

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http://fmf.igh.cnrs.fr/ISSAID/infevers

Registry for Familial Mediterranean Fever (FMF) and hereditary inflammatory disorders mutations. As of 2014, it includes twenty genes including: MEFV, MVK, TNFRSF1A, NLRP3, NOD2, PSTPIP1, LPIN2 and NLRP7, and contains over 1338 sequence variants. Confidential data, simple and complex alleles are accepted. For each gene, a menu offers: 1) a tabular list of the variants that can be sorted by several parameters; 2) a gene graph providing a schematic representation of the variants along the gene; 3) statistical analysis of the data according to the phenotype, alteration type, and location of the mutation in the gene; 4) the cDNA and gDNA sequences of each gene, showing the nucleotide changes along the sequence, with a color-based code highlighting the gene domains, the first ATG, and the termination codon; and 5) a download menu making all tables and figures available for the users, which, except for the gene graphs, are all automatically generated and updated upon submission of the variants. The entire database was curated to comply with the HUGO Gene Nomenclature Committee (HGNC) and HGVS nomenclature guidelines, and wherever necessary, an informative note was provided.

Proper citation: INFEVERS (RRID:SCR_007738) Copy   

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http://www.pharmacy.utah.edu/pharmtox/

The Department of Pharmacology and Toxicology at the University of Utah is located in Salt Lake City at the foot of the beautiful Wasatch Range of the Rocky Mountains. Our Department focuses on research, graduate and professional training, and service. The faculty of this department place a high priority on the teaching and research training of graduate students for the Ph.D. degree. Our program features close working relationships between individual students and their faculty mentor, rich and diverse research opportunities, and individualized programs of study based on the needs of the students. Doctoral graduates of our program gain employment in research and teaching positions at colleges and universities, engage in research and development in the biotechnology and pharmaceutical industries, and have additional opportunities in research institutes, government agencies, environmental protection organizations, and many other arenas. Our Summer Undergraduate Research Fellowship (SURF) Program provides enriching research experiences for undergraduate students anticipating research careers in the biological sciences.

Proper citation: University of Utah Salt Lake City Utah. Pharmacology & Toxicology (RRID:SCR_007537) Copy   

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http://rfam.xfam.org/

The Rfam database is a collection of RNA families, each represented by multiple sequence alignments, consensus secondary structures and covariance models (CMs). The families in Rfam break down into three broad functional classes: Non-coding RNA genes, structured cis-regulatory elements and self-splicing RNAs. Typically these functional RNAs often have a conserved secondary structure which may be better preserved than the RNA sequence. The CMs used to describe each family are a slightly more complicated relative of the profile hidden Markov models (HMMs) used by Pfam. CMs can simultaneously model RNA sequence and the structure in an elegant and accurate fashion. Rfam is also available via FTP. You can find data in Rfam in various ways... * Analyze your RNA sequence for Rfam matches * View Rfam family annotation and alignments * View Rfam clan details * Query Rfam by keywords * Fetch families or sequences by NCBI taxonomy * Enter any type of accession or ID to jump to the page for a Rfam family, sequence or genome

Proper citation: Rfam (RRID:SCR_007891) Copy   

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