Curator Dashboard

Functional Information Viewer and Analyzer (FIVA) aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software is able to assist in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes. Currently, seven different modules containing functional information have been implemented: (i) gene regulatory interactions, (ii) cluster of orthologous groups (COG) of proteins, (iii) gene ontologies (GO), (iv) metabolic pathways (v) Swiss Prot keywords, (vi) InterPro domains - and (vii) generic functional categories. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

• Resource
• SciCrunch
• 15 years ago - by Anonymous

Organization focusing on the regulation of important functions and the roles of the deregulation of these functions in the genesis and progression of diseases. Nineteen teams are structure in 2 departments, Physiology, Metabolism, Differenciation and Immunology, Cancer, Inflammation. This 2 departments are joined by a Therapeutic Innovations coordinated by a team manager. Through a strong synergism between scientists, clinicians and industry, the CRC has created the means of success of translational research. Several teams are involved in innovation in various fields such as imaging, identifications of genetic, protein or cellular prognostic markers, the production of molecules (monoclonal antibody) or generation of cells (dendritic cells or macrophage) for therapeutic use. The CRC has developed important core facilities including a modern animal facility with a transgenic service, a small animals imaging platform, a core facility for in vivo and ex vivo studies of renal functions, an ex vivo cell imaging facility with a confocal microscopy service, flow cytometry, electron microscopy and laser micro dissection. These facilities under the management of highly competent scientists and engineers work for the transfer of competence toward the students and ensures the continued formation through regular organization of scientific day meetings. The CRC participates in the research and academic training. On the campus is located the faculty of medicine Paris Descartes, the Pierre and Marie Curie Institute of Doctoral training and the school of physiology and pathophysiology. About a hundred PhD students belonging to 8 different Doctoral Schools are hosted in 36 doctoral training teams. Students from technical, bachelor and masters levels are also being trained. In addition, young pupils from secondary and high school participate regularly in the programs for initiation to research conceptions and technology. Seminars as well as meetings at national and European levels and scientific debates on various important contemporary topics, and open-door programs are conducted regularly in the Center.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

FunCluster is a genomic data analysis algorithm which performs functional analysis of gene expression data obtained from cDNA microarray experiments. Besides automated functional annotation of gene expression data, FunCluster functional analysis aims to detect co-regulated biological processes through a specially designed clustering procedure involving biological annotations and gene expression data. FunCluster''''s functional analysis relies on Gene Ontology and KEGG annotations and is currently available for three organisms: Homo Sapiens, Mus Musculus and Saccharomyces Cerevisiae. FunCluster is provided as a standalone R package, which can be run on any operating system for which an R environment implementation is available (Windows, Mac OS, various flavors of Linux and Unix). Download it from the FunCluster website, or from the worldwide mirrors of CRAN. FunCluster is provided freely under the GNU General Public License 2.0. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

• Resource
• SciCrunch
• 15 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 11, 2012. FuncExpression is a web-based resource for functional interpretation of large scale genomics data. FuncExpression can be used for the functional comparison of plant, animal, and fungal gene name lists generated from genomics and proteomics experiments. Multiple gene lists can be classified, compared and visualized. FuncExpression supports two way-integration of plant gene functional information and the gene expression data, which allows for further cross-validation with plant microarray data from related experiments at BarleyBase. Platform: Online tool

• Resource
• SciCrunch
• 15 years ago - by Anonymous

The Burroughs Wellcome Fund is an independent private foundation dedicated to advancing the biomedical sciences by supporting research and other scientific and educational activities. Within this broad mission, BWF has two primary goals: * To help scientists early in their careers develop as independent investigators * To advance fields in the basic biomedical sciences that are undervalued or in need of particular encouragement BWF''s financial support is channeled primarily through competitive peer-reviewed award programs. * BWF''s endowment: $586.8 million at the end of FY 2009 * BWF approved $26.4 million in grants during FY 2009 BWF makes grants primarily to degree-granting institutions on behalf of individual researchers, who must be nominated by their institutions. To complement these competitive award programs, BWF also makes grants to nonprofit organizations conducting activities intended to improve the general environment for science. A Board of Directors comprising distinguished scientists and business leaders governs BWF. BWF was founded in 1955 as the corporate foundation of the pharmaceutical firm Burroughs Wellcome Co. In 1993, a generous gift from the Wellcome Trust in the United Kingdom, enabled BWF to become fully independent from the company, which was acquired by Glaxo in 1995. BWF has no affiliation with any corporation.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. UCbase & miRfunc is a database of (i) human, mouse and rat microRNAs and (ii) Ultraconserved elements providing information about function, expression and correlation between these classes of non-coding RNAs and the disorders related to their aberrant expression. The genomics interface allows the user to explore where whole-genome collections of miRNAs and UCRs are located with respect to annotation sets such as band, disorders and known genes. The Blast interface provides a web tool for matching miRNAs/UCRs elements against any given sequence and providing specific functional information on the results. 481 Ultraconserved sequences (UCRs) longer than 200 bases were discovered in the genomes of human, mouse and rat. These are DNA sequences showing 100 percent identity among the human, mouse and rat genomes. UCRs are frequently located at genomic regions involved in cancer, differentially expressed in human leukemias and carcinomas and in some instances regulated by microRNAs (miRNAs), the most extensively studied category of non-coding RNAs (ncRNAs). Here we present the first database which links UCRs and miRNAs with the related human disorders and genomic properties.

• Resource
• SciCrunch
• 17 years ago - by Anonymous

GOdist is a Matlab program that analyzes Affymetrix microarray expression data implementing Kolmogorov-Smirnov (KS) continuous statistics approach. It also implements the discrete approach using Fisher exact test employing a two-tailed hypergeometric distribution. GOdist enables detection of both kinds of changes within specific GO terms represented on the array in relation to different populations: the global array population, the direct parents of the analyzed GO term and the global parent of it (e.g. biological process, molecular function or cellular component). Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

• Resource
• SciCrunch
• 15 years ago - by Anonymous

An organization composed of biochemical, clinical, cytogenetic, medical and molecular geneticists, genetic counselors and other health care professionals committed to the practice of medical genetics to Improve Health Through Medical Genetics. The American College of Medical Genetics and Genomics will: * Define and promote excellence in the practice of medical genetics and genomics in the integration of translational research into practice; * Promote and provide medical genetics and genomics education; * Increase access to medical genetics and genomics services and integrate them into patient care; * Advocate for and represent providers of medical genetics and genomics services and their patients; and * Maintain structure and integrity of ACMG and its value to members and the public.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

A web-based tool that accepts as input a list of genes, and returns a list of GO attributes that are over- (or under-) represented among the genes in the input list. Only those over- (or under-) representations that are statistically significant, after correcting for multiple hypotheses testing, are reported. Currently 37 organisms are supported. In addition to the input list of genes, users may specify a) whether this list should be regarded as ordered or unordered; b) the universe of genes to be considered by FuncAssociate; c) whether to report over-, or under-represented attributes, or both; and d) the p-value cutoff. A new version of FuncAssociate supports a wider range of naming schemes for input genes, and uses more frequently updated GO associations. However, some features of the original version, such as sorting by LOD or the option to see the gene-attribute table, are not yet implemented. Platform: Online tool

• Resource
• SciCrunch
• 15 years ago - by Anonymous

GlycoPeptideSearch (GPS) simplifies data interpretation of N-glycopeptide CID MS/MS datasets by searching for glycopeptide results consistent with MS/MS spectra. Results are tabulated in Excel format. Accelerate and simplify interpretation of N-glycopeptide CID MS/MS spectra using GlycoPeptideSearch (GPS). This tool is designed for tandem mass-spectra acquired from proteolytic digests of purified glycoproteins modified with N-glycans and analyzed by LC-MS/MS and CID. The search yields an Excel spreadsheet of N-glycopeptide matches consistent with the spectra. GPS requires two files as input - an mzXML (or other open spectral format) file of glycopeptide CID tandem mass-spectra and a text file (.txt) of peptide sequences containing the N-linked glycosylation motif NXS/T. Spectral datafiles must be converted from raw vendor formats, such as .RAW or .wiff, to an open peak list format (mzXML preferred). In addition to these two input files, the user must specify one or more glycan databases (provided in the software package). The database(s) selected by the user will be used to match glycan structures in the glycopeptide spectra. The output is an Excel spreadsheet with one or more rows for spectra within the dataset that contain evidence of glycoprotein fragmentation, paired with one or more proposed glycopeptide matches for each spectrum. Glycopeptide matches consist of a peptide-glycan pair, with the peptide drawn from the user-supplied peptide file, and the glycan selected from a glycan database(s). The human subset of the GlycomeDB glycan database is provided, and N-linked glycans are automatically selected from it. GPS interprets glycopeptide CID MS/MS spectra by first requiring MS/MS spectra contain evidence of glycopeptide fragmentation - the oxonium ion peaks (m/z 204 - Hex, m/z 366 - HexNAc), and N-glycopeptide core specific peaks (peptide, peptide + HexNAc, peptide + HexNAc-HexNAc, peptide + HexNAc-HexNAc-Hex). For spectra that meet these initial criteria, for a particular peptide, a mass-based search of one or more glycan databases looks for glycans which capture the remaining mass of the spectral precursor. Additional spectral information may be used to narrow the number of matches, and equivalent glycan topologies may be collapsed to a single peptide-glycan pair. GPS also provides N-glycan compositions with the necessary additional mass, even if no glycan with the composition is present in the glycan database(s). GPS can either be run from the command-line or by using its graphical user interface. We recommend the msconvert (or MSConvertGUI) software from the ProteoWizard project to convert spectral datafiles from vendor formats such as .wiff and .RAW into mzXML.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

To test a sample population of genes for overrepresentation of GO terms, the R/BioC function GOHyperGAll computes for all GO nodes a hypergeometric distribution test and returns the corresponding p-values. A subsequent filter function performs a GO Slim analysis using default or custom GO Slim categories. Basic knowledge about R and BioConductor is required for using this tool. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

A portal for hormone-related health information for the public, physicians, allied health professionals and the media. It serves as a resource for the public by promoting the prevention, treatment and cure of hormone-related conditions through outreach and education. It provides free educational materials, public forums, physician referral service, and media education campaigns. It offers a library of educational materials and programs covering a wide range of endocrine topics, including adrenal disorders, breast cancer, diabetes, osteoporosis, stress, thyroid disease and cancer.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

The Peptide Sequence Database contains putative peptide sequences from human, mouse, rat, and zebrafish. Compressed to eliminate redundancy, these are about 40 fold smaller than a brute force enumeration. Current and old releases are available for download. Each species'' peptide sequence database comprises peptide sequence data from releveant species specific UniGene and IPI clusters, plus all sequences from their consituent EST, mRNA and protein sequence databases, namely RefSeq proteins and mRNAs, UniProt''s SwissProt and TrEMBL, GenBank mRNA, ESTs, and high-throughput cDNAs, HInv-DB, VEGA, EMBL, IPI protein sequences, plus the enumeration of all combinations of UniProt sequence variants, Met loss PTM, and signal peptide cleavages. The README file contains some information about the non amino-acid symbols O (digest site corresponding to a protein N- or C-terminus) and J (no digest sequence join) used in these peptide sequence databases and information about how to configure various search engines to use them. Some search engines handle (very) long sequences badly and in some cases must be patched to use these peptide sequence databases. All search engines supported by the PepArML meta-search engine can (or can be patched to) successfully search these peptide sequence databases.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

The PeptideMapper Web-Service provides alignments of peptide sequence alignments to proteins, mRNA, EST, and HTC sequences from Genbank, RefSeq, UniProt, IPI, VEGA, EMBL, and HInvDb. This mapping infrastructure is supported, in part, by the compressed peptide sequence database infrastructure (Edwards, 2007) which enables a fast, suffix-tree based mapping of peptide sequences to gene identifiers and a gene-focused detailed mapping of peptide sequences to source sequence evidence. The PeptideMapper Web-Service can be used interactively or as a web-service using either HTTP or SOAP requests. Results of HTTP requests can be returned in a variety of formats, including XML, JSON, CSV, TSV, or XLS, and in some cases, GFF or BED; results of SOAP requests are returned as SOAP responses. The PeptideMapper Web-Service maps at most 20 peptides with length between 5 and 30 amino-acids in each request. The number of alignments returned, per peptide, gene, and sequence type, is set to 10 by default. The default can be changed on the interactive alignments search form or by using the max web-service parameter.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

A web server that predicts the functional impact of amino-acid substitutions in proteins, such as mutations discovered in cancer or nonsynonymous polymorphisms. The functional impact is assessed based on evolutionary conservation of the affected amino acid in protein homologs. The method has been validated on a large set (51k) of disease associated (OMIM) and polymorphic variants., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

ALCHEMY is a genotype calling algorithm for Affymetrix and Illumina products which is not based on clustering methods. Features include explicit handling of reduced heterozygosity due to inbreeding and accurate results with small sample sizes. ALCHEMY is a method for automated calling of diploid genotypes from raw intensity data produced by various high-throughput multiplexed SNP genotyping methods. It has been developed for and tested on Affymetrix GeneChip Arrays, Illumina GoldenGate, and Illumina Infinium based assays. Primary motivations for ALCHEMY''s development was the lack of available genotype calling methods which can perform well in the absence of heterozygous samples (due to panels of inbred lines being genotyped) or provide accurate calls with small sample batches. ALCHEMY differs from other genotype calling methods in that genotype inference is based on a parametric Bayesian model of the raw intensity data rather than a generalized clustering approach and the model incorporates population genetic principles such as Hardy-Weinberg equilibrium adjusted for inbreeding levels. ALCHEMY can simultaneously estimate individual sample inbreeding coefficients from the data and use them to improve statistical inference of diploid genotypes at individual SNPs. The main documentation for ALCHEMY is maintained on the sourceforge-hosted MediaWiki system. Features * Population genetic model based SNP genotype calling * Simultaneous estimation of per-sample inbreeding coefficients, allele frequencies, and genotypes * Bayesian model provides posterior probabilities of genotype correctness as quality measures * Growing number of scripts and supporting programs for validation of genotypes against control data and output reformating needs * Multithreaded program for parallel execution on multi-CPU/core systems * Non-clustering based methods can handle small sample sets for empirical optimization of sample preparation techniques and accurate calling of SNPs missing genotype classes ALCHEMY is written in C and developed on the GNU/Linux platform. It should compile on any current GNU/Linux distribution with the development packages for the GNU Scientific Library (gsl) and other development packages for standard system libraries. It may also compile and run on Mac OS X if gsl is installed.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

Database of gene expression in the marmoset brain.Comparative anatomy of marmoset and mouse cortex from genomic expression. Atlas comparing brain of neonatal marmoset with mouse using in situ hybridization.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

A ChIP-Seq peak calling or differential binding analysis tool that is primarily designed for data with biological replicates. It uses a negative binomial distribution to model the read counts among the samples in the same group, and look for consistent differences between ChIP and control group or two ChIP groups run under different conditions.

• Resource
• SciCrunch
• 12 years ago - by Anonymous

Collect, share, and distribute information about protein three-dimensional structures. It serves as a portal for the scientific community to learn about protein structures solved by SG centers, and also to contribute their expertise in annotating protein function. The premise of the TOPSAN project is that, no matter how much any individual knows about a particular protein, there are other members of the scientific community who know more about certain aspects of the same protein, and that the collective analyses from experts will be far more informative than any local group, let alone individual, could contribute. They believe that, if the members of the biological community are given the opportunity, authorship incentives, and an easy way to contribute their knowledge to the structure annotation, they would do so. Therefore, borrowing elements from successful, distributed, collaborative projects, such as Wikipedia (the free encyclopedia anyone can edit) and from other open source software development projects, TOPSAN will be a broad, collaborative effort to annotate protein structures, initially, those determined at the JCSG. They believe that the annotation of proteins solved by structural genomics consortia offers a unique opportunity to challenge the extant paradigm of how biological data is collected and distributed, and to connect structural genomics and structural biology to the entire biological research community. TOPSAN is designed to be scalable, modular and extensible. Furthermore, it is intended to be immediately useful in a simplistic way and will accommodate incremental improvements to functionality as usage becomes more sophisticated. Their annotation pages will offer the end user a combination of automatically generated as well as expert-curated annotations of protein structures. They will use available technology to increase the speed and granularity of the exchange of scientific ideas, and use incentive mechanisms that will encourage collaborative participation.

• Resource
• SciCrunch
• 15 years ago - by Anonymous

Software that utilizes a multiobjective evolutionary algorithm for genetic mapping. It is based on a the ECJ evolutionary software package written by Sean Luke and includes the Strength Pareto Evoluationary Algorithm Version 2 changes for multiobjective analysis. The code runs on any platform with Java Version 2. A genetic mapping project, typically implemented during a search for genes responsible for a disease, requires the acquisition of a set of data from each of a large number of individuals. This data set includes the values of multiple genetic markers. These genetic markers occur at discrete positions along the genome, which is a collection of one or more linear chromosomes. Typing the value of a marker in an individual carries a cost; one seeks to minimize the number of markers typed without excessively jeopardizing the probability of detecting an association between a marker and a disease phenotype. MAGMA is a project which employ''s a multiobjective evolutionary algorithm to solve this problem.

• Resource
• SciCrunch
• 15 years ago - by Anonymous