Curator Dashboard

The American Cancer Society is the nationwide, community-based, voluntary health organization dedicated to eliminating cancer as a major health problem by preventing cancer, saving lives, and diminishing suffering from cancer, through research, education, advocacy, and service. Together with our millions of supporters, the American Cancer Society (ACS) saves lives and creates a world with less cancer and more birthdays by helping people stay well, helping people get well, by finding cures, and by fighting back. Headquartered in Atlanta, Georgia, the ACS has 12 chartered Divisions, more than 900 local offices nationwide, and a presence in more than 5,100 communities.

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• SciCrunch
• 15 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 20,2019.Database and analysis environment for experimentally determined binding sites of RNA-binding proteins. It supports the automatic functional annotation of short reads resulting primarily from crosslinking and immunoprecipitation experiments (CLIP) performed with RNA-binding proteins in order to identify the binding sites of these proteins. The functional annotation could be also applied to short reads resulting from other types of experiments such as mRNA-Seq, Digital Gene Expression, small RNA cloning, etc. The platform enables visualization and mining of individual data sets as well as analysis involving multiple experimental data sets. The platform can support collaborative projects involving multiple users and groups of users as well as public and private datasets.

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• SciCrunch
• 13 years ago - by Anonymous

A database of oncogenes and tumor suppressor genes. Users can search by genes, chromosomes, and keywords. The coAnsensus domain analysis tool functions to identify conserved protein domains and GO terms among selected TAG genes, while the “oncogenic domain analysis” can analyze oncogenic potential of any user-provided protein based on a weighed term frequency table calculated from the TAG proteins. The completion of human genome sequences allows one to rapidly identify and analyze genes of interest through the use of computational approach. The available annotations including physical characterization and functional domains of known tumor-related genes thus can be used to study the role of genes involved in carcinogenesis. The tumor-associated gene (TAG) database was designed to utilize information from well-characterized oncogenes and tumor suppressor genes to facilitate cancer research. All target genes were identified through text-mining approach from the PubMed database. A semi-automatic information retrieving engine was built to collect specific information of these target genes from various resources and store in the TAG database. At current stage, 519 TAGs including 198 oncogenes, 170 tumor suppressor genes, and 151 genes related to oncogenesis were collected. Information collected in TAG database can be browsed through user-friendly web interfaces that provide searching genes by chromosome or by keywords. The “consensus domain analysis” tool functions to identify conserved protein domains and GO terms among selected TAG genes. In addition, the “oncogenic domain analysis” can analyze oncogenic potential of any user-provided protein based on a weighed term frequency table calculated from the TAG proteins. This study was supported by grant from National research program for genomic medicine (NRPGM) and personnel from Bioinformatics Center of Center for Biotechnology and Biosciences in the National Cheng Kung University, Taiwan.

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• SciCrunch
• 17 years ago - by Anonymous

The Human Adenovirus Type Classification coordinates the naming of candidate new types, prior to manuscript submission for peer review. This resource contains a method of submitting candidate HAdV, criteria for a new HAdV type, and a Serotyping tool, which displays all potential types corresponding to the query serotype entered by a user. The criteria are based on discussions at the International Adenovirus Meeting (Dobog��k, Hungary; 26-30 April, 2009) and the NIH Human Adenovirus Working Group Workshop (Bethesda, MD. USA; 3 February 2011), which are summarized in a Letter to the Editor.

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• SciCrunch
• 15 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 10, 2012. Cluster Assignment for Biological Inference (CLASSIFI) is a data-mining tool that can be used to identify significant co-clustering of genes with similar functional properties (e.g. cellular response to DNA damage). Briefly, CLASSIFI uses the Gene Ontology gene annotation scheme to define the functional properties of all genes/probes in a microarray data set, and then applies a cumulative hypergeometric distribution analysis to determine if any statistically significant gene ontology co-clustering has occurred. Platform: Online tool

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• SciCrunch
• 15 years ago - by Anonymous

Opasnet is a wiki-based website and workspace for helping societal decision making. The website collects, synthesizes, and distributes people''s values and scientific information. Opasnet welcomes anyone who wants to promote science-based decision-making in any field. The specialty is that the information is structured for both scientific scrutiny and for policy use at the same time. In practice, you can do original research, store data, make models, and perform policy assessments and discuss all of that work in one workspace. Originally, the developers of Opasnet came from the environmental health, i.e. a research field that studies the impacts of environment on human health. We are actively working, among other things, on climate change and air pollution, but you can also start a new assessment about a decision of your own interest, or participate in an existing assessment. Opasnet is a website that has basically two parts. One part is a wiki site (called Opasnet wiki or simply Opasnet) that has descriptive pages with text, figures, and tables; it also contains files. The other part is a database called Opasnet Base that contains quantitative estimates about anything that is described in Opasnet. The majority of information is openly available. However, both Opasnet wiki and Opasnet Base have a protected area for working with material that is non-public for some reason.

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• SciCrunch
• 15 years ago - by Anonymous

omniBiomarker is a web-application for analysis of high-throughput -omic data. Its primary function is to identify differentially expressed biomarkers that may be used for diagnostic or prognostic clinical prediction. Currently, omniBiomarker allows users to analyze their data with many different ranking methods simultaneously using a high-performance compute cluster. The next release of omniBiomarker will automatically select the most biologically relevant ranking method based on user input regarding prior knowledge. The omniBiomarker workflow * Data: Gene Expression * Algorithms: Knowledge-Driven Gene Ranking * Differentially expressed Genes * Clinical / Biological Validation * Knowledge: NCI Thesaurus of Cancer, Cancer Gene Index * back to Algorithms

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• SciCrunch
• 15 years ago - by Anonymous

Public research university in Louisville, Kentucky. It is part of the Kentucky state university system.

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• SciCrunch
• 13 years ago - submitted by Stephen Larson

A Cytoscape plug-in that visualizes the non-redundant biological terms for large clusters of genes in a functionally grouped network. It can be used in combination with GOlorize. The identifiers can be uploaded from a text file or interactively from a network of Cytoscape. The type of identifiers supported can be easily extended by the user. ClueGO performs single cluster analysis and comparison of clusters. From the ontology sources used, the terms are selected by different filter criteria. The related terms which share similar associated genes can be combined to reduce redundancy. The ClueGO network is created with kappa statistics and reflects the relationships between the terms based on the similarity of their associated genes. On the network, the node colour can be switched between functional groups and clusters distribution. ClueGO charts are underlying the specificity and the common aspects of the biological role. The significance of the terms and groups is automatically calculated. ClueGO is easy updatable with the newest files from Gene Ontology and KEGG. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

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• SciCrunch
• 15 years ago - by Anonymous

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• SciCrunch
• 17 years ago - submitted by Andrea Stagg

GraphWeb allows the detection of modules from biological, heterogeneous and multi-species networks, and the interpretation of detected modules using Gene Ontology, cis-regulatory motifs and biological pathways. GraphWeb is a public web server for graph-based analysis of biological networks that: * analyses directed and undirected, weighted and unweighted heterogeneous networks of genes, proteins and microarray probesets for many eukaryotic genomes; * integrates multiple diverse datasets into global networks; * incorporates multispecies data using gene orthology mapping; * filters nodes and edges based on dataset support, edge weight and node annotation; * detects gene modules from networks using a collection of algorithms; * interprets discovered modules using Gene Ontology, pathways, and cis-regulatory motifs. Platform: Online tool

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• SciCrunch
• 15 years ago - by Anonymous

SIMBioMS (System for Information Management in BioMedical Studies) is a multi-module solution for data management in biomedical studies. Any research concerning human samples and/or utilizing high-throughput technologies yields such amount of information that conventional data storage solution might not be sufficient. We offer here three software modules: * Sample Information Management System (SIMS), * Assay Information Management System (AIMS) * Sample avAILability system (SAIL) * Emanta Administration tool (Emanta) All three software modules were developed as a part of the integrated EU project MolPAGE (Molecular Phenotyping to Accelerate Genomic Epidemiology) and the collaborative research project ENGAGE (European Network of Genomic and Genetic Epidemiology). SIMS and AIMS can work either as united system or as two completely independent components. In turn, SAIL is an independent web-based system for indexing of phenotypes availability in different cohorts and collections. All systems are packaged in such a way that they can easily be installed either as local (e.g. on a laptop) or as centralized databases (to be used by a group of people). SIMS and AIMS benefit from customizable interface, editable vocabularies and a choice of options for tackling data confidentiality issues. The systems provides a user with efficient means of control over data exchange process and at the same time helps to format the metadata in compliance with the standards accepted in functional genomics. Since SIMBioMS is an open source project, source files can be downloaded and changed by the user if needed.

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• SciCrunch
• 15 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. Web-based and standalone application that returns a wide range of functional genomic data for a given set of study genes and provides rank scores for over-representation of particular functions or categories in the data. It uses the hypergeometric test statistic which returns statistically correct results for samples of all sizes and is the #2 fastest GO tool available (Khatri and Draghici, 2005). GeneMerge can be used with any discrete, locus-based annotation data, including, literature references, genetic interactions, mutant phenotypes as well as traditional Gene Ontology queries. GeneMerge is particularly useful for the analysis of microarray data and other large biological datasets. The big advantage of GeneMerge over other similar programs is that you are not limited to analyzing your data from the perspective of a pre-packaged set of gene-association data. You can download or create gene-association files to analyze your data from an unlimited number of perspectives. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

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• SciCrunch
• 15 years ago - by Anonymous

Public research university in Lafayette, Louisiana. It has the largest enrollment within the nine-campus University of Louisiana System and the second largest enrollment in Louisiana.

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• SciCrunch
• 12 years ago - submitted by Stephen Larson

ESTScan is a program that can detect coding regions in DNA sequences, even if they are of low quality. ESTScan will also detect and correct sequencing errors that lead to frameshifts. ESTScan is not a gene prediction program , nor is it an open reading frame detector. In fact, its strength lies in the fact that it does not require an open reading frame to detect a coding region. As a result, the program may miss a few translated amino acids at either the N or the C terminus, but will detect coding regions with high selectivity and sensitivity. ESTScan takes advantages of the bias in hexanucleotide usage found in coding regions relative to non-coding regions. This bias is formalized as an inhomogeneous 3-periodic fifth-order Hidden Markov Model (HMM). Additionally, the HMM of ESTScan has been extended to allows insertions and deletions when these improve the coding region statistics.

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• SciCrunch
• 15 years ago - by Anonymous

ZMM is a molecular modeling program for theoretical studies of systems of any complexity: small molecules, peptides, proteins, nucleic acids, and ligand-receptor complexes. ZMM searches optimal structures in the space of generalized coordinates: torsion angles, bond angles, bond lengths, positions free molecules and ions, and orientation of free molecules. Any generalized coordinate may be kept fixed. Molecules and fragments that are not expected to undergo significant conformational changes may be treated as rigid bodies. Popular molecular modeling programs usually work in the space of Cartesian coordinates of atoms. During energy minimization of a big system, many Cartesian coordinates-variables move collectively. For example, rotation of a benzene ring around the C-Ph bond in the Cartesian-coordinates space involves collective motion of 33 variables. In the generalized-coordinates space, this rotation involves variation of just one torsion angle. In ZMM, any fragment of a molecular system may be treated as either rigid or flexible. The generalized-coordinates method saves large computational resources if only a small part of a system is considered flexible. Examples are ligand-protein and protein-protein interactions. The savings occur because the sampling space is reduced and because molecular interactions within rigid fragments are not computed. * ZMM runs on Windows 95, 98, 2000, XP, UNIX, and Linux * ZMM can be used via the command-line interface * ZMM can also be used at Windows via a graphical user interface

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• SciCrunch
• 15 years ago - by Anonymous

ProteInOn calculates semantic similarity between GO terms or proteins annotated with GO terms. It also calculates term enrichment of protein sets, by applying a term representativity score, and gives additional information on protein interactions. The query compute protein semantic similarity returns the semantic similarity scores between all proteins entered, in matrix format. The option Measure allows users to choose one of several semantic similarity measures: Resnik, Lin, or Jiang & Conrath's measures with or without the DCA approach, plus the graph-based simUI and simGIC measures. These measures are listed by order of performance as evaluated with protein sequence similarity. The option GO type allows users to choose one of the aspects of GO: molecular function, biological process and cellular component. The option Ignore IEA limits the query to non-electronic annotations, excluding evidence types: IEA, NAS, ND, NR.

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• SciCrunch
• 15 years ago - by Anonymous

SocioPatterns is an interdisciplinary research collaboration that adopts data-driven methodology with the aim of uncovering fundamental patterns in social dynamics and coordinated human activity. To achieve its scientific goals, the SocioPatterns collaboration also contributes to the development of new technologies for collecting relevant data. In particular, the collaboration supports the development of the SocioPatterns sensing platform, which uses wireless wearable sensors to gather longitudinal data on human mobility and face-to-face proximity in real-world environments. The SocioPatterns team also works on developing tools and techniques to represent, analyze and visualize the collected data. We increasingly use digital media and computational devices in our daily activities, and leave behind a sizable amount of digital traces while doing so. The proliferation of mobile devices, and the incorporation of various sensing technologies in these devices, will further add to this growing trail of data. The possibility to mine and analyze these data, and the scale at which this can be done on contemporary computer systems, affords a novel, data-driven approach in the investigation of various aspects of human behavior. The following collection of datasets obtained through the SocioPatterns sensing platform are available: * Infectious SocioPatterns dynamic contact networks * Hypertext 2009 dynamic contact network * Primary school cumulative networks * Infectious SocioPatterns

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• SciCrunch
• 15 years ago - by Anonymous

FuSSiMeG is being discontinued, may not be working properly. Please use our new tool ProteinOn. Functional Semantic Similarity Measure between Gene Products (FuSSiMeG) provides a functional similarity measure between two proteins using the semantic similarity between the GO terms annotated with the proteins. Platform: Online tool

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• SciCrunch
• 15 years ago - by Anonymous

CateGOrizer takes batch input of GO term IDs in a list format or unformatted plain text file, allows users to choose one of the available classifications such as GO_slim, GOA, EGAD, MGI_GO_slim, GO-ROOT, or a self-defined classification list, find its parental branch and performs an accumulative classification count, and returns the results in a sorted table of counts, percentages, and a pie chart (if it takes longer than standard time out period, it will email the user with a URL link to the results). This tool is comprised with a set of perl CGI programs coupled with a MySQL DBMS that stores the GO terms DAG data. Platform: Online tool

• Resource
• SciCrunch
• 15 years ago - by Anonymous