Tools   Select Another Resource Report Type

http://www.utrome.org

This database is intended as a comprehensive resource for UTR (Untranslated Region) biology in C. elegans. The database provides detailed information on UTR structures for all protein-coding mRNAs, and includes annotations extracted from other databases (such as WormBase and PicTar) as well as new annotations generated as part of the NYU UTRome project (including preliminary characterization of UTR clones, USTs (UTR sequence tags), curated sequences, and computational and experimental analysis of functional elements). Examples of functional elements within UTRs include predicted and validated microRNA (miRNA) binding sites (responsible for post-transcriptional gene regulation), putative consensus signals for polyA addition, and predicted secondary structures (which may influence the biological activity of UTRs). The UTRome project is part of the ModEncode Consortium, an NIH initiative to characterize at a genomic scale functional sequence elements encoded in the worm (C. elegans) and fly (D. melanogaster) genomes. UTRs are important portions of mRNAs required for post-transcriptional regulation by interacting with proteins or non-coding RNAs (e.g. microRNAs). To study the role of UTRs we are building a UTR database for C. elegans.

Proper citation: UTRome.org (RRID:SCR_005878) Copy   

•   Source: SciCrunch Registry

http://webclu.bio.wzw.tum.de/profcom/

Profiling of Complex Functionality (ProfCom) is a web-based tool for the functional interpretation of a gene list that was identified to be related by experiments. A trait which makes ProfCom a unique tool is an ability to profile enrichments of not only available Gene Ontology (GO) terms but also of complex function. A complex function is constructed as Boolean combination of available GO terms. The complex functions inferred by ProfCom are more specific in comparison to single terms and describe more accurately the functional role of genes. Platform: Online tool

Proper citation: ProfCom - Profiling of complex functionality (RRID:SCR_005797) Copy   

•   Source: SciCrunch Registry

http://roar.eprints.org/

Listing of institutional repositories for depositing preprints of published materials with the aim of promoting the development of open access by providing timely information about the growth and status of repositories throughout the world. Open access to research maximizes research access and thereby also research impact, making research more productive and effective. Repository Types: * Research Institutional or Departmental * Research Multi-institution Repository * Research Cross-Institutional * e-Journal/Publication * e-Theses * Database/A&I Index * Research Data * Open and Linked Data * Learning and Teaching Objects * Demonstration * Web Observatory * Other Repository Software: * ARNO * Bepress * CDS Invenio * ContentDM by OCLC * DIGIBIB * DigiTool * DiVA * DoKS * DSpace * EDOC * EPrints * Equella * ETD-db * Fedora ** Fez * Greenstone * HAL * i-Tor * IntraLibrary * Keystone DLS * MiTOS * MyCoRe * Open Journal System * Open Repository * OPUS (Open Publications System) * Other softwares (various) * PMB Services * SBCAT * SciX * SobekCM * WIKINDX * Zentity

Proper citation: ROAR (RRID:SCR_005951) Copy   

•   Source: SciCrunch Registry

http://gst.ornl.gov/

We are the Computational Biology and Bioinformatics Group of the Biosciences Division of Oak Ridge National Laboratory. We conduct genetics research and system development in genomic sequencing, computational genome analysis, and computational protein structure analysis. We provide bioinformatics and analytic services and resources to collaborators, predict prospective gene and protein models for analysis, provide user services for the general community, including computer-annotated genomes in Genome Channel. Our collaborators include the Joint Genome Institute, ORNL''s Computer Science and Mathematics Division, the Tennessee Mouse Genome Consortium, the Joint Institute for Biological Sciences, and ORNL''s Genome Science and Technology Graduate Program.

Proper citation: Computational Biology at ORNL (RRID:SCR_005710) Copy   

•   Source: SciCrunch Registry

http://estbioinfo.stat.ub.es/apli/serbgov131/index.php

SerbGO is a web-based tool intended to assist researchers determine which microarray tools for gene expression analysis which make use of the GO ontologies are best suited to their projects. SerbGO is a bidirectional application. The user can ask for some features by checking on the Query Form to get the appropriate tools for their interests. The user can also compare tools to check which features are implemented in each one. Platform: Online tool

Proper citation: SerbGO (RRID:SCR_005798) Copy   

•   Source: SciCrunch Registry

http://www.youtube.com/user/NIGMS/

YouTube videos provided by the National Institute of General Medical Sciences (NIGMS).

Proper citation: NIGMS - YouTube (RRID:SCR_005678) Copy   

•   Source: SciCrunch Registry

http://pidfinder.appspot.com/

PIDFinder is a tool for the exploration of the Primary Immunodeficiency Disease Ontology. Apart from browsing the knowledge contained in the ontology, it can also be used for the identification of PIDs based on a set of observed Phenotypes. The PidFinder web application is a developing prototype application that allows non-bioinformaticians to quickly view and use the knowledge contained in the Primary Immunodeficiency Disease Ontology. The application consists of a number of components: * The PIDFinder: allows the selection of a set of phenotypes and subsequently compares the set with the canonical set of phenotypes defined in the PID Ontology. The phenotypes, that can be selected are grouped by biomarker and are thus available in a number of different facets. Once phenotypes have been selected, the application compares them to canonical phenotypes associated with PIDs in the PID Ontology, by computing a semantic similarity measure. The similarity is determined using a Tanimoto Distance - the more closely related an observed phenotype is to a canonical ontology phenotype, the closer the calculated Tanimoto Distance is to 1 - with increasing dissimilarity, the Tanimoto Coefficient tends towards 0. * The Phenotype Explorer: a rudimentary browser for phenotypes currently contained in the PID Ontology. The browser allows the user to find phenotypes based on biomarker categories and provides some basic definitions (not all definitions are available at this stage) and disease association information. * A Heatmap comparing the phenotypic overlap of PIDs: In essence, the heatmap is a many-to-many comparison of the phenotypic overlap between all Primary Immunodeficiency Diseases contained in the PID Ontology. Again, overlap is calculated using a Tanimoto Distance. The heatmap is a matrix, plotting the Tanimoto coefficients for every PID/PID pair. Increased off-diagonal overlap between PIDs most likely indicates genes in the same pathway. * A Phenotype Frequency Visualization: The phenotype frequency visualization is a simple bar chart indicating how often a particular phenotype is associated with a Primary Immunodeficiency Disease in the Ontology. * A PID Expert map: All of the phenotypes and knowledge contained in the Primary Immunodeficiency Disease Ontology has been extracted from primary clinical or research literature. To construct the map, we have extracted the affiliations and locations of the authors of the literature sources and overlayed them on a map. The hope is that this will facilitate the identification of (local) experts on primary immunodeficiency diseases.

Proper citation: PIDFinder (RRID:SCR_005833) Copy   

•   Source: SciCrunch Registry

http://tolweb.org/tree/

A collection of information about biodiversity compiled collaboratively by hundreds of expert and amateur contributors. Its goal is to contain a page with pictures, text, and other information for every species and for each group of organisms, living or extinct. Connections between Tree of Life web pages follow phylogenetic branching patterns between groups of organisms, so visitors can browse the hierarchy of life and learn about phylogeny and evolution as well as the characteristics of individual groups.

Proper citation: Tree of Life Web Project (RRID:SCR_005673) Copy   

•   Source: SciCrunch Registry

http://www.compbio.dundee.ac.uk/gotcha/gotcha.php

GOtcha provides a prediction of a set of GO terms that can be associated with a given query sequence. Each term is scored independently and the scores calibrated against reference searches to give an accurate percentage likelihood of correctness. These results can be displayed graphically. Why is GOtcha different to what is already out there and why should you be using it? * GOtcha uses a method where it combines information from many search hits, up to and including E-values that are normally discarded. This gives much better sensitivity than other methods. * GOtcha provides a score for each individual term, not just the leaf term or branch. This allows the discrimination between confident assignments that one would find at a more general level and the more specific terms that one would have lower confidence in. * The scores GOtcha provides are calibrated to give a real estimate of correctness. This is expressed as a percentage, giving a result that non-experts are comfortable in interpreting. * GOtcha provides graphical output that gives an overview of the confidence in, or potential alternatives for, particular GO term assignments. The tool is currently web-based; contact David Martin for details of the standalone version. Platform: Online tool

Proper citation: GOtcha (RRID:SCR_005790) Copy   

•   Source: SciCrunch Registry

http://xldb.fc.ul.pt/biotools/rebil/goa/

A tool for assisting the GO annotation of UniProt entries by linking the GO terms present in the uncurated annotations with evidence text automatically extracted from the documents linked to UniProt entries. Platform: Online tool

Proper citation: GoAnnotator (RRID:SCR_005792) Copy   

•   Source: SciCrunch Registry

https://www.facebase.org/node/252

THIS RESOURCE IS NO LONGER IN SERVICE,documented on January,18, 2022. FaceBase Biorepository is now collecting biological samples from people with cleft lip/palate and their family members. Information for Prospective Cases: Clefts of the lip and/or palate can be caused by a wide range of genetic, environmental and other factors. The FaceBase Biorepository will serve as a common source of both biological samples and information that can be made available to investigators trying to determine the underlying cause of these common birth defects. Genetic studies, in particular, will benefit from both family history information and having samples from affected individuals as well as their family members. DNA is the information containing molecules found in all the cells of our body and can be easily obtained from material such as blood or saliva samples. As part of the FaceBase Biorepository, we are requesting families to submit biological samples from specific family members as well as information from other family members that might be affected with either the same condition or a similar condition. The medical and family history information that is collected includes other relevant information such as exposure to possible environmental causes during pregnancy. The biorepository is managed by Nichole Nidey, a research study coordinator, and Jeff Murray, a pediatric clinical geneticist and researcher. They are available to speak with family members regarding questions they may have, including providing information about the biorepository and making arrangements for the collection of samples for those who wish to participate. All participation is voluntary. Your name or other personally identifiable information (name, address, etc) will be removed before information is placed in the biorepository. Summary data to show how the database itself has been used overall as well as updates on whether specific findings might have been made using this database will be available on the FaceBase website at www.facebase.org. A newsletter containing this information will also be given to families and referring clinicians so that they may discuss the specifics with the families if there appears to be information that might be relevant in a particular case. Families will also need to sign a consent form that has been approved by the Institutional Review Board at the University of Iowa. Also, any submitted samples or data can also be removed from the database at any time should the family no longer wish to participate. Investigators interested in requesting DNA samples or for more information, please contact cleftresearch (at) uiowa.edu, Nichole Nidey, nichole-nidey (at) uiowa.edu or (319) 353-4365, or Jeff Murray, jeff-murray (at) uiowa.edu.

Proper citation: FaceBase Biorepository (RRID:SCR_006001) Copy   

•   Source: SciCrunch Registry

http://gopubmed.org/web/gopubmed/

A web server which allows users to explore PubMed search results with the Gene Ontology, a hierarchically structured vocabulary for molecular biology. GoPubMed submits a user''''s keywords to PubMed, retrieves the abstracts, detects Gene Ontology terms in the abstracts, displays the subset of Gene Ontology relevant to the original query, and allows the user to browse through the ontology displaying associated papers and their GO annotation. Platform: Online tool

Proper citation: GoPubMed (RRID:SCR_005823) Copy   

•   Source: SciCrunch Registry

http://agbase.msstate.edu/cgi-bin/tools/goslimviewer_select.pl

Service to summarize the GO function associated with a data set using prepared GO Slim sets. The input is a tab separated list of gene product IDs and GO IDs.

Proper citation: GOSlimViewer (RRID:SCR_005665) Copy   

•   Source: SciCrunch Registry

http://pipeclip.qbrc.org/

A Galaxy framework-based online pipeline for reliable analysis of data generated by three types of CLIP-seq protocols: HITS-CLIP, PAR-CLIP and iCLIP. It provides both data processing and statistical analysis to determine candidate cross-linking regions, which are comparable to those regions identified from the original studies or using existing computational tools., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PIPE-CLIP (RRID:SCR_005820) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/expressionprofiler/

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. The EP:GO browser is built into EBI's Expression Profiler, a set of tools for clustering, analysis and visualization of gene expression and other genomic data. With it, you can search for GO terms and identify gene associations for a node, with or without associated subnodes, for the organism of your choice.

Proper citation: Expression Profiler (RRID:SCR_005821) Copy   

•   Source: SciCrunch Registry

http://www.snubi.org/software/GOChase/

GOChase is a set of web-based utilities to detect and correct the errors in GO-based annotations. # GOChase-History resolves the whole modification history of GO IDs. # GOChase-Correct highlights merged GO IDs and redirects to the correct primary term into which the secondary ID was merged. For obsolete GO terms, the nearest non-discarded parent term is recommended by GOChase. This function may be used by GO browsers such as AmiGO and QuickGO to fix broken hyperlinks. # A whole database (such as LocusLink) as a flat file can be loaded into GOChase, reporting the annotation errors and GOChase corrections. # When one inputs a GO ID, GOChase will resolve all gene products annotated with the GO ID across all the major databases. Platform: Online tool

Proper citation: GOChase (RRID:SCR_005822) Copy   

•   Source: SciCrunch Registry

http://nmr.cmbi.ru.nl/NRG-CING/HTML/index.html

NRG-CING presents a complete validation report for all 9,000+ wwPDB NMR entries including remediated experimental data such as chemical shifts from BMRB and restraints from NRG . These CING reports are compiled from internal analyses and those by CCPN, DSSP, PROCHECK-NMR/Aqua, ShiftX, Talos+, Vasco, Wattos, and WHAT_CHECK. The NRG-CING website is a collection of CING reports that has been pre-calculated for all PDB files solved by NMR. (See website for more information on CING.) In case the underlying experimental data is available, these have been cleaned up and made syntactically and semantically correct and homogeneous. For many macromolecular NMR ensembles from the Protein Data Bank (PDB) the experiment-based restraint lists used in the structure calculation are accessible, while other experimental data, mainly chemical shift values, are often available from the BioMagResBank. Assessment of the quality of the structural result is paramount to their usage and a combined, integrated repository of both input data and structural results greatly facilitates such an analysis. In addition, the accuracy and precision of the coordinates in these macromolecular NMR ensembles can be improved by recalculations using the available experimental data and present-day software with improved protocols and force fields. Such efforts, however, generally fail on over half of all deposited structures due to the syntactic and semantic heterogeneity of the data and the wide variety of formats used for their deposition. We have combined the cleaned-up restraints information from the NMR Restraints Grid (NRG) database with available chemical shifts from the BioMagResBank in the weekly updated NRG-CING database. Eleven programs, in addition to CING itself, have been included in the NRG-CING production pipeline to arrive at validation reports that list for each entry the potential inconsistencies between the coordinates and the available restraint and chemical shift data. The longitudinal validation of this data yielded a set of indicators that can be used to judge the quality of every macromolecular structure solved with NMR. The cleaned up NMR experimental datasets and the validation reports are freely available.

Proper citation: NRG-CING (RRID:SCR_006079) Copy   

•   Source: SciCrunch Registry

http://www.yeastgenome.org/cgi-bin/GO/goSlimMapper.pl

The GO Slim Mapper (aka GO Term Mapper) maps the specific, granular GO terms used to annotate a list of budding yeast gene products to corresponding more general parent GO slim terms. Uses the SGD GO Slim sets. Three GO Slim sets are available at SGD: * Macromolecular complex terms: protein complex terms from the Cellular Component ontology * Yeast GO-Slim: GO terms that represent the major Biological Processes, Molecular Functions, and Cellular Components in S. cerevisiae * Generic GO-Slim: broad, high level GO terms from the Biological Process and Cellular Component ontologies selected and maintained by the Gene Ontology Consortium (GOC) Platform: Online tool

Proper citation: SGD Gene Ontology Slim Mapper (RRID:SCR_005784) Copy   

•   Source: SciCrunch Registry

http://yh.genomics.org.cn

This database presents the entire DNA sequence of the first diploid genome sequence of a Han Chinese, a representative of Asian population. The genome, named as YH, represents the start of YanHuang Project, which aims to sequence 100 Chinese individuals in 3 years. It was assembled based on 3.3 billion reads (117.7Gbp raw data) generated by Illumina Genome Analyzer. In total of 102.9Gbp nucleotides were mapped onto the NCBI human reference genome (Build 36) by self-developed software SOAP (Short Oligonucleotide Alignment Program), and 3.07 million SNPs were identified. The personal genome data is illustrated in a MapView, which is powered by GBrowse. A new module was developed to browse large-scale short reads alignment. This module enabled users track detailed divergences between consensus and sequencing reads. In total of 53,643 HGMD recorders were used to screen YH SNPs to retrieve phenotype related information, to superficially explain the donor's genome. Blast service to align query sequences against YH genome consensus was also provided.

Proper citation: YanHuang Project (RRID:SCR_006077) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/uniparc/

Database that contains publicly available protein sequences with stable and unique identifiers (UPI) which are never removed, changed or reassigned. UniParc tracks sequence changes in the source databases and archives the history of all changes. Information other than protein sequence must be retrieved from the UniParc source databases using the database cross-references.

Proper citation: UniParc (RRID:SCR_005818) Copy   

•   Source: SciCrunch Registry