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http://www.patternlabforproteomics.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented July 5, 2018. Gene Ontology Explorer (GOEx) combines data from protein fold changes with GO over-representation statistics to help draw conclusions in proteomic experiments. It is tightly integrated within the PatternLab for Proteomics project and, thus, lies within a complete computational environment that provides parsers and pattern recognition tools designed for spectral counting. GOEx offers three independent methods to query data: an interactive directed acyclic graph, a specialist mode where key words can be searched, and an automatic search. A recent hack included in GOEx is to load the sparse matrix index file directly into GOEx, instead of going through the report generation using the AC/T-fold methods. This makes it easy for GOEx to analyze any list of proteins as long as the list follows the index file format (described in manuscript) . Please note that if using this alternative strategy, there will be no protein fold information. Platform: Windows compatible

Proper citation: GOEx - Gene Ontology Explorer (RRID:SCR_005779) Copy   

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https://www.applichem.com/home/

An Antibody supplier

Proper citation: AppliChem (RRID:SCR_005814) Copy   

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http://code.google.com/p/owltools/

OWLTools (aka OWL2LS - OWL2 Life Sciences) is a java API for accessing ontologies in either OBO or OWL. OWLTools provides a bio-ontologies friendly wrapper on top of the Manchester OWL API. It provides many features, including: * convenience methods for OBO-like properties such as synonyms, textual definitions, obsoletion, replaced_by * simple graph-like operations over ontologies * visualization using the QuickGO graphs libraries Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: OWLTools (RRID:SCR_005732) Copy   

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http://www.blipkit.org/

Biomedical Logical Programming (Blip) is a research-oriented deductive database and prolog application library for handling biological and biomedical data. It includes packages for advanced querying of ontologies and annotations. Blip underpins the Obol tool. Here are some distinguishing characteristics of Blip * Lightweight. Bloat-free: Blip only has as many modules as it needs to do its job. * Fast. * Declarative. Say what you want to do, not how you want to do it * Blip can be Query-oriented: specify your data sources and ask your query * Blip can be Application-oriented: it is designed to be used as an application library used by other bioinformatics tools * Mature and fully functional ontology module for handling both OBO-style ontologies and OWL ontologies. * Modules for handling biological sequences and sequence features. (currently limited functionality, added as needed) * A systems biology module for querying pathway and interaction data. (currently limited functionality, added as needed) * Relational database integration. SQL can be viewed as a highly restricted dialect of Prolog. Although the SWI-Prolog in-memory database is fast and scalable, sometimes it is nice to be able to fetch data from an external database. Blip contains a generic SQL utility module and predicate mappings for the GO database, Ensembl and Chado * Integration with a variety of bioinformatics file formats. SWI-Prolog has a variety of fast libraries for dealing with XML, RDF and tabular data files. Blip provides bridges from bio file formats encoded using these syntaxes into its native models. For other syntaxes, Blip seamlessly integrates other packages such as BioPerl and go-perl. Although these dependencies require extra installation, there is no point reinventing the wheel * Rapid development of web applications. Blip extends SWI-Prolog''''s excellent http support with a simple and powerful logical-functional-programming style application server, serval. This has been used to prototype a fully-featured next-generation replacement for the GO project amigo browser. * Scalable. Blip is not intended to be a toy system on toy data (although it is happy to be used as a toy if you like!). It is intended to be used as an application component and a tool operating on real-world biological and biomedical data Blip is written in SWI-Prolog, a fast, robust and scalable implementation of ISO Prolog. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Blip: Biomedical Logic Programming (RRID:SCR_005733) Copy   

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http://www.brainsmatter.com/

Welcome to the Brains Matter podcast where brains really do matter. A discussion of science, trivia, history, and general knowledge. The show started in September 2006, and includes discussion on various topics, as well as interviews with experts in their field. You can subscribe to the show via iTunes, a standard RSS reader, or listen to the individual MP3 shows from the ''flash player'' on the website, or direct download.

Proper citation: Brains Matter (RRID:SCR_005847) Copy   

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http://brainnetworks.sourceforge.net

Brain Networks: Code to perform network analysis on brain imaging data.

Proper citation: Brain Networks (RRID:SCR_005841) Copy   

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http://www.clo-ontology.org/

A community-driven ontology that is developed to standardize and integrate cell line information and support computer-assisted reasoning. Its focus is on permanent cell lines from culture collections. Upper ontology structures that frame the skeleton of CLO include Basic Formal Ontology and Relation Ontology. Cell lines contained in CLO are associated with terms from other ontologies such as Cell Type Ontology, NCBI Taxonomy, and Ontology for Biomedical Investigation. A common design pattern for the cell line is used to model cell lines and their attributes, the Jurkat cell line provides ane xample. Currently CLO contains over 36,000 cell line entries obtained from ATCC, HyperCLDB, Coriell, and bymanual curation. The cell lines are derived from 194 cell types, 656 anatomical entries, and 217 organisms. The OWL-based CLO is machine-readable and can be used in various applications. The CLO development has become a community effort with international collaborations. The development consortium includes experts from all over the world: the USA, Europe, and Japan.

Proper citation: Cell Line Ontology (RRID:SCR_005840) Copy   

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http://www2.unine.ch/

French-speaking university based in Neuchâtel, Switzerland. The university has four faculties and more than a dozen institutes, including arts and human sciences, natural sciences, law and economics.

Proper citation: University of Neuchatel; Neuchatel; Switzerland (RRID:SCR_005875) Copy   

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http://www.cancer.org/

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.

Proper citation: American Cancer Society (RRID:SCR_005756) Copy   

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http://crdd.osdd.net/raghava/ccpdb/

ccPDB (Compilation and Creation of datasets from PDB) is designed to provide service to scientific community working in the field of function or structure annoation of proteins. This database of datasets is based on Protein Data Bank (PDB), where all datasets were derived from PDB. ccPDB have four modules; i) compilation of datasets, ii) creation of datasets, iii) web services and iv) Important links. * Compilation of Datasets: Datasets at ccPDB can be classified in two categories, i) datasets collected from literature and ii) datasets compiled from PDB. We are in process of collecting PDB datasetsfrom literature and maintaining at ccPDB. We are also requesting community to suggest datasets. In addition, we generate datasets from PDB, these datasets were generated using commonly used standard protocols like non-redundant chains, structures solved at high resolution. * Creation of datasets: This module developed for creating customized datasets where user can create a dataset using his/her conditions from PDB. This module will be useful for those users who wish to create a new dataset as per ones requirement. This module have six steps, which are described in help page. * Web Services: We integrated following web services in ccPDB; i) Analyze of PDB ID service allows user to submit their PDB on around 40 servers from single point, ii) BLAST search allows user to perform BLAST search of their protein against PDB, iii) Structural information service is designed for annotating a protein structure from PDB ID, iv) Search in PDB facilitate user in searching structures in PDB, v)Generate patterns service facility to generate different types of patterns required for machine learning techniques and vi) Download useful information allows user to download various types of information for a given set of proteins (PDB IDs). * Important Links: One of major objectives of this web site is to provide links to web servers related to functional annotation of proteins. In first phase we have collected and compiled these links in different categories. In future attempt will be made to collect as many links as possible.

Proper citation: ccPDB - Compilation and Creation of datasets from PDB (RRID:SCR_005870) Copy   

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https://www.antigenix.com/

An Antibody supplier

Proper citation: Antigenix America (RRID:SCR_005871) Copy   

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http://utrdb.ba.itb.cnr.it/

UTRdb/UTRsite is a portal to other databases, including Nucleotide Sequence Databases, Protein Sequence Databases, other Sequence databanks, Untranslated Nucleotide Sequence Databases, Mitochondrial Databases, Mutation Databases, and others. The site also allows users to start long-term permanent projects or just to do quick searches, depending on the user''s needs.

Proper citation: UTRdb/UTRsite (RRID:SCR_005868) Copy   

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http://www.ici.upmc.fr/cluego/

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.

Proper citation: ClueGO (RRID:SCR_005748) Copy   

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http://daac.ornl.gov/citation_policy.html

Data Product Citation Policy of including a bibliographic citation for the products that were used in publications to acknowledge the scientists who have provided archived ORNL DAAC (Oak Ridge National Laboratory Distributed Active Archive Center) data products. Such citations will help others find the products and see how they have been used. Citation information is provided in the documentation that accompanies all data products. ORNL DAAC is operated by the ORNL Environmental Sciences Division and is responsible for data archival, product development and distribution, and user support for biogeochemical and ecological data and models. The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) for biogeochemcial dynamics is one of the NASA Earth Observing System Data and Information System (EOSDIS) data centers managed by the Earth Science Data and Information System (ESDIS) Project, which is responsible for providing scientific and other users access to data from NASA''s Earth Science Missions.

Proper citation: ORNL DAAC Data Product Citation Policy (RRID:SCR_005902) Copy   

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http://louisville.edu/

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

Proper citation: University of Louisville; Kentucky; USA (RRID:SCR_005749) Copy   

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http://estscan.sourceforge.net/

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.

Proper citation: ESTScan (RRID:SCR_005742) Copy   

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http://www.alzheimers.org/clinicaltrials/

A database of Alzheimer's disease and dementia clinical trials currently in progress at centers throughout the U.S.

Proper citation: AD Clinical Trials Database (RRID:SCR_005863) Copy   

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http://www.louisiana.edu/

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.

Proper citation: University of Louisiana; Louisiana; USA (RRID:SCR_005743) Copy   

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http://www.mellon.org/

The Andrew W. Mellon Foundation currently makes grants in five core program areas: * Higher Education and Scholarship * Scholarly Communications and Information Technology * Art History, Conservation, and Museums * Performing Arts * Conservation and the Environment Within each of its core programs, the Foundation concentrates most of its grantmaking in a few areas. Institutions and programs receiving support are often leaders in fields of Foundation activity, but they may also be promising newcomers, or in a position to demonstrate new ways of overcoming obstacles to achieve program goals. Our grantmaking philosophy is to build, strengthen and sustain institutions and their core capacities, rather than be a source for narrowly defined projects. As such, we develop thoughtful, long-term collaborations with grant recipients and invest sufficient funds for an extended period to accomplish the purpose at hand and achieve meaningful results.

Proper citation: Andrew W. Mellon Foundation (RRID:SCR_005864) Copy   

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http://simile.mit.edu/

SIMILE, a joint project conducted by the MIT Libraries and MIT CSAIL, was focused on developing robust, open source tools that empower users to access, manage, visualize and reuse digital assets. SIMILE seeks to enhance interoperability among digital assets, schemata/vocabularies/ontologies, metadata, and services. A key challenge is that the collections which must inter-operate are often distributed across individual, community, and institutional stores. We seek to be able to provide end-user services by drawing upon the assets, schemata/vocabularies/ontologies, and metadata held in such stores. SIMILE will leverage and extend DSpace, enhancing its support for arbitrary schemata and metadata, primarily though the application of RDF and semantic web techniques. The project also aims to implement a digital asset dissemination architecture based upon web standards. The dissemination architecture will provide a mechanism to add useful views to a particular digital artifact (i.e. asset, schema, or metadata instance), and bind those views to consuming services. To guide the SIMILE effort we will focus on well-defined, real-world use cases in the libraries domain. Since parallel work is underway to deploy DSpace at a number of leading research libraries, we hope that such an approach will lead to a powerful deployment channel through which the utility and readiness of semantic web tools and techniques can be compellingly demonstrated in a visible and global community. The SIMILE Project and its members are fully committed to the open source principles of software distribution and open development and for this reason, it releases the created intellectual property (both software and reports) under a BSD-style license. The SIMILE Project Team Members gladly welcome community efforts.

Proper citation: SIMILE (RRID:SCR_005862) Copy   

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