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http://toppgene.cchmc.org/

ToppGene Suite is a one-stop portal for gene list enrichment analysis and candidate gene prioritization based on functional annotations and protein interactions network. ToppGene Suite is a one-stop portal for (i) gene list functional enrichment, (ii) candidate gene prioritization using either functional annotations or network analysis and (iii) identification and prioritization of novel disease candidate genes in the interactome. Functional annotation-based disease candidate gene prioritization uses a fuzzy-based similarity measure to compute the similarity between any two genes based on semantic annotations. The similarity scores from individual features are combined into an overall score using statistical meta-analysis.

Proper citation: ToppGene Suite (RRID:SCR_005726) 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://sourceforge.net/projects/bless-ec/

Software tool for Bloom-filter-based error correction for next-generation sequencing (NGS) reads. The algorithm produces accurate correction results with much less memory.

Proper citation: BLESS (RRID:SCR_005963) Copy   

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http://web.mit.edu/Edgerton/www/HighSpeed.html

The MIT Edgerton Center carries on the legacy of Doc Edgerton''s research and teaching by providing the Institute with a continuing expertise in high-speed and scientific imaging. Our facilities include a large studio space, a photographic darkroom, and a digital imaging studio equipped with an array of scanners, digital cameras, printers and plotters, and Macintosh computers. In addition, we have several technical digital cameras, including: * Redlake MASD PCI Motionscope, monochrome high-speed video at up to 8,000 images per second. * Concurrent analog data acquisition via a National Instruments A/D card. * Midas 2.0 motion analysis software from Xcitex, Inc. * NAC Model color high-speed camera (in process of donation). * Redlake MASD Ektapro 1012 high-speed video, monochrome, up to 12,000 images per second. * Redlake MASD Megaplus 1.4i scientific still camera These systems are available for use by interested MIT researchers and instructors, and by students pursuing hands-on projects. Each summer we offer a week-long course on high-speed imaging through the MIT Professional Institute. This subject (6.51s) is designed for scientists, engineers, and photographers who need to gather data on rapidly moving subjects and events for study, motion analysis, and trouble-shooting. Mornings are spent in the lecture hall learning the fundamentals for lighting, imaging technologies, and motion analysis. Afternoons are spent making high-speed images in the laboratory. For MIT students, we offer the popular Strobe Project Lab (6.163) to 24 students each term, where students learn the fundamentals of high-speed imaging and apply these techniques to final projects of their own choosing. Two subjects are offered that investigate digital imaging and image manipulation, SP.757 in Fall terms, and SP.747 in Spring terms.

Proper citation: Edgerton Center High Speed Imaging (RRID:SCR_005960) 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://freesurfer.net/fswiki/HippocampalSubfieldSegmentation

A software package for automatic segmentation of hippocampal subfields in magnetic resonance imges. Given a pair of T1-weighted and T2-weighted images (the latter acquired using a protocol tuned for hippocampus imaging), ASHS will automatically label main subfields of the hippocampus, and some extra-hippocampal structures, using multi-atlas segmentation. The main method is described in the Yushkevich et al. 2011 Neuroimage paper (http://tinyurl.com/cffrp3p). * execution requires: Advanced Normalization Tools, FSL

Proper citation: Segmentation of Hippocampus Subfields (RRID:SCR_005996) 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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http://purl.bioontology.org/ontology/RCTONT

Ontology specifically for Randomized Controlled Trials in order to facilitate the production of systematic reviews and metaanalysis.

Proper citation: Randomized Controlled Trials Ontology (RRID:SCR_005992) Copy   

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http://web.mit.edu/swg/software.htm

Toolbox for post-processing fMRI data. Includes software for comprehensive analysis of sources of artifacts in timeseries data including spiking and motion. Most compatible with SPM processing, but adaptable for FSL as well. * Operating System: MacOS, Windows, Linux * Programming Language: MATLAB * Supported Data Format: ANALYZE

Proper citation: Artifact Detection Tools (RRID:SCR_005994) 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://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://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.brain-map.org/api/index.html

API and demo application for accessing the Allen Brain Atlas Mouse Brain data. Data available via the API includes download high resolution images, expression data from a 3D volume, 3D coordinates of the Allen Reference Atlas, and searching genes with similar gene expression profiles using NeuroBlast. Data made available includes: * High resolution images for gene expression, connectivity, and histology experiments, as well as annotated atlas images * 3-D expression summaries registered to a reference space for the Mouse Brain and Developing Mouse Brain * Primary microarray results for the Human Brain and Non-Human Primate * RNA sequencing results for the Developing Human Brain * MRI and DTI files for Human Brain The API consists of the following resources: * RESTful model access * Image download service * 3-D expression summary download service * Differential expression search services * NeuroBlast correlative searches * Image-to-image synchronization service * Structure graph download service

Proper citation: Allen Brain Atlas API (RRID:SCR_005984) Copy   

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http://connectomes.utah.edu/

A web-compliant application that allows connectomics visualization by converting datasets to web-optimized tiles, delivering volume transforms to client devices, and providing groups of users with connectome annotation tools and data simultaneously via conventional internet connections. Viking is an extensible tool for connectomics analysis and is generalizable to histomics applications.

Proper citation: Viking Viewer for Connectomics (RRID:SCR_005986) Copy   

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http://www.unc.edu/~grwu/Software.html

A software plugin for 3D Slicer that matches morphological signatures of medical images automatically. HAMMER is an acronym for Hierarchical Attribute Matching Mechanism for Elastic Registration (Dinggang Shen, Christos Davatzikos, HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration, IEEE Trans. on Medical Imaging, 21(11):1421-1439, Nov 2002) - an elastic registration algorithm for medical images, matching morphological signatures of images in a hierarchical multi-scale regime. White matter lesion (WML) segmentation is a novel multi-spectral WML segmentation protocol via incorporating information from T1-w, T2-w, PD-w and FLAIR MR brain images. (Zhiqiang Lao, Dinggang Shen, Dengfeng Liu, Abbas F Jawad, Elias R Melhem, Lenore J Launer, Nick R Bryan, Christos Davatzikos, Computer-Assisted Segmentation of White Matter Lesions in 3D MR images, Using Pattern Recognition, Academic Radiology, 15(3):300-313, March 2008).

Proper citation: Hammer And WML Modules for 3D Slicer (RRID:SCR_005980) Copy   

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http://www.nitrc.org/projects/abc

A comprehensive processing pipeline developed and used at University of North Carolina and University of Utah for brain MRIs. The processing pipeline includes image registration, filtering, segmentation and inhomogeneity correction. The tool is cross-platform and can be run within 3D Slicer or as a stand-alone program. The image segmentation algorithm is based on the EMS software developed by Koen van Leemput.

Proper citation: ABC (Atlas Based Classification) (RRID:SCR_005981) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=clench

Cluster Enrichment (CLENCH) allows A. thaliana researchers to perform automated retrieval of GO annotations from TAIR and calculate enrichment of GO terms in gene group with respect to a reference set. Before calculating enrichment, CLENCH allows mapping of the returned annotations to arbitrary coarse levels using GO slim term lists (which can be edited by the user) and a local installation of GO. Platform: Windows compatible, Linux compatible,

Proper citation: CLENCH (RRID:SCR_005735) Copy   

•   Source: SciCrunch Registry