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http://www.compneurosci.com/CoSMo2012/

This unique summer school focuses on computational techniques integrating the multi-disciplinary nature of sensory-motor neuroscience through combined empirical-theoretical teaching modules and a focus on the use of databases of movement data (NSF CRCNS). Major breakthroughs in brain research have been achieved through computational models. The goal of the Summer School in Computational Sensory-Motor Neuroscience is to provide cross-disciplinary training in mathematical modelling techniques relevant to understanding brain function, dysfunction and treatment. In a unique approach bridging experimental research, clinical pathology and computer simulations, students will learn how to translate ideas and empirical findings into mathematical models. Students will gain a profound understanding of the brain''s working principles and diseases using advanced modelling techniques in hands-on simulations of models during tutored sessions by making use of data / model sharing. This summer school aims at propelling promising students into world-class researchers. Dates: August 5-19, 2012 Location: Northwestern University Chicago (Evanston campus), Illinois, USA Deadlines: * April 22, 2012: Application due, including letters of reference (extended!!!) * May 1, 2012: Notification of acceptance * May 20, 2012: Attendance confirmation of applicants and registration payment This summer school is directed at graduate students and post-doctoral fellows from multi-disciplinary backgrounds, including Life Sciences, Psychology, Computer Science, Mathematics and Engineering. We will also accept highly motivated outstanding under-graduate students. There are no formal prerequisites, but basic knowledge in calculus, linear algebra, neuroscience and the Matlab simulation environment is expected. Enrollment will be limited to 40 participants.

Proper citation: Summer School in Computational Sensory-Motor Neuroscience (RRID:SCR_006894) Copy   

•   Source: SciCrunch Registry

http://www.softpedia.com/get/Science-CAD/DynGO.shtml

DynGO is a client-server application that provides several advanced functionalities in addition to the standard browsing capability. DynGO allows users to conduct batch retrieval of GO annotations for a list of genes and gene products, and semantic retrieval of genes and gene products sharing similar GO annotations (which requires more disk and memory to handle the semantic retrieval). The result are shown in an association tree organized according to GO hierarchies and supported with many dynamic display options such as sorting tree nodes or changing orientation of the tree. For GO curators and frequent GO users, DynGO provides fast and convenient access to GO annotation data. DynGO is generally applicable to any data set where the records are annotated with GO terms, as illustrated by two examples. Requirements: Java Platform: Windows compatible, Linux compatible, Unix compatible

Proper citation: DynGO (RRID:SCR_007009) Copy   

•   Source: SciCrunch Registry

http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:Jackal

Jackal is a collection of programs designed for the modeling and analysis of protein structures. Its core program is a versatile homology modeling package. It contains twelve individual programs, each with their own function.

Proper citation: Jackal (RRID:SCR_008665) Copy   

•   Source: SciCrunch Registry

http://pslid.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented August 23, 2017.

Annotated database of fluorescence microscope images depicting subcellular location proteins with two interfaces: a text and image content search interface, and a graphical interface for exploring location patterns grouped into Subcellular Location Trees. The annotations in PSLID provide a description of sample preparation and fluorescence microscope imaging.

Proper citation: Protein Subcellular Location Image Database (RRID:SCR_008663) Copy   

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http://iubio.bio.indiana.edu:8089/

Provides summary of gene and genomic information from eukaryotic organism databases. This includes gene symbol and full name, chromosome, genetic and molecular map information, Gene Ontology (Function/Location/Process) and gene homology, product information, links to extended gene information.

Proper citation: Eukaryote Genes (RRID:SCR_008617) Copy   

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http://bioinformatics.engineering.asu.edu/springs/Sprouts/index.html

SPROUTS is a database of predicted protein folding related data. It was designed to gather all the results from a study concerning the comparison between tools devoted to the prediction of stability changes upon point mutations. The second aim of this database is to offer simple and user-friendly tools to better visualize and analyze the results obtained. We are now able to propose three ways of visualization and analysis: the first one consists in getting raw Delta Delta G values in a table. The second one is a 2D graph representation of a computed stability score for each residue of a given sequence and for each tool. The last one is based on a Jmol applet (Jmol) with the possibility to represent the 3D structure of a given protein with symbols representing the information stored in the database. We assume that each visualization mode offers a different look on the data stored in the database and will suit to every scientists willing to query the database whether they are more used to handle 3D protein structure or 1D/2D sequence problems. Finally, the ultimate objective is to integrate these data and their analysis with other structural bioinformatic concepts in order to improve other methods that may be related to this concept. We are currently working at adding the information extracted from our other projects related to the prediction of protein folding nucleus in order to obtain a meta server devoted to the characterization of the folding core of proteins. As of today, this database has grown up and consists in more than 100 structures which have been computed for a total of around 16500 amino acids.

Proper citation: SPROUTS- Structural Prediction for Protein Folding Utility System (RRID:SCR_005118) Copy   

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http://research.cs.wisc.edu/wham/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. High-throughput sequence alignment tool that aligns short DNA sequences (reads) to the whole human genome at a rate of over 1500 million 60bps reads per hour, which is one to two orders of magnitudes faster than the leading state-of-the-art techniques. Feature list for the current version (v 0.1.5) of WHAM: * Supports paired-end reads * Supports up to 5 errores * Supports alignments with gaps * Supports quality scores for filtering invalid alignments, and sorting valid alignments * finds ALL valid alignments * Supports multi-threading * Supports rich reporting modes * Supports SAM format output

Proper citation: WHAM (RRID:SCR_005497) Copy   

•   Source: SciCrunch Registry

http://www.musicianbrain.com/#index

The human brain has the remarkable ability to adapt in response to changes in the environment over the course of a lifetime. This is the mechanism for learning, growth, and normal development. Similar changes or adaptations can also occur in response to focal brain injuries, e.g., partially-adapted neighboring brain regions or functionally-related brain systems can either substitute for some of the lost function or develop alternative strategies to overcome a disability. Through ongoing research, the Music and Neuroimaging Laboratory''s mission is to: * Reveal the perceptual and cognitive aspects of music processing including the perception and memory for pitch, rhythmic, harmonic, and melodic stimuli. * Investigate the use of music and musical stimuli as an interventional tool for educational and therapeutic purposes. * Reveal the behavioral and neural correlates of learning, skill acquisition, and brain adaptation in response to changes in the environment or brain injury in the developing and adult brain. * Reveal the determinants and facilitators for recovery from brain injury. Project topics include: Aphasia Therapy, Singing and Speaking, Tone Deafness / Congenital Amusia, Motor Recovery Studies, Music and Emotions, Music and Autism, Children and Music Making, Brain Stimulation, Adult Musician Studies, Absolute Pitch Studies, Acute Stroke Studies

Proper citation: Music and Neuroimaging Laboratory (RRID:SCR_005447) Copy   

•   Source: SciCrunch Registry

http://cs.nyu.edu/~bingsun/

NYU Bioinformatics group applies algorithmic, statistical, and mathematical techniques to solve problems of interest to biology, biotechnology and biomedicine. The group focuses on bioinformatics, computational biology and systems biology with many active projects in areas ranging from single molecules to entire populations: Analysis of Single-Molecule/Single-Cell Data, SPM-based Transcriptomic Profiling, Whole-Genome Haplotype Sequencing using SMASH (Single Molecule Approaches to Haplotype Sequencing), SUTTA (Scoring and Unfolding Trimmed Tree Assembler) assembly algorithm, Analysis of Spatio-Temporal Data, Model Checking and Model Building for Systems Biology, GOALIE-based Phenomenological Models and their Verification, Causality Analysis, Causal Models and their Verification, Analysis of EHR (Electronic Health Record Data) and Disease Models (e.g., Chronic Fatigue Syndrome, Congestive Heart Failure, Deep Vein Thrombosis, etc.), Models of Cancer, Applications to Pancreatic Cancer, Polymorphisms and Biomarkers, Strategies for Group Testing, Epidemiological and Bio-Warfare Models, Planning with Large Agent Networks against Catastrophes (PLAN C), Population Genomics, and Genome Wide Association Studies (GWAS). The group has received its funding from Air Force, Army, CCPR, DARPA, NIH, NIST, NSF, NYSTAR, etc. and various other governmental and commercial entities. Currently, the group is part of an NSF funded Expedition in Computing project (CMACS: Center for Modeling and Analysis of Complex Systems at CMU) and collaborates widely, both nationally and internationally. The group is highly multi-disciplinary, attracting researchers and students from mathematics, statistics, computer science, and biology who team up with physicians, physicists, and chemists as well as professionals in their own disciplines. This group is led by Prof. Bud Mishra, a professor of computer science and mathematics at NYU''s Courant Institute of Mathematical Sciences.

Proper citation: NYU Bioinformatics Group (RRID:SCR_005697) Copy   

•   Source: SciCrunch Registry

http://www.nescent.org/

The National Evolutionary Synthesis Center (NESCent) is a nonprofit science center dedicated to cross-disciplinary research in evolution. NESCent promotes the synthesis of information, concepts and knowledge to address significant, emerging, or novel questions in evolutionary science and its applications. NESCent achieves this by supporting research and education across disciplinary, institutional, geographic, and demographic boundaries. Synthetic research in evolutionary science takes many forms but includes integrating novel data sets and models to address important problems within a discipline, developing new analytical approaches and tools, and combining methods and perspectives from multiple disciplines to answer and even create new fundamental scientific questions. NESCent facilitates such synthetic research by providing an environment for fertile interactions among scientists. Our Science and Synthesis program sponsors postdoctoral fellows and sabbatical scholars as resident scientists, and two kinds of meetings, working groups and catalysis meetings. Catalysis meetings provide a novel mechanism for bringing together diverse research communities and cultures to identify common interests, while working groups provide an opportunity for scientists to work together intensively on fundamental synthetic questions over a several-year period. These activities are community driven through our application process and evaluated by an external advisory board. Our Informatics program provides state of the art informatics tools to visiting and in-house scientists and aims to take the lead in assembling novel databases and developing new analytical tools for evolutionary biology. Finally it is sponsoring a major initiative to provide a digital data repository for work in evolutionary biology. NESCent''s Education and Outreach group communicates the results of evolutionary biology research to the general public and scientific community, provides outreach to groups who are underrepresented in evolutionary biology and works to improve evolution education.

Proper citation: NESCent - National Evolutionary Synthesis Center (RRID:SCR_005911) Copy   

•   Source: SciCrunch Registry

https://datadryad.org

International, curated, digital repository that makes the data underlying scientific publications discoverable, freely reusable, and citable. Particularly data for which no specialized repository exists. Provides the infrastructure for, and promotes the re-use of, data underlying the scholarly literature. Governed by a nonprofit membership organization. Membership is open to any stakeholder organization, including but not limited to journals, scientific societies, publishers, research institutions, libraries, and funding organizations. Most data are associated with peer-reviewed articles, although data associated with non-peer reviewed publications from reputable academic sources, such as dissertations, are also accepted. Used to validate published findings, explore new analysis methodologies, repurpose data for research questions unanticipated by the original authors, and perform synthetic studies.UC system is member organization of Dryad general subject data repository.

Proper citation: Dryad Digital Repository (RRID:SCR_005910) Copy   

•   Source: SciCrunch Registry

http://virome.diagcomputing.org/#view=home

A web-application designed for scientific exploration of metagenome sequence data collected from viral assemblages occurring within a number of different environmental contexts. The VIROME informatics pipeline focuses on the classification of predicted open-reading frames (ORFs) from viral metagenomes. The portal allows you to submit your viral metagenome to be processed through the VIROME analysis pipeline, and enable you to investigate your data via the VIROME user interface., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: VIROME (RRID:SCR_004362) Copy   

•   Source: SciCrunch Registry

http://vamps.mbl.edu/overview.php

A publicly-accessible website to measure and visualize similarities and differences between molecular profiles of complex microbial communities. The project includes visualization tools such as heat maps that simultaneously compare the taxonomic distributions of multiple datasets and 3-D charts of the frequency distributions of 16S rRNA tags. Analytical tools include Chao diversity estimates and rarefaction curves. As a service to the community, researchers have the opportunity to upload their own data to the site for private viewing with the full range of data and analysis tools. Public data can be downloaded for further analysis locally.

Proper citation: VAMPS (RRID:SCR_004483) Copy   

•   Source: SciCrunch Registry

http://www.arabidopsis.org

Database of genetic and molecular biology data for the model higher plant Arabidopsis thaliana. Data available includes the complete genome sequence along with gene structure, gene product information, metabolism, gene expression, DNA and seed stocks, genome maps, genetic and physical markers, publications, and information about the Arabidopsis research community. Gene product function data is updated every two weeks from the latest published research literature and community data submissions. Gene structures are updated 1-2 times per year using computational and manual methods as well as community submissions of new and updated genes. TAIR also provides extensive linkouts from data pages to other Arabidopsis resources. The data can be searched, viewed and analyzed. Datasets can also be downloaded. Pages on news, job postings, conference announcements, Arabidopsis lab protocols, and useful links are provided.

Proper citation: TAIR (RRID:SCR_004618) Copy   

•   Source: SciCrunch Registry

http://www.aftol.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented Jan 13, 2022; To enhance the understanding of the evolution of the Kingdom Fungi, 1500+ species were sampled for eight gene loci across all major fungal clades, plus a subset of taxa for a suite of morphological and ultrastructural characters with resulting data: AFTOL Molecular Database (generated by WASABI - Web Accessible Sequence Analysis for Biological Inference), Blast search the AFTOL Database (generated by WASABI), AFTOL primers (generated by WASABI), AFTOL primers by species (generated by WASABI), AFTOL alignments, and the AFTOL Structural and Biochemical Database. Users may submit samples to the AFTOL project. AFTOL is a collaboration centered around four universities in the United States: Duke University (Francois Lutzoni and Rytas Vilgalys), Clark University (David Hibbett), Oregon State University (Joey Spatafora), and University of Minnesota (David McLaughlin). Participants throughout the world have donated vouchers, taxon samples, and gene sequences. The aim of the project is to reconstruct the fungal tree of life using all available data for eight loci (nuclear ribosomal DNA: LSU, SSU, ITS (including 5.8s, ITS1 and ITS2); RNA polymerase II: RPB1, RPB2; elongation factor 1-alpha; mitochondrial SSU rDNA, and mitochondrial ATP synthase protein subunit 6). A further objective of this study is to summarize and integrate current knowledge regarding fungal subcellular features within this new phylogenetic framework. The name of the bioinformatic package developed for AFTOL is WASABI which provides an efficient communication platform to facilitate the collection and dissemination of molecular data to (and from) the laboratories and participants. All molecular data can be viewed, downloaded, verified, and corrected by the participants of AFTOL. A central goal of the WASABI interface is to establish an automated analysis framework that includes basecalling of newly generated chromatograms, contig assembly, quality verification of sequences (including a local BLAST), sequence alignment, and congruence test. Gene sequences that pass all tests and are finally verified by their authors will undergo automated phylogenetic analysis on a regular schedule. Although all steps are initially carried out noninteractively, the users can verify and correct the results at any step and thus initiate the reanalysis of dependent data.

Proper citation: AFTOL (RRID:SCR_004650) Copy   

•   Source: SciCrunch Registry

http://www.stanford.edu/group/brainsinsilicon/neurogrid.html

A specialized hardware platform that will perform cortex-scale emulations while offering software-like flexibility. With sixteen 12x14 sq-mm chips (Neurocores) assembled on a 6.5x7.5 sq-in circuit board that can model a slab of cortex with up to 16x256x256 neurons - over a million! The chips are interconnected in a binary tree by 80M spike/sec links. An on-chip RAM (in each Neurocore) and an off-chip RAM (on a daughterboard, not shown) softwire vertical and horizontcal cortical connections, respectively. It provides an affordable option for brain simulations that uses analog computation to emulate ion-channel activity and uses digital communication to softwire synaptic connections. These technologies impose different constraints, because they operate in parallel and in serial, respectively. Analog computation constrains the number of distinct ion-channel populations that can be simulatedunlike digital computation, which simply takes longer to run bigger simulations. Digital communication constrains the number of synaptic connections that can be activated per secondunlike analog communication, which simply sums additional inputs onto the same wire. Working within these constraints, Neurogrid achieves its goal of simulating multiple cortical areas in real-time by making judicious choices.

Proper citation: Neurogrid (RRID:SCR_005024) Copy   

•   Source: SciCrunch Registry

http://www.datamonkey.org/

Web-based suite of phylogenetic analysis tools for use in evolutionary biology. Web application for comparative analysis of sequence alignments using statistical models. Used for analyzing evolutionary signatures in sequence data. Datamonkey 2.0 provides curated collection of methods for interrogating coding-sequence alignments for imprints of natural selection, packaged as a responsive (i.e. can be viewed on tablet and mobile devices), fully interactive, and API-enabled web application.

Proper citation: Datamonkey (RRID:SCR_010278) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/InterNano

A custom-built terminology to describe the nanomanufacturing enterprise.

Proper citation: InterNano Nanomanufacturing Taxonomy (RRID:SCR_010346) Copy   

•   Source: SciCrunch Registry

http://www.aoncadis.org

A repository and data management services for Arctic research data. Data include long-term observational timeseries, local, regional, and system-scale research from many diverse domains.

Proper citation: ACADIS Gateway (RRID:SCR_010473) Copy   

•   Source: SciCrunch Registry

http://evolution.genetics.washington.edu/phylip/software.html

392 phylogeny software packages and 54 free web servers describing all known software for inferring phylogenies (evolutionary trees). Submissions are welcome. Programs are listed by methods available, by computer systems on which they work, cross-referenced by method and by computer system, by ones which analyze particular kinds of data, to show the most recent listings, or to show ones most recently changed.

Proper citation: Phylogeny Programs (RRID:SCR_010797) Copy   

•   Source: SciCrunch Registry