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http://www.cise.ufl.edu/~tichen/ShapeComplexAtlas.zip

A Matlab demo for constructing a neuro-anatomical shape complex atlas from 3D MRI brain structures, based on the paper Ting Chen, Anand Rangarajan, Stephan J. Eisenschenk and Baba C. Vemuri, Construction of a Neuroanatomical Shape Complex Atlas from 3D MRI Brain Structures. In NeuroImage, Volume 60, Page 1778-1787, 2012

Proper citation: ShapeComplexAtlas (RRID:SCR_002553) Copy   

•   Source: SciCrunch Registry

http://clovr.org/

A desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing.

Proper citation: CloVR (RRID:SCR_005290) Copy   

•   Source: SciCrunch Registry

http://total-impact.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on February 8, 2017. Service that aggregates altmetrics: diverse impacts from articles, datasets, blog posts, and more, to create a measure of the impact of scholarly output. * view metrics: Point to research products in Slideshare, GitHub, and Dryad. Import items from Google Scholar profiles or a BibTex file and the output is a metrics report that can be viewed and shared. * embed anywhere: Use the full-featured API to add metrics to projects. Or drop the embeddable Javascript widget into a publishing platform''s HTML. * Free - metrics data (and source code). They believe open altmetrics are key for building the coming era of Web-native science.

Proper citation: total impact.org (RRID:SCR_005952) Copy   

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https://github.com/raphael-group/chisel

Software tool to infer allele and haplotype specific copy numbers in individual cells from low coverage single cell DNA sequencing data. Integrates weak allelic signals across individual cells, powering strength of single cell sequencing technologies to overcome weakness. Includes global clustering of RDRs and BAFs, and rigorous model selection procedure for inferring genome ploidy that improves both inference of allele specific and total copy numbers.

Proper citation: CHISEL (RRID:SCR_023220) Copy   

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https://www.rosettacommons.org/home

Molecular modeling software package for 3D structure prediction and high resolution design of proteins, nucleic acids, and non natural polymers. Used in computational biology, including de novo protein design, enzyme design, ligand docking, and structure prediction of biological macromolecules and macromolecular complexes.

Proper citation: Rosetta (RRID:SCR_015701) Copy   

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http://animatlab.com/

A software tool that combines biomechanical simulation and biologically realistic neural networks to create realistic models of, and perform tests on, biomechanical workings. AnimatLab was primarily designed to model and test the operation of neural circuits that might produce behavior patterns observed in an intact animal. Users can create an animalistic or robotic body and place it in a virtual environment with physics that are accurate and realistic. Users can then design a nervous system that controls the behavior of the body within the physically realistic environment. Various models for different types of actions, builds, and movements are available.

Proper citation: AnimatLab (RRID:SCR_014252) Copy   

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https://github.com/KarrLab/de_sim

Software object oriented discrete event simulation tool for complex, data driven modeling. Open source, Python based object oriented discrete event simulation tool that makes it easy to use large, heterogeneous datasets and high level data science tools such as NumPy, Scipy, pandas, and SQLAlchemy to build and simulate complex computational models.

Proper citation: DE-Sim (RRID:SCR_018770) Copy   

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https://github.com/schatzlab/genomescope

Open source software package for fast genome analysis from unassembled short reads. Used to estimate genome heterozygosity, repeat content, and size from sequencing reads using a kmer-based statistical approach.

Proper citation: GenomeScope (RRID:SCR_017014) Copy   

•   Source: SciCrunch Registry

http://paleobiodb.org/

A non-governmental, non-profit public database for paleontological data providing researchers and the public with information about the entire fossil record. It has been organized and operated by a multi-disciplinary, multi-institutional, international group of paleobiological researchers. Its purpose is to provide global, collection-based occurrence and taxonomic data for organisms of all geological ages, as well data services to allow easy access to data for independent development of analytical tools, visualization software, and applications of all types. The Database's broader goal is to encourage and enable data-driven collaborative efforts that address large-scale paleobiological questions. Paleontological data files are accepted for upload. However, PaleoBioDB needs some basic data types to be included in order to perform an upload. The Application Programming Interface (API) gives scientists, students, and developers programmatic access to taxonomic, spatial, and temporal data contained within the database.

Proper citation: Paleobiology Database (RRID:SCR_003798) Copy   

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http://fold.it/

Foldit is a revolutionary new multiplayer online computer game that engages non-scientists in solving hard prediction problems, enabling you to contribute to important scientific research. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. Here are the basic principles to keep in mind when folding proteins. Your score on each protein is based on how well you do with these three things: # Pack the protein: The smaller the protein, the better. More precisely, you want to avoid empty spaces (voids) in the structure of the protein where water molecules can get inside. So you want the atoms in the protein to be as close together as possible. Certain structures, such as sheets, will even connect together with hydrogen bonds if you line them up right and get them close together. This is also good. Key word: Compact. # Hide the hydrophobics: Hydrophobics are the sidechains that don't want to be touching water, just like oil or wax. Since most proteins float around in water, you want to keep the hydrophobics (orange sidechains) surrounded by as many atoms as possible so the water won't get to them. The other side of this rule is that hydrophilics (blue sidechains) do want to be touching water, so they should be exposed as much as possible. Key word: Buried. # Clear the clashes: Two atoms can't occupy the same space at the same time. If you've folded a protein so two sidechains are too close together, your score will go down a lot. This is represented by a red spiky ball (clash) where the two sidechains are intersecting. If there are clashes, you know something is wrong with your protein. So make sure everything is far enough apart. Key word: Apart. The current series of Science Puzzles, the Grand Challenges, are meant to generate the evidence needed to prove that human protein folders can be more effective than computers at certain aspects of protein structure prediction. That's what all the puzzles in Foldit are about right now: predicting the structure of a protein based on its amino acid sequence. The three rules mentioned above describe the characteristics of correct protein structures.

Proper citation: Foldit (RRID:SCR_003788) 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   

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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.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   

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http://rankprop.gs.washington.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented on May,18,2020. Ranking algorithm that exploits global network structure of similarity relationships among proteins in database by performing diffusion operation on protein similarity network with weighted edges. Source code and web server for searching non-redundant protein database. Web server ranks proteins found in NRDB40 (from PairsDB) against query sequence of amino acids using Rankprop algorithm.

Proper citation: Rankprop - Protein Ranking by Network Propagation (RRID:SCR_007159) Copy   

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http://krasnow1.gmu.edu/cn3/index3.html

Multidisciplinary research team devoted to the study of basic neuroscience with a specific interest in the description and generation of dendritic morphology, and in its effect on neuronal electrophysiology. In the long term, they seek to create large-scale, anatomically plausible neural networks to model entire portions of a mammalian brain (such as a hippocampal slice, or a cortical column). Achievements by the CNG include the development of software for the quantitative analysis of dendritic morphology, the implementation of computational models to simulate neuronal structure, and the synthesis of anatomically accurate, large scale neuronal assemblies in virtual reality. Based on biologically plausible rules and biophysical determinants, they have designed stochastic models that can generate realistic virtual neurons. Quantitative morphological analysis indicates that virtual neurons are statistically compatible with the real data that the model parameters are measured from. Virtual neurons can be generated within an appropriate anatomical context if a system level description of the surrounding tissue is included in the model. In order to simulate anatomically realistic neural networks, axons must be grown as well as dendrites. They have developed a navigation strategy for virtual axons in a voxel substrate.

Proper citation: Computational Neuroanatomy Group (RRID:SCR_007150) Copy   

•   Source: SciCrunch Registry

http://organelledb.lsi.umich.edu/

Database of organelle proteins, and subcellular structures / complexes from compiled protein localization data from organisms spanning the eukaryotic kingdom. All data may be downloaded as a tab-delimited text file and new localization data (and localization images, etc) for any organism relevant to the data sets currently contained in Organelle DB is welcomed. The data sets in Organelle DB encompass 138 organisms with emphasis on the major model systems: S. cerevisiae, A. thaliana, D. melanogaster, C. elegans, M. musculus, and human proteins as well. In particular, Organelle DB is a central repository of yeast protein localization data, incorporating results from both previous and current (ongoing) large-scale studies of protein localization in Saccharomyces cerevisiae. In addition, we have manually curated several recent subcellular proteomic studies for incorporation in Organelle DB. In total, Organelle DB is a singular resource consolidating our knowledge of the protein composition of eukaryotic organelles and subcellular structures. When available, we have included terms from the Gene Ontologies: the cellular component, molecular function, and biological process fields are discussed more fully in GO. Additionally, when available, we have included fluorescent micrographs (principally of yeast cells) visualizing the described protein localization. Organelle View is a visualization tool for yeast protein localization. It is a visually engaging way for high school and undergraduate students to learn about genetics or for visually-inclined researchers to explore Organelle DB. By revealing the data through a colorful, dimensional model, we believe that different kinds of information will come to light.

Proper citation: Organelle DB (RRID:SCR_007837) Copy   

•   Source: SciCrunch Registry

http://www.fgsc.net/

The Fungal Genetics Stock Center is a resource available to the Fungal Genetics research community and to educational and research organizations in general. While some fungi can cause disease in humans, most people have innate immunity against fungi. Some people with diseases of the immune system are at increased risk of infection by fungi. Drugs have been developed in the last 5 years that help with this. Fungal Genetics is the study of genes and genetic traits in fungi. In the past this has been important in the elucidation of what a gene is, what the genetic material is, how genes relate to enzymes, how enzymes relate to traits and how important traits change or evolve. In the present, Fungal Genetics is important to understanding how fungi are pathogens of plants and animals, how fungi can be used in industry for the production of enzymes, chemicals, food, and drugs. Fungi are also essential to processing bio-mass in the attempt to use ethanol as a fuel source. The FGSC is funded largely by a grant from the National Science Foundation (Award Number 0235887) of the United States of America. Sponsors: Supported by a grant from the National Science Foundation.

Proper citation: Fungal Genetics Stock Center (RRID:SCR_008143) Copy   

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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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://wings-workflows.org

A software application which assists scientists with designing computational experiments. WINGS is a semantic workflow system which incorporates semantic constraints about datasets and workflow components into its workflow representations. The workflow system has an open modular design and can be easily integrated with other existing workflow systems and execution frameworks to extend them with semantic reasoning capabilities. WINGS also allows users to express high-level descriptions of their analysis goals, and assists them by automatically and systematically generating possible workflows that are consistent with that request. In cases where privacy or off-line use are important, WINGS can submit workflows in a scripted format for execution in the local host. It uses Pegasus or OODT as the execution engine for large-scale distributed workflow execution.

Proper citation: WINGS (RRID:SCR_013997) Copy   

•   Source: SciCrunch Registry