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http://images.nigms.nih.gov/

Database of scientific photos, illustrations, and videos made available by the National Institute of General Medical Sciences.

Proper citation: National Institute of General Medical Sciences Image Gallery (RRID:SCR_003480) Copy   

•   Source: SciCrunch Registry

https://compbio.uth.edu/FusionGDB2/

Functional annotation database of human fusion genes.FusionGDB 2.0 has updates of contents such as up-to-date human fusion genes, fusion gene breakage tendency score with FusionAI deep learning model based on 20 kb DNA sequence around BP, investigation of overlapping between fusion breakpoints with human genomic features across cellular role's categories, transcribed chimeric sequence and following open reading frame analysis with coding potential based on deep learning approach with Ribo-seq read features, and rigorous investigation of protein feature retention of individual fusion partner genes in protein level.

Proper citation: FusionGDB2 (RRID:SCR_025299) Copy   

•   Source: SciCrunch Registry

https://polymerscreen.yale.edu

Open access web app that allows users to search for optimal condition for extraction of membrane proteins into membrane active polymers which allows for retention of native membrane environment around target protein.

Proper citation: MAP Database Guide for Membrane Protein Solubilization (RRID:SCR_025656) Copy   

•   Source: SciCrunch Registry

https://endomap.hms.harvard.edu/

Structural interactome viewer. Interactive database of endosomal protein-protein interactions identified by cross-linking mass spectrometry and modeled by AlphaFold multimer. Structural protein interactome of human early endosomes.

Proper citation: EndoMap (RRID:SCR_026690) Copy   

•   Source: SciCrunch Registry

http://carbonyldb.missouri.edu/CarbonylDB/index.php/

Curated data resource of protein carbonylation sites.Manually curated data resource of experimentally confirmed carbonylated proteins and sites.Provides information on other related resources such as list of other oxidative protein modification databases, list of protein oxidation and carbonylation prediction tools.

Proper citation: CarbonylDB (RRID:SCR_023924) Copy   

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http://publications.nigms.nih.gov/computinglife/

An NIGMS magazine that showcases the exciting ways that scientists are using the power of computers to expand our knowledge of biology and medicine. From text messaging friends to navigating city streets with GPS technology, we''re all living the computing life. But as we''ve upgraded from snail mail and compasses, so too have scientists. Computer advances now let researchers quickly search through DNA sequences to find gene variations that could lead to disease, simulate how flu might spread through your school and design three-dimensional animations of molecules that rival any video game. By teaming computers and biology, scientists can answer new and old questions that could offer insights into the fundamental processes that keep us alive and make us sick. This booklet introduces you to just some of the ways that physicists, biologists and even artists are computing life. Each section focuses on a different research problem, offers examples of current scientific projects and acquaints you with the people conducting the work. You can follow the links for online extras and other opportunities to learn aboutand get involved inthis exciting new interdisciplinary field.

Proper citation: NIGMS Computing Life (RRID:SCR_005850) Copy   

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https://www.npatlas.org

Open access knowledge base for microbial natural products discovery. Database of microbially derived natural product structures. Provides coverage of bacterial and fungal natural products to visualize chemical diversity. Includes compounds and contains referenced data for structure, compound names, source organisms, isolation references, total syntheses, and instances of structural reassignment. Interactive web portal permits searching by structure, substructure, and physical properties. Provides mechanisms for visualizing natural products chemical space and dashboards for displaying author and discovery timeline data. Atlas has been developed under FAIR principles.

Proper citation: Natural Products Atlas (RRID:SCR_025107) Copy   

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http://www.mrmatlas.org/

Resource of targeted proteomics assays to detect and quantify proteins in complex proteome digests by mass spectrometry. Used to quantify the complete human proteome.

Proper citation: SRMAtlas (RRID:SCR_016996) Copy   

•   Source: SciCrunch Registry

http://www.zfishbook.org/NGP/journalcontent/SCORE/SCORE.html

Narrative resource describing a visual data analysis and collection approach that takes advantage of the cylindrical nature of the zebrafish allowing for an efficient and effective method for image capture called, Specimen in a Corrected Optical Rotational Enclosure (SCORE) Imaging. To achieve a non-distorted image, zebrafish were placed in a fluorinated ethylene propylene (FEP) tube with a surrounding, optically corrected imaging solution: water. By similarly matching the refractive index of the housing (FEP tubing) to that of the inner liquid and outer liquid (water), distortion was markedly reduced, producing a crisp imagable specimen that is able to be fully rotated 360 degrees. A similar procedure was established for fixed zebrafish embryos using convenient, readily available borosilicate capillaries surrounded by 75% glycerol. The method described could be applied to chemical genetic screening and other, related high-throughput methods within the fish community and among other scientific fields.

Proper citation: Zebrafish - SCORE Imaging: Specimen in a Corrected Optical Rotational Enclosure (RRID:SCR_001300) Copy   

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http://www.cellimagelibrary.org/

Freely accessible, public repository of vetted and annotated microscopic images, videos, and animations of cells from a variety of organisms, showcasing cell architecture, intracellular functionalities, and both normal and abnormal processes. Explore by Cell Process, Cell Component, Cell Type or Organism. The Cell includes images acquired from historical and modern collections, publications, and by recruitment.

Proper citation: Cell Image Library (CIL) (RRID:SCR_003510) Copy   

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http://www.reactome.org

Collection of pathways and pathway annotations. The core unit of the Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological interactions and are grouped into pathways (signaling, innate and acquired immune function, transcriptional regulation, translation, apoptosis and classical intermediary metabolism) . Provides website to navigate pathway knowledge and a suite of data analysis tools to support the pathway-based analysis of complex experimental and computational data sets.

Proper citation: Reactome (RRID:SCR_003485) Copy   

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http://www.minituba.org

miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. miniTUBA represents in a network view possible influences that occur between time varying variables in your dataset. For these networks of influence, miniTUBA predicts time courses of disease progression or response to therapies. minTUBA offers a probabilistic framework that is suitable for medical inference in datasets that are noisy. It conducts simulations and learning processes for predictive outcomes. The DBN analysis conducted by miniTUBA describes from variables that you specify how multiple measures at different time points in various variables influence each other. The DBN analysis then finds the probability of the model that best fits the data. A DBN analysis runs every combination of all the data; it examines a large space of possible relationships between variables, including linear, non-linear, and multi-state relationships; and it creates chains of causation, suggesting a sequence of events required to produce a particular outcome. Such chains of causation networks - are difficult to extract using other machine learning techniques. DBN then scores the resulting networks and ranks them in terms of how much structured information they contain compared to all possible models of the data. Models that fit well have higher scores. Output of a miniTUBA analysis provides the ten top-scoring networks of interacting influences that may be predictive of both disease progression and the impact of clinical interventions and probability tables for interpreting results. The DBN analysis that miniTUBA provides is especially good for biomedical experiments or clinical studies in which you collect data different time intervals. Applications of miniTUBA to biomedical problems include analyses of biomarkers and clinical datasets and other cases described on the miniTUBA website. To run a DBN with miniTUBA, you can set a number of parameters and constrain results by modifying structural priors (i.e. forcing or forbidding certain connections so that direction of influence reflects actual biological relationships). You can specify how to group variables into bins for analysis (called discretizing) and set the DBN execution time. You can also set and re-set the time lag to use in the analysis between the start of an event and the observation of its effect, and you can select to analyze only particular subsets of variables.

Proper citation: miniTUBA (RRID:SCR_003447) Copy   

•   Source: SciCrunch Registry

http://www.sbgn.org/Main_Page

The Systems Biology Graphical Notation (SBGN) project aims to develop high quality, standard graphical languages for representing biological processes and interactions. Each SBGN language is based on the consensus of the broad international SBGN community of biologists, curators and software developers. Over the course of its development many individuals, organizations and companies made invaluable contributions to the SBGN through participating in discussions and meetings, providing feedback on the documentation and worked examples, adopting the standard and spreading the word. Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling. A list of software packages known to provide (or have started to develop) support for SBGN notations is available.

Proper citation: Systems Biology Graphical Notation (RRID:SCR_004671) Copy   

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http://www.mcell.cnl.salk.edu/

Software modeling tool for realistic simulation of cellular signaling in complex 3-D subcellular microenvironment in and around living cells. Program that uses spatially realistic 3D cellular models and specialized Monte Carlo algorithms to simulate movements and reactions of molecules within and between cells.

Proper citation: MCell (RRID:SCR_007307) Copy   

•   Source: SciCrunch Registry

https://simtk.org/home/simtkcore

SimTK Core is one of the two packages that together constitute SimTK, the biosimulation toolkit from the Simbios Center. The other major component of SimTK is OpenMM which is packaged separately. This SimTK Core project collects together all the binaries needed for the various SimTK Core subprojects. These include Simbody, Molmodel, Simmath (including Ipopt), Simmatrix, CPodes, SimTKcommon, and Lapack. See the individual projects for descriptions. SimTK brings together in a robust, convenient, open source form the collection of highly-specialized technologies necessary to building successful physics-based simulations of biological structures. These include: strict adherence to an important set of abstractions and guiding principles, robust, high-performance numerical methods, support for developing and sharing physics-based models, and careful software engineering. Accessible High Performance Computing We believe that a primary concern of simulation scientists is performance, that is, speed of computation. We seek to build valid, approximate models using classical physics in order to achieve reasonable run times for our computational studies, so that we can hope to learn something interesting before retirement. In the choice of SimTK technologies, we are focused on achieving the best possible performance on hardware that most researchers actually have. In today''s practice, that means commodity multiprocessors and small clusters. The difference in performance between the best methods and the do-it-yourself techniques most people use can be astoundingeasily an order of magnitude or more. The growing set of SimTK Core libraries seeks to provide the best implementation of the best-known methods for widely used computations such as: Linear algebra, numerical integration and Monte Carlo sampling, multibody (internal coordinate) dynamics, molecular force field evaluation, nonlinear root finding and optimization. All SimTK Core software is in the form of C++ APIs, is thread-safe, and quietly exploits multiple CPUs when they are present. The resulting pre-built binaries are available for download and immediate use. Audience: Biosimulation application programmers interested in including robust, high-performance physics-based simulation in their domain-specific applications.

Proper citation: SimTKCore (RRID:SCR_008268) Copy   

•   Source: SciCrunch Registry

http://www.jneurosci.org/supplemental/18/12/4570/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 29, 2013. Supplemental data for the paper Changes in mitochondrial function resulting from synaptic activity in the rat hippocampal slice, by Vytautas P. Bindokas, Chong C. Lee, William F. Colmers, and Richard J. Miller that appears in the Journal of Neuroscience June 15, 1998. You can view digital movies of changes in fluorescence intensity by clicking on the title of interest.

Proper citation: Hippocampal Slice Wave Animations (RRID:SCR_008372) Copy   

•   Source: SciCrunch Registry

http://www.genenetwork.org/

Web platform that provides access to data and tools to study complex networks of genes, molecules, and higher order gene function and phenotypes. Sequence data (SNPs) and transcriptome data sets (expression genetic or eQTL data sets). Quantitative trait locus (QTL) mapping module that is built into GN is optimized for fast on-line analysis of traits that are controlled by combinations of gene variants and environmental factors. Used to study humans, mice (BXD, AXB, LXS, etc.), rats (HXB), Drosophila, and plant species (barley and Arabidopsis). Users are welcome to enter their own private data.

Proper citation: GeneNetwork (RRID:SCR_002388) Copy   

•   Source: SciCrunch Registry

https://simtk.org/home/contrack

An algorithm for identifying pathways that are known to exist between two regions within DTI data of anisotropic tissue, e.g., muscle, brain, spinal cord. The ConTrack algorithms use knowledge of DTI scanning physics and apriori information about tissue architecture to identify the location of connections between two regions within the DTI data. Assuming a course of connection or pathway between these two regions is known to exist within the measured tissue, ConTrack can be used to estimate properties of these connections in-vivo.

Proper citation: ConTrack (RRID:SCR_002681) Copy   

•   Source: SciCrunch Registry

http://diseasome.eu

A disease / disorder relationships explorer and a sample of a map-oriented scientific work. It uses the Human Disease Network dataset and allows intuitive knowledge discovery by mapping its complexity. The Human Disease Network (official) dataset, a poster of the data and related book (Biology - The digital era, ISBN: 978-2-271-06779-1) are available. This kind of data has a network-like organization, and relations between elements are at least as important as the elements themselves. More data could be integrated to this prototype and could eventually bring closer phenotype and genotype. Results should be visual, but also printable. Creating posters can enhance collaborative work. It facilitates discussion and sharing of ideas about the data. This website initiative is an invitation to think about the benefits of networks exploration but above all it tries to outline future designs of scientific information systems.

Proper citation: Diseasome (RRID:SCR_002792) Copy   

•   Source: SciCrunch Registry

http://www.pharmgkb.org/

Database and central repository for genetic, genomic, molecular and cellular phenotype data and clinical information about people who have participated in pharmacogenomics research studies. The data includes, but is not limited to, clinical and basic pharmacokinetic and pharmacogenomic research in the cardiovascular, pulmonary, cancer, pathways, metabolic and transporter domains. PharmGKB welcomes submissions of primary data from all research into genes and genetic variation and their effects on drug and disease phenotypes. PharmGKB collects, encodes, and disseminates knowledge about the impact of human genetic variations on drug response. They curate primary genotype and phenotype data, annotate gene variants and gene-drug-disease relationships via literature review, and summarize important PGx genes and drug pathways. PharmGKB is part of the NIH Pharmacogenomics Research Network (PGRN), a nationwide collaborative research consortium. Its aim is to aid researchers in understanding how genetic variation among individuals contributes to differences in reactions to drugs. A selected subset of data from PharmGKB is accessible via a SOAP interface. Downloaded data is available for individual research purposes only. Drugs with pharmacogenomic information in the context of FDA-approved drug labels are cataloged and drugs with mounting pharmacogenomic evidence are listed.

Proper citation: PharmGKB (RRID:SCR_002689) Copy   

•   Source: SciCrunch Registry