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http://emboss.sourceforge.net/apps/cvs/embassy/index.html#DOMAINATRIX

Software for protein domain search. It is a part of Embassy software package.

Proper citation: DOMAINATRIX (RRID:SCR_016084) Copy   

•   Source: SciCrunch Registry

http://cudasw.sourceforge.net/

CUDASW++ is a bioinformatics software for Smith-Waterman protein database searches that takes advantage of the massively parallel CUDA architecture of NVIDIA Tesla GPUs to perform sequence searches 10x-50x faster than NCBI BLAST. In this algorithm, we deeply explore the SIMT (Single Instruction, Multiple Thread) and virtualized SIMD (Single Instruction, Multiple Data) abstractions to achieve fast speed. This algorithm has been fully tested on Tesla C1060, Tesla C2050, GeForce GTX 280 and GTX 295 graphics cards, and has been incorporated to NVIDIA Tesla Bio Workbench. * Operating System: Linux * Programming language: CUDA and C * Other requirements: CUDA SDK and Toolkits 2.0 or higher

Proper citation: CUDASW++ (RRID:SCR_008862) Copy   

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https://circinteractome.nia.nih.gov/

Web tool for exploring circular RNAs and their interacting proteins and microRNAs. Predicts the miRNAs which can potentially target the circRNA.

Proper citation: Circular RNA Interactome (RRID:SCR_016304) Copy   

•   Source: SciCrunch Registry

http://mapman.gabipd.org/web/guest/mapman

Software tool that displays large genomics datasets (e.g. gene expression data from Arabidopsis Affymetrix arrays) onto diagrams of metabolic pathways or other biological processes.

Proper citation: MapMan (RRID:SCR_003543) Copy   

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https://sites.google.com/a/blueprint.org/trades/

With Trajectory Directed Ensemble Sampling (TraDES) create large ensembles of high-quality protein structures quickly, ranging from near-native to partially unfolded to intrinsically unfolded. TraDES is a system for directly controlling and sampling protein conformational space. TraDES has been previously used for measuring the vastness of protein conformational space and testing the hypothesis of a brute force solution to the protein folding problem. Over 10 Billion protein structures have been produced by TraDES software in previous distributed computing experiments. The package is comprised of binary executable programs and accessory programs and scripts as well as protein structure data files that map out protein conformational space in a probabilistic way. The main programs are: * trades - generates protein structures following the Trajectory Distribution (see below) * seq2trj - makes Trajectory Distributions from sequences for sampling * str2tr - makes Trajectory Distributions from 3D structures for sampling Trajectory Distributions - Controlling the Sampling of Conformational Space The concept of the trajectory distribution may be new to many protein scientists. A trajectory distribution is simply a map of available conformational space at an amino acid residue. NMR scientists are the primary users of the TraDES package.

Proper citation: TraDES (RRID:SCR_006142) Copy   

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http://www.ncbi.nlm.nih.gov/projects/homology/maps/

This page provides quick access to the Comparative mapping functions available in the Map Viewer. Currently, comparative maps are calculated using HomoloGene orthology predictions. Once the gene pairs have been established, blocks of conserved syteny can be established using the positions of each gene object in their respective builds. Sponsors: This resource is supported by NCBI.

Proper citation: Homology Maps Page (RRID:SCR_001666) Copy   

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https://physiomeproject.org/

The Physiome Project is a worldwide public domain effort to provide a computational framework for understanding human and other eukaryotic physiology. It aims to develop integrative models at all levels of biological organization, from genes to the whole organism via gene regulatory networks, protein pathways, integrative cell function, and tissue and whole organ structure/function relations. Additionally, an important goal of the project is to develop applications for teaching physiology. Current projects include the development of: - ontologies to organize biological knowledge and access to databases - markup languages to encode models of biological structure and function in a standard format for sharing between different application programs and for re-use as components of more comprehensive models - databases of structure at the cell, tissue and organ levels - software to render computational models of cell function such as ion channel electrophysiology, cell signaling and metabolic pathways, transport, motility, the cell cycle, etc. in 2 & 3D graphical form - software for displaying and interacting with the organ models which will allow the user to move across all spatial scales Sponsors: This project is supported by the International Union of Physiological Sciences (IUPS), the IEEE Engineering. in Medicine and Biology (EMBS), and the International Federation for Medical and Biological Engineering (IFMBE)

Proper citation: International Union of Physiological Sciences: Physiome Project (RRID:SCR_001760) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.albany.edu/~dmaps

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 6, 2016. DMAPS database contains pre-computed multiple structure alignments for protein chains in the Protein Data Bank (PDB). Automated structure alignments have been generated for classified protein families using CE-MC algorithm. Alignments have been built only for those families with at least three members. Currently, multiple structure alignments are available for 3050 SCOP-, 3087 CATH-, 664 ENZYME- and 1707 CE-based families. Users will be able to retrieve multiple alignments for a given PDB chain classified by one of these criteria.

Proper citation: DMAPS - A Database of Multiple Alignments for Protein Structures (RRID:SCR_007140) Copy   

•   Source: SciCrunch Registry

http://bioinfo.pl/

This service offers a gateway to well-benchmarked protein structure and function prediction methods. Structural models collected from the prediction servers are assessed using the powerful 3D-jury consensus approach. The Structure Prediction Meta Server provides access to various fold recognition, function prediction and local structure prediction methods. The Server takes the amino acid sequence of the query protein, the reference name for the prediction job, and the E-mail address as input. The E-mail address is used only for notification about errors during the execution of the job. The query sequence and the reference name are placed in the process queue. The Meta Server accepts only sequences, which have not been submitted before. In case of duplicate sequences the second user will be notified with a link to the previous submission. Sequences longer than 800 amino acids are not accepted by some services. The internal SQL database offers the possibility to find any previous jobs processed by the Meta Server using regular expressions addressing field like E-mail, Job Name and the host name, from which the job was initiated. Each server has its own process queuing system managed by the Meta Server. All results of fold recognition servers are translated into uniform formats. The information extracted from the raw output of the servers includes the PDB codes of the hits, the alignments and the similarity (reliability) scores specific for every server. Mapping of the hits to the SCOP and FSSP classifications are made either using known PDB representatives or alignment of the template sequence with the databases of proteins in both classifications. The secondary structure assignments for all hits are taken from the mapped FSSP (red for helices and blue for strands). Underscored amino acids indicate the first residue after an insertion in the template sequence. The Meta server provides translation of the alignments in standard formats like FASTA, PDB or CASP. The Meta Server is coupled to consensus servers. They provide jury predictions based on the results collected from other services. Not all fold recognition servers are used by the jury system. The data stored on the meta server is available through http://meta.bioinfo.pl/data/JOBID/. Jobs older than 2 months are not shown. The Meta Server is only a set of programs aimed to process and manage biological data, while the predictive power of the service comes from (mostly) remote prediction providers. Sponsors: This resource is supported by The BioInfoBank Institute.

Proper citation: BioInfoBank Meta Server (RRID:SCR_007181) Copy   

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http://autodock.scripps.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 23,2023.Software for automated docking analysis to precalculate the set of grids describing the target protein. It is a part of automated molecular modeling simulation software AutoDock.

Proper citation: Autogrid (RRID:SCR_015982) Copy   

•   Source: SciCrunch Registry

https://omicssimla.sourceforge.io

Software tool for generating multi omics data with disease status. Simulates genomics (SNPs and copy number variations), epigenomics ( whole genome bisulphite sequencing), transcriptomics ( RNA seq), and proteomics (normalized reverse phase protein array) data at the whole genome level. Available as desktop and web application version.

Proper citation: OmicsSIMLA (RRID:SCR_017011) Copy   

•   Source: SciCrunch Registry

http://brainarray.mbni.med.umich.edu/Brainarray/Database/ProbeMatchDB/ncbi_probmatch_para_step1.asp

Matches a list of microarray probes across different microrarray platforms (GeneChip, EST from different vendors, Operon Oligos) and species (human, mouse and rat), based on NCBI UniGene and HomoloGene. The capability to match protein sequence IDs has just been added to facilitate proteomic studies. The ProbeMatchDB is mainly used for the design of verification experiments or comparing the microarray results from different platforms. It can be used for finding equivalent EST clones in the Research Genetics sequence verified clone set based on results from Affymetirx GeneChips. It will also help to identify probes representing orthologous genes across human, mouse and rat on different microarray platforms.

Proper citation: ProbeMatchDB 2.0 (RRID:SCR_003433) Copy   

•   Source: SciCrunch Registry

http://prosite.expasy.org/

Database of protein families and domains that is based on the observation that, while there is a huge number of different proteins, most of them can be grouped, on the basis of similarities in their sequences, into a limited number of families. Proteins or protein domains belonging to a particular family generally share functional attributes and are derived from a common ancestor. It is complemented by ProRule, a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids. ScanProsite finds matches of your protein sequences to PROSITE signatures. PROSITE currently contains patterns and profiles specific for more than a thousand protein families or domains. Each of these signatures comes with documentation providing background information on the structure and function of these proteins. The database is available via FTP.

Proper citation: PROSITE (RRID:SCR_003457) Copy   

•   Source: SciCrunch Registry

http://www.thesgc.org/

Charity registered in United Kingdom whose mission is to accelerate research in new areas of human biology and drug discovery.Not for profit, public-private partnership that carries out basic science of relevance to drug discovery whose core mandate is to determine 3D structures on large scale and cost effectively targeting human proteins of biomedical importance and proteins from human parasites that represent potential drug targets.

Proper citation: Structural Genomics Consortium (RRID:SCR_003890) Copy   

•   Source: SciCrunch Registry

http://www.wholecellkb.org/

A collection of free, open-source model organism databases designed specifically to enable comprehensive, dynamic simulations of entire cells and organisms. WholeCellKB provides comprehensive, quantitative descriptions of individual species including: * Their subcellular organization, * Their chromosome sequences, * The essentiality, location, length, direction, and homologs of each gene, * The organization and promoter of each transcription unit, * The expression and degradation rate of each RNA gene product, * The specific folding and maturation pathway of each RNA and protein species including the localization, N-terminal cleavage, signal sequence, prosthetic groups, disulfide bonds, and chaperone interactions of each protein species, * The subunit composition of each macromolecular complex, * Their genetic code, * The binding sites and footprint of every DNA-binding protein, * The structure, charge, and hydrophobicity of every metabolite, * The stoichiometry, catalysis, coenzymes, energetics, and kinetics of every chemical reaction, * The regulatory strength of each transcription factor on each promoter, * Their chemical composition, and * The composition of its typical SP-4 laboratory growth medium. WholeCellKB currently contains a single database of Mycoplasma genitalium, an extremely small gram-positive bacterium and common human pathogen. This database is the most comprehensive description of any single organism to date, and was used to develop the first whole-cell computational model. Users can download the WholeCellKB source code and content to create and customize - including the content, data model, and user interface - their own model organism database.

Proper citation: WholeCellKB (RRID:SCR_004104) Copy   

•   Source: SciCrunch Registry

http://www.pharma-planta.net/

Consortium to develop efficient and safe strategies for the production of clinical-grade protein pharmaceuticals in plants, and to define the procedures needed for the production of these proteins in compliance with the strict regulatory standards that govern the manufacture of all pharmaceuticals. Ultimately the consortium aimed to take a candidate product all the way through the development pipeline culminating in a phase I human clinical trial. The consortium has a wide range of expertise spanning the areas of molecular biology, plant biology, immunology, recombinant protein expression technology, vaccinology, and plant biotechnology. The objectives listed at the beginning of the Pharma-Planta project are as follows: # To produce a recombinant pharmaceutical molecule in transgenic plants, which will be developed through all regulatory requirements, GMP (good manufacturing practice) standards and pre-clinical toxicity testing. This will then be evaluated in Phase I human clinical trials. # To develop robust risk assessment practices for recombinant pharmaceutical molecules produced in plants, based on health and environmental impact, working with regulatory authorities within the EU as well as public groups to ensure that the production systems are as safe and as acceptable as possible, and that they comply with all biosafety regulations. # To define and carry out a coordinated program for securing and managing intellectual property that will facilitate the availability of high priority plant-derived recombinant pharmaceuticals to the poor in developing countries while simultaneously allowing the products to be developed commercially in Europe and North America. # To develop and refine new strategies for the expression of recombinant pharmaceuticals in plants, which can be used on a generic basis for molecules that are normally expressed poorly. # To develop and generate transgenic plants expressing a second generation of recombinant molecules that will be used in future clinical trials. In 2011 they reached their benchmark for success launching a phase I clinical study of an antibody that neutralizes HIV, produced in and isolated from tobacco plants. This antibody could one day become an inexpensive component of a microbicide used to prevent the spread of HIV/AIDS. The project has also spun off many additional technologies that are being adopted by researchers all over the world, and has resulted in more than 100 publications in peer-reviewed scientific journals.

Proper citation: Pharma-Planta Consortium (RRID:SCR_003880) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/pdbe/

The European resource for the collection, organization and dissemination of data on biological macromolecular structures. In collaboration with the other worldwide Protein Data Bank (wwPDB) partners - the Research Collaboratory for Structural Bioinformatics (RCSB) and BioMagResBank (BMRB) in the USA and the Protein Data Bank of Japan (PDBj) - they work to collate, maintain and provide access to the global repository of macromolecular structure data. The main objectives of the work at PDBe are: * to provide an integrated resource of high-quality macromolecular structures and related data and make it available to the biomedical community via intuitive user interfaces. * to maintain in-house expertise in all the major structure-determination techniques (X-ray, NMR and EM) in order to stay abreast of technical and methodological developments in these fields, and to work with the community on issues of mutual interest (such as data representation, harvesting, formats and standards, or validation of structural data). * to provide high-quality deposition and annotation facilities for structural data as one of the wwPDB deposition sites. Several sophisticated tools are also available for the structural analysis of macromolecules.

Proper citation: PDBe - Protein Data Bank in Europe (RRID:SCR_004312) Copy   

•   Source: SciCrunch Registry

http://www.science.mcmaster.ca/biochem/faculty/truant/truantlab.htm

THIS RESOURCE IS NO LONGER IN SERVICE, documented on March 21, 2013. Laboratory portal of Ray Truant, PhD. It provides an image gallery and videos.

Proper citation: Ray Truant Lab (RRID:SCR_004301) Copy   

•   Source: SciCrunch Registry

http://blast.ncbi.nlm.nih.gov/Blast.cgi

Web search tool to find regions of similarity between biological sequences. Program compares nucleotide or protein sequences to sequence databases and calculates statistical significance. Used for identifying homologous sequences.

Proper citation: NCBI BLAST (RRID:SCR_004870) Copy   

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http://noble.gs.washington.edu/proj/philius/

Web server that predicts protein transmembrane topology and signal peptides. Hidden Markov models (HMM) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. They expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBN). Their model, Philius, is inspired by a previously published HMM, Phobius, and combines a signal peptide sub-model with a transmembrane sub-model. They introduce a two-stage DBN decoder which combines the power of posterior decoding with the grammar constraints of Viterbi-style decoding. Philius also provides protein type, segment, and topology confidence metrics to aid in the interpretation of the predictions.

Proper citation: Philius (RRID:SCR_004625) Copy   

•   Source: SciCrunch Registry