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http://ec2-52-91-98-53.compute-1.amazonaws.com/run/

Web based platform that integrates several bioinformatics tools for screening and annotation of cDNA construct sequences. Translates the nucleotide sequence of the construct into an amino acid sequence, aligns the predicted sequence to a reference database of protein sequences and identifies the best protein and isoform match, annotates any variants present in the construct, and incorporates disease-associated mutations and transcriptomic data.

Proper citation: Clonotator (RRID:SCR_016730) Copy   

•   Source: SciCrunch Registry

https://www.proteinmetrics.com/products/byonic/

Software package for advanced peptide and protein identification by tandem mass spectrometry. Allows to define unlimited number of variable modification type and allows the user to set a separate limit on the number of occurrences of each modification type.

Proper citation: PMI-Byonic (RRID:SCR_016735) Copy   

•   Source: SciCrunch Registry

http://metascape.org/gp/index.html#/main/step1

Web service to analyze gene or protein lists. Provides automated meta analysis tools to understand pathways within a group of orthogonal target-discovery studies.

Proper citation: Metascape (RRID:SCR_016620) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/Biostrings.html

Software package for efficient manipulation of biological strings. Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences.

Proper citation: Biostrings (RRID:SCR_016949) Copy   

•   Source: SciCrunch Registry

http://prosightlite.northwestern.edu/

Software application for matching a single candidate protein sequence and its modifications against a set of mass spectrometric observations. Used to analyze top-down mass spectrometry data.

Proper citation: ProSight Lite (RRID:SCR_016908) Copy   

•   Source: SciCrunch Registry

http://nrtdp.northwestern.edu/

Organization dedicated to analysis of protein molecules by mass spectrometry, with a focus on intact protein measurements. Biomedical projects originated from clinical and basic research programs that utilize both targeted and untargeted analyses. Used for the development of new technology, training and dissemination of proteomics methods to laboratories and scientists.

Proper citation: National Resource for Translational and Developmental Proteomics (RRID:SCR_016907) Copy   

•   Source: SciCrunch Registry

http://www.bumc.bu.edu/cardiovascularproteomics/cpctools/strap/

Software program that automatically annotates a protein list with information that helps in the meaningful interpretation of data from mass spectrometry and other techniques. It takes protein lists as input, in the form of plain text files, protXML files (usually from the TPP), or Dat files from MASCOT search results. From this, it generates protein annotation tables, and a variety of GO charts to aid individual and differential analysis of proteomics data. It downloads information from mainly the Uniprot and EBI QuickGO databases. STRAP requires Windows XP or higher with at least version 3.5 of the Microsoft .NET Framework installed. Platform: Windows compatible

Proper citation: STRAP (RRID:SCR_005675) Copy   

•   Source: SciCrunch Registry

http://hcc.musc.edu/research/resources/biorepository/

The Hollings Cancer Center Tissue Biorepository & Research Pathology Services Shared Resource provides investigators with a centralized infrastructure that promotes biomedical research involving the use and study of human biospecimens. The shared resource is comprised of four integrated components: Biospecimens and data bank, Laser Capture Microdissection, Tissue Microarray, and Research Pathology Services. These components, along with extensive staff expertise, offer a comprehensive means by which researchers can utilize valuable human biospecimens and cutting edge technology to support basic, translational and clinical research. Services: * Biospecimen and Data Bank ** Collecting, processing, and banking of tissue, saliva, urine, blood, plasma, serum, and other tissue derivatives; including those for protocol driven studies ** Retrieval of banked specimens linked to clinicopathologic data, while maintaining patient confidentiality, for research use ** Quality control of collected tissue by the Tissue Biorepository Director, a trained pathologist: verification of diseased state and assessment of tumor purity, etc ** Quality control of DNA/RNA/protein isolated from collected tissue using the Agilent Bioanalyzer * Laser Capture Microdissection ** Identification, localization, and microdissection of targeted cell populations (from human and animal tissue sources) ** Extraction of DNA/RNA/protein from microdissected samples. ** Quality analysis and quality control of isolated nucleic acid using Agilent Bioanalyzer * Tissue Microarray ** Create custom and standard TMAs ** Consultation and technical support in the construction and analyses of TMA * Research Pathology Services ** Macrodissection of tissue prior to isolation of DNA/RNA/protein to increase tumor purity ** Immunohistochemistry and In-situ hybridization ** Quantitative image analysis on conventional and TMA sections, including tissue scoring, Ki-67 labeling index, microvascular density counting, and tissue microarray scoring, etc. * Bio-molecular Assessment ** Cellular DNA, RNA and protein prepared by the Tissue Repository from banked specimens or any other biomolecules submitted by investigators can be qualitatively assessed by Agilent Bioanalyzer, prior to use for downstream applications such as microarray and/or qRT-PCR analysis

Proper citation: Hollings Cancer Center Tissue Biorepository and Research Pathology Services Shared Resource (RRID:SCR_004626) Copy   

•   Source: SciCrunch Registry

http://www.cs.ualberta.ca/~bioinfo/PA/GOSUB/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 30, 2015. Refer to Proteome Analyst 3.0. Subcellular Localization and GO General Molecular Function predictions for many model organism proteomes using Protein Analyst, with a very high coverage rate. When users blast their proteins against the database of results, they will not only be shown blast homologs from the model organisms, but also the Subcellular Localization and GO General Molecular Function predictions as well.

Proper citation: Proteome Analyst PA-GOSUB (RRID:SCR_008234) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/Tools/blast2/index.html

It is used to compare a novel sequence with those contained in nucleotide and protein databases by aligning the novel sequence with previously characterized genes.

Proper citation: Washington University Basic Local Alignment Search Tool (RRID:SCR_008285) Copy   

•   Source: SciCrunch Registry

http://www.uwstructuralgenomics.org/

It is a specialized research center supported by the Protein Structure Initiative (PSI) of the National Institute of General Medical Sciences (NIGMS), one of the National Institutes of Health (NIH). PSI is a federal, university, and industry effort aimed at dramatically reducing the costs and lessening the time it takes to determine a three-dimensional protein structure. The long-range goal of PSI is to solve 10,000 protein structures in 10 years and to make the three-dimensional atomic-level structures of most proteins easily obtainable from knowledge of their corresponding DNA sequences. CESG is located within the Department of Biochemistry at the University of Wisconsin-Madison (Madison, WI) and the Department of Biochemistry at the Medical College of Wisconsin (Milwaukee, WI). CESG develops new methods and technologies to address unique eukaryotic bottlenecks and disseminates its methodologies and experimental results to the scientific community worldwide through: :- Cell-Free Protein Production Workshops :- Plasmids at PSI Materials Repository :- Posters Presented at Scientific Meetings :- Publications in PubMed / PubMed Central :- Sesame (LIMS) Available for Researchers :- Solved Structures in the Protein Data Bank :- Technology Dissemination Reports They have welcomed requests by researchers to solve eukaryotic protein structures, particularly medically relevant proteins, through our Online Structure Request System for Researchers. They have solved many community-nominated targets and deposited information about these targets in public databases and published on our investigations and findings. Sponsors: CESG is supported by NIH / NIGMS Protein Structure Initiative grant numbers U54 GM074901 and P50 GM064598.

Proper citation: CESG (RRID:SCR_008451) Copy   

•   Source: SciCrunch Registry

http://expasy.org/

Portal which provides access to scientific databases and software tools (i.e., resources) in different areas of life sciences including proteomics, genomics, phylogeny, systems biology, population genetics, transcriptomics etc. It contains resources from many different SIB groups as well as external institutions.

Proper citation: ExPASy Bioinformatics Resource Portal (RRID:SCR_012880) Copy   

•   Source: SciCrunch Registry

http://www.unihi.org

A database of human molecular interaction networks that integrates human protein-protein and transcriptional regulatory interactions from 15 distinct resources and aims to give direct and easy access to the integrated data set and to enable users to perform network-based investigations. The database includes tools (i) to search for molecular interaction partners of query genes or proteins in the integrated dataset, (ii) to inspect the origin, evidence and functional annotation of retrieved proteins and interactions, (iii) to visualize and adjust the resulting interaction network, (iv) to filter interactions based on method of derivation, evidence and type of experiment as well as based on gene expression data or gene lists and (v) to analyze the functional composition of interaction networks.

Proper citation: Unified Human Interactome (RRID:SCR_005805) Copy   

•   Source: SciCrunch Registry

http://snps-and-go.biocomp.unibo.it/snps-and-go/

A server for the prediction of single point protein mutations likely to be involved in the insurgence of diseases in humans.

Proper citation: SNPsandGO (RRID:SCR_005788) Copy   

•   Source: SciCrunch Registry

http://prorepeat.bioinformatics.nl/

ProRepeat is an integrated curated repository and analysis platform for in-depth research on the biological characteristics of amino acid tandem repeats. ProRepeat collects repeats from all proteins included in the UniProt knowledgebase, together with 85 completely sequenced eukaryotic proteomes contained within the RefSeq collection. It contains non-redundant perfect tandem repeats, approximate tandem repeats and simple, low-complexity sequences, covering the majority of the amino acid tandem repeat patterns found in proteins. The ProRepeat web interface allows querying the repeat database using repeat characteristics like repeat unit and length, number of repetitions of the repeat unit and position of the repeat in the protein. Users can also search for repeats by the characteristics of repeat containing proteins, such as entry ID, protein description, sequence length, gene name and taxon. ProRepeat offers powerful analysis tools for finding biological interesting properties of repeats, such as the strong position bias of leucine repeats in the N-terminus of eukaryotic protein sequences, the differences of repeat abundance among proteomes, the functional classification of repeat containing proteins and GC content constrains of repeats' corresponding codons.

Proper citation: ProRepeat (RRID:SCR_006113) Copy   

•   Source: SciCrunch Registry

http://pcidb.russelllab.org/

The database of protein-chemical structural interactions includes all existing 3D structures of complexes of proteins with low molecular weight ligands. When one considers the proteins and chemical vertices of a graph, all these interactions form a network. Biological networks are powerful tools for predicting undocumented relationships between molecules. The underlying principle is that existing interactions between molecules can be used to predict new interactions. For pairs of proteins sharing a common ligand, we use protein and chemical superimpositions combined with fast structural compatibility screens to predict whether additional compounds bound by one protein would bind the other. The current version includes data from the Protein Data Bank as of August 2011. The database is updated monthly.

Proper citation: ProtChemSI (RRID:SCR_006115) Copy   

•   Source: SciCrunch Registry

http://web.cbio.uct.ac.za/ITGOM/

The Integrated Tool for IC-based GO Semantic Similarity Measures (IT-GOM) integrates the currently known GO semantic similarity measures into a single tool. It provides the information content (IC) of GO terms, semantic similarity between GO terms and GO-based protein functional similarity scores. The specificity of GO terms and the similarity of biological content between GO terms or proteins are transformed into numeric values for protein analyses at the functional level. The integration of the different measures enables users to choose the measure best suited to their application and to compare results between different semantic similarity measures. Platform: Online tool

Proper citation: IT-GOM: Integrated Tool for IC-based GO Semantic Similarity Measures (RRID:SCR_005815) Copy   

•   Source: SciCrunch Registry

http://prism.ccbb.ku.edu.tr/hotregion/index.php

Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. HotRegion provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided. The number of interfaces in the database is 147909 and still growing.

Proper citation: HotRegion - A Database of Cooperative Hotspots (RRID:SCR_006022) Copy   

•   Source: SciCrunch Registry

http://dommino.org

DOMMINO is a comprehensive structural database on macromolecular interactions. As of June, 2011, it contains more than 407,000 binary interactions. The distinctive features of DOMMINO are: # Automated updates: DOMMINO is fully automated and is designed to update itself on a weekly basis, one day after a PDB weekly update. Thus, the community will be able to study macromolecular interactions almost immediately after they are released by PDB. # Coverage of non-domain mediated interactions: In addition to domain-domain and domain-peptide interactions the database characterizes the interaction between domains and unstructured protein regions that are not parts of a domain, such as inter-domain linkers and N- and C-termini. The interactions that involve the latter unstructured parts of proteins have been included to the database for the first time providing additional ~186,000 interactions (~45% of the total number of interactions, as of June, 2011). # Coverage of new structural domains: DOMMINO employs one of the most accurate structural classifications of proteins, SCOP. In addition to the existing SCOP-annotated domains, we employ a state-of-the-art machine learning approach to classify newer protein structures into existing SCOP families. With the progress of structural genomics, we do not expect a significant growth of the number of structurally novel folds or protein families and therefore our method allows covering almost all new protein structures. In total, using this predictive approach has allowed us to add more than 261,000 new interactions, almost twice as many as existing SCOP-annotated interactions. # The web-interface is designed to give the user a possibility of a flexible search as well as the capability to study macromolecular interactions in a PDB structure at the interaction network level and at the individual interface level. The web interface of the DOMMINO database includes a comprehensive list of help topics linked to the specific actions. In addition, we have designed a step-by-step tutorial that covers all aspects of working with the data from DOMMINO using the web interface.

Proper citation: DOMMINO - Database Of MacroMolecular INteractiOns (RRID:SCR_005958) Copy   

•   Source: SciCrunch Registry

http://www.jcvi.org/charprotdb/index.cgi/home

The Characterized Protein Database, CharProtDB, is designed and being developed as a resource of expertly curated, experimentally characterized proteins described in published literature. For each protein record in CharProtDB, storage of several data types is supported. It includes functional annotation (several instances of protein names and gene symbols) taxonomic classification, literature links, specific Gene Ontology (GO) terms and GO evidence codes, EC (Enzyme Commisssion) and TC (Transport Classification) numbers and protein sequence. Additionally, each protein record is associated with cross links to all public accessions in major protein databases as ��synonymous accessions��. Each of the above data types can be linked to as many literature references as possible. Every CharProtDB entry requires minimum data types to be furnished. They are protein name, GO terms and supporting reference(s) associated to GO evidence codes. Annotating using the GO system is of importance for several reasons; the GO system captures defined concepts (the GO terms) with unique ids, which can be attached to specific genes and the three controlled vocabularies of the GO allow for the capture of much more annotation information than is traditionally captured in protein common names, including, for example, not just the function of the protein, but its location as well. GO evidence codes implemented in CharProtDB directly correlate with the GO consortium definitions of experimental codes. CharProtDB tools link characterization data from multiple input streams through synonymous accessions or direct sequence identity. CharProtDB can represent multiple characterizations of the same protein, with proper attribution and links to database sources. Users can use a variety of search terms including protein name, gene symbol, EC number, organism name, accessions or any text to search the database. Following the search, a display page lists all the proteins that match the search term. Click on the protein name to view more detailed annotated information for each protein. Additionally, each protein record can be annotated.

Proper citation: CharProtDB: Characterized Protein Database (RRID:SCR_005872) Copy   

•   Source: SciCrunch Registry