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http://sourceforge.net/projects/orfer/

An extended software package for high throughput PCR primer design for biological sequences. It reads the NCBI GenBank XML sequence format and extracts open reading frames for proteins. Sequences can be requested by GI or accession number.

Proper citation: ORFprimer (RRID:SCR_003269) Copy   

•   Source: SciCrunch Registry

http://www.cbs.dtu.dk/databases/NESbase

Database of proteins in which the presence of Leucine-rich nuclear export signal (NES) has been experimentally verified. It is curated from literature. Each NESbase entry contains information of whether NES was shown to be necessary and/or sufficient for export, and whether the export was shown to be mediated by the export receptor CRM1. The compiled information was used to make a sequence logo of the Leucine-rich NESs, displaying the conservation of amino acids within a window of 25 residues. Error reports and submissions of new data are most welcome!

Proper citation: NESbase (RRID:SCR_003268) Copy   

•   Source: SciCrunch Registry

http://braintrap.inf.ed.ac.uk/braintrap/

This database contains information on protein expression in the Drosophila melanogaster brain. It consists of a collection of 3D confocal datasets taken from EYFP expressing protein trap Drosophila lines from the Cambridge Protein Trap project. Currently there are 884 brain scans from 535 protein trap lines in the database. Drosophila protein trap strains were generated by the St Johnston Lab and the Russell Lab at the University of Cambridge, UK. The piggyBac insertion method was used to insert constructs containing splice acceptor and donor sites, StrepII and FLAG affinity purification tags, and an EYFP exon (Venus). Brain images were acquired by Seymour Knowles-Barley, in the Armstrong Lab at the University of Edinburgh. Whole brain mounts were imaged by confocal microscopy, with a background immunohistochemical label added to aid the identification of brain structures. Additional immunohistochemical labeling of the EYFP protein using an anti-GFP antibody was also used in most cases. The trapped protein signal (EYFP / anti-GFP), background signal (NC82 label), and the merged signal can be viewed on the website by using the corresponding channel buttons. In all images the trapped protein / EYFP signal appears green and the background / NC82 channel appears magenta. Original .lsm image files are also available for download.

Proper citation: BrainTrap: Fly Brain Protein Trap Database (RRID:SCR_003398) Copy   

•   Source: SciCrunch Registry

http://caintegrator-info.nci.nih.gov/rembrandt

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 28,2023. An initiative to develop a molecular classification schema that is both clinically and biologically meaningful, based on gene expression and genomic data from tumors (Gliomas) of patients who will be prospectively followed through natural history and treatment phase of their illness. The study will also explore gene expression profiles to determine the responsiveness of the patients and correlate with discrete chromosomal abnormalities. The initiative was designed to obtain a large amount of molecular data on DNA and RNA of freshly collected tumor samples that were collected, processed and analyzed in a standardized fashion to allow for large-scale cross sample analysis. The sample collection is accompanied by careful and prospective clinical data acquisition, allowing a variety of matched molecular and clinical data permitting a wide variety of analyses. GMDI has accrued fresh frozen tumors in the retrospective phase (all from the Henry Ford Hospital, without germline DNA) and fresh frozen tumors in the prospective phase (from a variety of institutions). In addition to characterizing the samples from patients enrolled in GMDI, the microarray group has generated genomic-scale analyses of the many human and canine glioma initiating cells/glioma stem cells (GIC/GSC) lines, as well as many canine and murine normal neural stem cell (NSC) lines produced in laboratory.

Proper citation: Glioma Molecular Dignostic Initiatives (RRID:SCR_003329) Copy   

•   Source: SciCrunch Registry

http://nrresource.org

Collection of individual databases on members of the steroid and thyroid hormone receptor superfamily. Although the databases are located on different servers and are managed individually, they each form a node of the NRR. The NRR itself integrates the separate databases and allows an interactive forum for the dissemination of information about the superfamily. NRR Components: Androgen receptor, Estrogen receptor, Glucocorticoid receptor, Peroxisome proliferator, Steroid receptor protein, Thyroid receptor, Vitamin D receptor.

Proper citation: Nuclear Receptor Resource (RRID:SCR_003285) Copy   

•   Source: SciCrunch Registry

http://www.candidagenome.org/

Database of genetic and molecular biological information about Candida albicans. Contains information about genes and proteins, descriptions and classifications of their biological roles, molecular functions, and subcellular localizations, gene, protein, and chromosome sequence information, tools for analysis and comparison of sequences and links to literature information. Each CGD gene or open reading frame has an individual Locus Page. Genetic loci that are not tied to DNA sequence also have Locus Pages. Provides Gene Ontology, GO, to all its users. Three ontologies that comprise GO (Molecular Function, Cellular Component, and Biological Process) are used by multiple databases to annotate gene products, so that this common vocabulary can be used to compare gene products across species. Development of ontologies is ongoing in order to incorporate new information. Data submissions are welcome.

Proper citation: Candida Genome Database (RRID:SCR_002036) Copy   

•   Source: SciCrunch Registry

http://biodev.extra.cea.fr/interoporc/

Automatic prediction tool to infer protein-protein interaction networks, it is applicable for lots of species using orthology and known interactions. The interoPORC method is based on the interolog concept and combines source interaction datasets from public databases as well as clusters of orthologous proteins (PORC) available on Integr8. Users can use this page to ask InteroPorc for all species present in Integr8. Some results are already computed and users can run InteroPorc to investigate any other species. Currently, the following databases are processed and merged (with datetime of the last available public release for each database used): IntAct, MINT, DIP, and Integr8.

Proper citation: InteroPorc (RRID:SCR_002067) Copy   

•   Source: SciCrunch Registry

http://www.structuralgenomics.org/

The Structural Genomics Project aims at determination of the 3D structure of all proteins. It also aims to reduce the cost and time required to determine three-dimensional protein structures. It supports selection, registration, and tracking of protein families and representative targets. This aim can be achieved in four steps : -Organize known protein sequences into families. -Select family representatives as targets. -Solve the 3D structure of targets by X-ray crystallography or NMR spectroscopy. -Build models for other proteins by homology to solved 3D structures. PSI has established a high-throughput structure determination pipeline focused on eukaryotic proteins. NMR spectroscopy is an integral part of this pipeline, both as a method for structure determinations and as a means for screening proteins for stable structure. Because computational approaches have estimated that many eukaryotic proteins are highly disordered, about 1 year into the project, CESG began to use an algorithm. The project has been organized into two separate phases. The first phase was dedicated to demonstrating the feasibility of high-throughput structure determination, solving unique protein structures, and preparing for a subsequent production phase. The second phase, PSI-2, has focused on implementing the high-throughput structure determination methods developed in PSI-1, as well as homology modeling and addressing bottlenecks like modeling membrane proteins. The first phase of the Protein Structure Initiative (PSI-1) saw the establishment of nine pilot centers focusing on structural genomics studies of a range of organisms, including Arabidopsis thaliana, Caenorhabditis elegans and Mycobacterium tuberculosis. During this five-year period over 1,100 protein structures were determined, over 700 of which were classified as unique due to their < 30% sequence similarity with other known protein structures. The primary goal of PSI-1 was to develop methods to streamline the structure determination process, resulted in an array of technical advances. Several methods developed during PSI-1 enhanced expression of recombinant proteins in systems like Escherichia coli, Pichia pastoris and insect cell lines. New streamlined approaches to cell cloning, expression and protein purification were also introduced, in which robotics and software platforms were integrated into the protein production pipeline to minimize required manpower, increase speed, and lower costs. The goal of the second phase of the Protein Structure Initiative (PSI-2) is to use methods introduced in PSI-1 to determine a large number of proteins and continue development in streamlining the structural genomics pipeline. Currently, the third phase of the PSI is being developed and will be called PSI: Biology. The consortia will propose work on substantial biological problems that can benefit from the determination of many protein structures Sponsors: PSI is funded by the U.S. National Institute of General Medical Sciences (NIGMS),

Proper citation: Protein Structure Initiative (RRID:SCR_002161) Copy   

•   Source: SciCrunch Registry

http://provean.jcvi.org/

A software tool which predicts whether an amino acid substitution or indel has an impact on the biological function of a protein.

Proper citation: PROVEAN (RRID:SCR_002182) Copy   

•   Source: SciCrunch Registry

http://www.humgen.rwth-aachen.de/

Catalog of all changes detected in PKHD1 (Polycystic Kidney and Hepatic Disease 1) in a locus specific database. Investigators are invited to submit their novel data to this database. These data should be meaningful for clinical practice as well as of relevance for the reader interested in molecular aspects of polycystic kidney disease (PKD). There are also some links and information for ARPKD patients and their parents. Autosomal recessive polycystic kidney disease (ARPKD/PKHD1) is an important cause of renal-related and liver-related morbidity and mortality in childhood. This study reports mutation screening in 90 ARPKD patients and identifies mutations in 110 alleles making up a detection rate of 61%. Thirty-four of the detected mutations have not been reported previously. Two underlying mutations in 40 patients and one mutation in 30 cases are disclosed, and no mutation was detected on the remaining chromosomes. Mutations were found to be scattered throughout the gene without evidence of clustering at specific sites. PKHD1 mutation analysis is a powerful tool to establish the molecular cause of ARPKD in a given family. Direct identification of mutations allows an unequivocal diagnosis and accurate genetic counseling even in families displaying diagnostic challenges.

Proper citation: Autosomal Recessive Polycystic Kidney Disease Mutation Database (RRID:SCR_002290) Copy   

•   Source: SciCrunch Registry

http://www.csardock.org

Experimental datasets of crystal structures and binding affinities for diverse protein-ligand complexes. Some datasets are generated in house while others are collected from the literature or deposited by academic labs, national centers, and the pharmaceutical industry. For the community to improve their approaches, they need exceptional datasets to train scoring functions and develop new docking algorithms. They aim to provide the highest quality data for a diverse collection of proteins and small molecule ligands. They need input from the community in developing target priorities. Ideal targets will have many high-quality crystal structures (apo and 10-20 bound to diverse ligands) and affinity data for 25 compounds that range in size, scaffold, and logP. It is best if the ligand set has several congeneric series that span a broad range of affinity, with low nanomolar to mid-micromolar being most desirable. They prefer Kd data over Ki data over IC50 data (no % activity data). They will determine solubility, pKa, logP/logD data for the ligands whenever possible. They have augmented some donated IC50 data by determining Kon/Koff and ITC data.

Proper citation: Community Structure-Activity Resource (RRID:SCR_002206) Copy   

•   Source: SciCrunch Registry

http://consurfdb.tau.ac.il/

Provides pre-calculated evolutionary conservation profiles for proteins of known structure in the PDB. Enables flexibility in setting the parameters of the calculation, and accepts optional uploads of atomic coordinates, multiple sequence alignments, and phylogenetic trees for use in the calculation of conservation profiles.

Proper citation: ConSurf Database (RRID:SCR_002320) Copy   

•   Source: SciCrunch Registry

http://www.glycosciences.de/

Portal of glycoinformatics resources including databases and bioinformatics tools for glycobiology and glycomics research. Databases include a bibliography, structure, nuclear magnetic resonance (NMR), mass spectroscopy (ms) and a PDB search.

Proper citation: glycosciences.de (RRID:SCR_002324) Copy   

•   Source: SciCrunch Registry

http://www.pharmabase.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 25, 2015. Open content cheminformatics database linking physiology with pharmacology, it targets the action and use of pharmacological compounds in modifying protein function, while revealing molecular relationships and linking out to related databases and sites. Pharmabase has been developed as a research tool, a resource for students, and an ongoing interactive forum on the use of pharmacological compounds in cellular research. It has several navigational routes, including a graphics browser (shows graphics of cell types and pathways) and membrane transport, which also illustrates the diversity of mechanisms that are covered. Users have access to detailed compound records with interactive features, and a form to send comments to the editor. Investigators are encouraged to alert the editors to mistakes, omissions or new compound information available from their reading and research.

Proper citation: Pharmabase - an open content cheminformatics resource linking physiology with pharmacology (RRID:SCR_002462) Copy   

•   Source: SciCrunch Registry

http://www.ensembl.org/

Collection of genome databases for vertebrates and other eukaryotic species with DNA and protein sequence search capabilities. Used to automatically annotate genome, integrate this annotation with other available biological data and make data publicly available via web. Ensembl tools include BLAST, BLAT, BioMart and the Variant Effect Predictor (VEP) for all supported species.

Proper citation: Ensembl (RRID:SCR_002344) Copy   

•   Source: SciCrunch Registry

http://www.bioinfo.tsinghua.edu.cn/dbsubloc.html

A database of protein subcellular localization containing proteins from primary protein database SWISS-PROT and PIR. By collecting the subcellular localization annotation, these information are classified and categorized by cross references to taxonomies and Gene Ontology database. Annotations were taken from primary protein databases, model organism genome projects and literature texts, and then were analyzed to dig out the subcellular localization features of the proteins. The proteins are also classified into different categories. Based on sequence alignment, nonredundant subsets of the database have been built, which may provide useful information for subcellular localization prediction. The database now contains >60 000 protein sequences including 30 000 protein sequences in the nonredundant data sets. Online download, SOAP server, Blast tools and prediction services are also available.

Proper citation: DBSubLoc - Database of protein Subcellular Localization (RRID:SCR_002339) Copy   

•   Source: SciCrunch Registry

http://iclab.life.nctu.edu.tw/iclab_webtools/sodock/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. An optimization algorithm based on particle swarm optimization (PSO) for solving flexible protein-ligand docking problems.

Proper citation: SODOCK (RRID:SCR_000193) Copy   

•   Source: SciCrunch Registry

http://www.scfbio-iitd.res.in/sanjeevini/sanjeevini.jsp

A complete drug designing software suite with an accessible web-server for targeted directed lead molecule discovery.

Proper citation: Sanjeevini (RRID:SCR_000191) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/as-peak/

A software that utilizes a peak detection algorithm to identify RNA-protein binding sites.

Proper citation: AS-Peak (RRID:SCR_000380) Copy   

•   Source: SciCrunch Registry

http://thomsonreuters.com/metadrug/

A leading systems pharmacology solution that incorporates extensive manually curated information on biological effects of small molecule compounds. Predictive and analytical algorithms look at chemical compounds from different angles in one integrated workflow are available for: * Individual previously described compounds to look up their known information and predict currently unknown properties * Individual newly synthesized or isolated compounds to predict their properties from its structures * Compound libraries to extract known and predict new properties of individual compounds and perform their comparison and prioritization

Proper citation: MetaDrug (RRID:SCR_000461) Copy   

•   Source: SciCrunch Registry