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Public archive providing a comprehensive record of the world''''s nucleotide sequencing information, covering raw sequencing data, sequence assembly information and functional annotation. All submitted data, once public, will be exchanged with the NCBI and DDBJ as part of the INSDC data exchange agreement. The European Nucleotide Archive (ENA) captures and presents information relating to experimental workflows that are based around nucleotide sequencing. A typical workflow includes the isolation and preparation of material for sequencing, a run of a sequencing machine in which sequencing data are produced and a subsequent bioinformatic analysis pipeline. ENA records this information in a data model that covers input information (sample, experimental setup, machine configuration), output machine data (sequence traces, reads and quality scores) and interpreted information (assembly, mapping, functional annotation). Data arrive at ENA from a variety of sources including submissions of raw data, assembled sequences and annotation from small-scale sequencing efforts, data provision from the major European sequencing centers and routine and comprehensive exchange with their partners in the International Nucleotide Sequence Database Collaboration (INSDC). Provision of nucleotide sequence data to ENA or its INSDC partners has become a central and mandatory step in the dissemination of research findings to the scientific community. ENA works with publishers of scientific literature and funding bodies to ensure compliance with these principles and to provide optimal submission systems and data access tools that work seamlessly with the published literature. ENA is made up of a number of distinct databases that includes the EMBL Nucleotide Sequence Database (Embl-Bank), the newly established Sequence Read Archive (SRA) and the Trace Archive. The main tool for downloading ENA data is the ENA Browser, which is available through REST URLs for easy programmatic use. All ENA data are available through the ENA Browser. Note: EMBL Nucleotide Sequence Database (EMBL-Bank) is entirely included within this resource.
Proper citation: European Nucleotide Archive (ENA) (RRID:SCR_006515) Copy
Pictorial database of an at-a-glance overview of the contents of each 3D structure deposited in the Protein Data Bank (PDB). It shows the molecule(s) that make up the structure (ie protein chains, DNA, ligands and metal ions) and schematic diagrams of their interactions. Extensive use is made of the freely available RasMol molecular graphics program to view the molecules and their interactions in 3D. Entries are accessed either by their 4-character PDB code, or by one of the two search boxes provided on the PDBsum home page: text search or sequence search. The information given on each PDBsum entry is spread across several pages, as listed below and accessible from the tabs at the top of the page. Only the relevant tabs will be present on any given page. * Top page - summary information including thumbnail image of structure, molecules in structure, enzyme reaction diagram (where relevant), GO functional assignments, and selected figures from key reference * Protein - wiring diagram, topology diagram(s) by CATH domain, and residue conservation (where available) * DNA/RNA - DNA/RNA sequence and NUCPLOT showing interactions made with protein * Ligands - description of bound molecule and LIGPLOT showing interactions made with protein * Prot-prot - schematic diagrams of any protein-protein interfaces and the residue-residue interactions made across them * Clefts - listing of top ten clefts in the surface of the protein, listed by volume with any bound ligands shown * Links - links to external databases Additionally, it accepts users'''' own PDB format files and generates a private set of analyses for each uploaded structure.
Proper citation: PDBsum (RRID:SCR_006511) Copy
A public database that enhances understanding of the effects of environmental chemicals on human health. Integrated GO data and a GO browser add functionality to CTD by allowing users to understand biological functions, processes and cellular locations that are the targets of chemical exposures. CTD includes curated data describing cross-species chemical–gene/protein interactions, chemical–disease and gene–disease associations to illuminate molecular mechanisms underlying variable susceptibility and environmentally influenced diseases. These data will also provide insights into complex chemical–gene and protein interaction networks.
Proper citation: Comparative Toxicogenomics Database (CTD) (RRID:SCR_006530) Copy
http://www.ebi.ac.uk/Rebholz-srv/ebimed/
A web application that combines Information Retrieval and Extraction from Medline. EBIMed finds Medline abstracts in the same way PubMed does. Then it goes a step beyond and analyses them to offer a complete overview on associations between UniProt protein/gene names, GO annotations, Drugs and Species. The results are shown in a table that displays all the associations and links to the sentences that support them and to the original abstracts. By selecting relevant sentences and highlighting the biomedical terminology EBIMed enhances your ability to acquire knowledge, relate facts, discover implications and, overall, have a good overview economizing the effort in reading.
Proper citation: EBIMed (RRID:SCR_005314) Copy
Scientists at the Yates Lab at The Scripps Research Institute (TSRI) rely on information yielded by tandem mass spectrometry to identify proteins from complex mixtures. Using this powerful technique, researchers draw upon a cross section of fields to increase the scope, sensitivity, and throughput of technologies for practical proteomics. Biologists provide the questions that drive our research. By identifying complexes that are poorly understood or organism-wide issues requiring further exploration, we gain a theoretical understanding of issues that are tractable only through proteomic strategies. Analytical chemists and biochemists improve our tools for revealing the proteins present in biological samples. Targets for optimization include the isolations used to obtain proteins, the steps to generate peptides from these proteins, and the separation of peptides en route to the mass spectrometer. Chemistry is vital to increasing power of proteomic technology. Computer science yields tools on two scales. First, the sequence corresponding to each peptide''s tandem mass spectrum must be identified. Once those identifications have been completed, additional tools are needed to summarize and organize these identifications.
Proper citation: TSRI-Yates Lab (RRID:SCR_005699) Copy
A web-based software package for comparative genomics.
Proper citation: Sybil (RRID:SCR_005593) Copy
http://edwardslab.bmcb.georgetown.edu/ws/peptideMapper/
The PeptideMapper Web-Service provides alignments of peptide sequence alignments to proteins, mRNA, EST, and HTC sequences from Genbank, RefSeq, UniProt, IPI, VEGA, EMBL, and HInvDb. This mapping infrastructure is supported, in part, by the compressed peptide sequence database infrastructure (Edwards, 2007) which enables a fast, suffix-tree based mapping of peptide sequences to gene identifiers and a gene-focused detailed mapping of peptide sequences to source sequence evidence. The PeptideMapper Web-Service can be used interactively or as a web-service using either HTTP or SOAP requests. Results of HTTP requests can be returned in a variety of formats, including XML, JSON, CSV, TSV, or XLS, and in some cases, GFF or BED; results of SOAP requests are returned as SOAP responses. The PeptideMapper Web-Service maps at most 20 peptides with length between 5 and 30 amino-acids in each request. The number of alignments returned, per peptide, gene, and sequence type, is set to 10 by default. The default can be changed on the interactive alignments search form or by using the max web-service parameter.
Proper citation: PeptideMapper (RRID:SCR_005763) Copy
Collect, share, and distribute information about protein three-dimensional structures. It serves as a portal for the scientific community to learn about protein structures solved by SG centers, and also to contribute their expertise in annotating protein function. The premise of the TOPSAN project is that, no matter how much any individual knows about a particular protein, there are other members of the scientific community who know more about certain aspects of the same protein, and that the collective analyses from experts will be far more informative than any local group, let alone individual, could contribute. They believe that, if the members of the biological community are given the opportunity, authorship incentives, and an easy way to contribute their knowledge to the structure annotation, they would do so. Therefore, borrowing elements from successful, distributed, collaborative projects, such as Wikipedia (the free encyclopedia anyone can edit) and from other open source software development projects, TOPSAN will be a broad, collaborative effort to annotate protein structures, initially, those determined at the JCSG. They believe that the annotation of proteins solved by structural genomics consortia offers a unique opportunity to challenge the extant paradigm of how biological data is collected and distributed, and to connect structural genomics and structural biology to the entire biological research community. TOPSAN is designed to be scalable, modular and extensible. Furthermore, it is intended to be immediately useful in a simplistic way and will accommodate incremental improvements to functionality as usage becomes more sophisticated. Their annotation pages will offer the end user a combination of automatically generated as well as expert-curated annotations of protein structures. They will use available technology to increase the speed and granularity of the exchange of scientific ideas, and use incentive mechanisms that will encourage collaborative participation.
Proper citation: TOPSAN (RRID:SCR_005758) Copy
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 20,2019.Database and analysis environment for experimentally determined binding sites of RNA-binding proteins. It supports the automatic functional annotation of short reads resulting primarily from crosslinking and immunoprecipitation experiments (CLIP) performed with RNA-binding proteins in order to identify the binding sites of these proteins. The functional annotation could be also applied to short reads resulting from other types of experiments such as mRNA-Seq, Digital Gene Expression, small RNA cloning, etc. The platform enables visualization and mining of individual data sets as well as analysis involving multiple experimental data sets. The platform can support collaborative projects involving multiple users and groups of users as well as public and private datasets.
Proper citation: CLIPZ (RRID:SCR_005755) Copy
ZMM is a molecular modeling program for theoretical studies of systems of any complexity: small molecules, peptides, proteins, nucleic acids, and ligand-receptor complexes. ZMM searches optimal structures in the space of generalized coordinates: torsion angles, bond angles, bond lengths, positions free molecules and ions, and orientation of free molecules. Any generalized coordinate may be kept fixed. Molecules and fragments that are not expected to undergo significant conformational changes may be treated as rigid bodies. Popular molecular modeling programs usually work in the space of Cartesian coordinates of atoms. During energy minimization of a big system, many Cartesian coordinates-variables move collectively. For example, rotation of a benzene ring around the C-Ph bond in the Cartesian-coordinates space involves collective motion of 33 variables. In the generalized-coordinates space, this rotation involves variation of just one torsion angle. In ZMM, any fragment of a molecular system may be treated as either rigid or flexible. The generalized-coordinates method saves large computational resources if only a small part of a system is considered flexible. Examples are ligand-protein and protein-protein interactions. The savings occur because the sampling space is reduced and because molecular interactions within rigid fragments are not computed. * ZMM runs on Windows 95, 98, 2000, XP, UNIX, and Linux * ZMM can be used via the command-line interface * ZMM can also be used at Windows via a graphical user interface
Proper citation: ZMM (RRID:SCR_005741) Copy
A portal to the Mouse Atlas of Gene Expression Project and Dissecting Gene Expression Networks in Mammalian Organogenesis Project. This Atlas will define the normal state for many tissues by determining, in a comprehensive and quantitative fashion, the number and identity of genes expressed throughout development. The resource will be comprehensive, quantitative, and publicly accessible, containing data on essentially all genes expressed throughout select stages of mouse development. Serial Analysis of Gene Expression (SAGE) is the gene expression methodology of choice for this work. Unlike expressed sequence tags (ESTs) and gene chip data, SAGE data are independent of prior gene discovery and are quantitative. Furthermore, SAGE data are digital, easily exchanged between laboratories for comparison and can be added to by scientists for years to come. Thus, this Atlas will include a data structure and data curation strategy that will facilitate the ongoing collection of gene expression data, even after the completion of this project. The Mouse Atlas project compromises 202 SAGE Libraries from 198 tissues. The list of libraries is available in a number of different groupings, including groups of libraries taken from specific tissue locations and libraries taken from specific developmental stages. Furthermore, this atlas will assemble gene expression profiles for a few focused experiments that will test hypotheses related to the techniques employed, tumor models and models of abnormal development. This will test the resource and provide quality control, validation and demonstrate applicability. Additionally, The Mammalian Organogenesis - Regulation by Gene Expression Networks (MORGEN) project will provide a complete, permanent, and accurate picture of mouse gene expression in the heart (atrioventricular canal and outflow tract), pancreas, and liver; new techniques to understand the interplay of proteins governing the expression of genes key to the development of these organ systems; and the identification of the master regulatory switches that control development of the tissues.
Proper citation: Mouse Gene Expression at the BC Cancer Agency (RRID:SCR_008091) Copy
MitoRes, is a comprehensive and reliable resource for massive extraction of sequences and sub-sequences of nuclear genes and encoded products targeting mitochondria in metazoa. It has been developed for supporting high-throughput in-silico analyses aimed to studies of functional genomics related to mitochondrial biogenesis, metabolism and to their pathological dysfunctions. It integrates information from the most accredited world-wide databases to bring together gene, transcript and encoded protein sequences associated to annotations on species name and taxonomic classification, gene name, functional product, organelle localization, protein tissue specificity, Enzyme Classification (EC), Gene Ontology (GO) classification and links to other related public databases. The section Cluster, has been dedicated to the collection of data on protein clustering of the entire catalogue of MitoRes protein sequences based on all versus all global pair-wise alignments for assessing putative intra- and inter-species functional relationships. The current version of MitoRes is based on the UniProt release 4 and contains 64 different metazoan species. The incredible explosion of knowledge production in Biology in the past two decades has created a critical need for bioinformatic instruments able to manage data and facilitate their retrieval and analysis. Hundreds of biological databases have been produced and the integration of biological data from these different resources is very important when we want to focus our efforts towards the study of a particular layer of biological knowledge. MitoRes is a completely rebuilt edition of MitoNuc database, which has been extensively modified to deal successfully with the challenges of the post genomic era. Its goal is to represent a comprehensive and reliable resource supporting high-quality in-silico analyses aimed to the functional characterization of gene, transcript and amino acid sequences, encoded by the nuclear genome and involved in mitochondrial biogenesis, metabolism and pathological dysfunctions in metazoa. The central features of MitoRes are: # an integrated catalogue of protein, transcript and gene sequences and sub-sequences # a Web-based application composed of a wide spectrum of search/retrieval facilities # a sequence export manager allowing massive extraction of bio-sequences (genes, introns, exons, gene flanking regions, transcripts, UTRs, CDS, proteins and signal peptides) in FASTA, EMBL and GenBank formats. It is an interconnected knowledge management system based on a MySQL relational database, which ensures data consistency and integrity, and on a Web Graphical User Interface (GUI), built in Seagull PHP Framework, offering a wide range of search and sequence extraction facilities. The database is compiled extracting and integrating information from public resources and data generated by the MitoRes team. The MitoRes database consists of comprehensive sequence entries whose core data are protein, transcript and gene sequences and taxonomic information describing the biological source of the protein. Additional information include: bio-sequences structure and location, biological function of protein product and dynamic links to both, external public databases used as data resources and public databases reporting complementary information. The core entity of the MitoRes database is represented by the protein so that each MitoRes entry is generated for each protein reported in the UniProt database as a nuclear encoded protein involved in mitochondrial biogenesis and function. Sponsors: MitoRes has been supported by Ministero Universit e Ricerca Scientifica, Italy (PRIN, Programma Biotecnologie legge 95/95-MURST 5, Proiect MURST Cluster C03/2000, CEGBA). Currently it is supported by operating grants from the Ministero dellIstruzione, dellUniversit e della Ricerca (MIUR), Italy (PNR 2001-2003 (FIRB art.8) D.M. 199, Strategic Program: Post-genome, grant 31-063933 and Project n.2, Cluster C03 L. 488/929).
Proper citation: MitoRes (RRID:SCR_008208) Copy
THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. An algorithm that finds articles most relevant to a genetic sequence. In the genomic era, researchers often want to know more information about a biological sequence by retrieving its related articles. However, there is no available tool yet to achieve conveniently this goal. Here, a new literature-mining tool MedBlast is developed, which uses natural language processing techniques, to retrieve the related articles of a given sequence. An online server of this program is also provided. The genome sequencing projects generate such a large amount of data every day that many molecular biologists often encounter some sequences that they know nothing about. Literature is usually the principal resource of such information. It is relatively easy to mine the articles cited by the sequence annotation; however, it is a difficult task to retrieve those relevant articles without direct citation relationship. The related articles are those described in the given sequence (gene/protein), or its redundant sequences, or the close homologs in various species. They can be divided into two classes: direct references, which include those either cited by the sequence annotation or citing the sequence in its text; indirect references, those which contain gene symbols of the given sequence. A few additional issues make the task even more complicated: (1) symbols may have aliases; and (2) one sequence may have a couple of relatives that we want to take into account too, which include redundant (e.g. protein and gene sequences) and close homologs. Here the issues are addressed by the development of the software MedBlast, which can retrieve the related articles of the given sequence automatically. MedBlast uses BLAST to extend homology relationships, precompiled species-specific thesauruses, a useful semantics technique in natural language processing (NLP), to extend alias relationship, and EUtilities toolset to search and retrieve corresponding articles of each sequence from PubMed. MedBlast take a sequence in FASTA format as input. The program first uses BLAST to search the GenBank nucleic acid and protein non-redundant (nr) databases, to extend to those homologous and corresponding nucleic acid and protein sequences. Users can input the BLAST results directly, but it is recommended to input the result of both protein and nucleic acid nr databases. The hits with low e-values are chosen as the relatives because the low similarity hits often do not contain specific information. Very long sequences, e.g. 100k, which are usually genomic sequences, are discarded too, for they do not contain specific direct references. User can adjust these parameters to meet their own needs.
Proper citation: MedBlast (RRID:SCR_008202) Copy
http://www.koki.hu/main.php?folderID=903
The aim of this laboratory is to understand how information is encoded in specific spatiotemporal activity patterns and structural configurations at the circuit, cellular, and molecular levels in the hippocampus, thereby enabling the process of memory. A major task is to find the neuronal codes of internal representations of memory items and the mapping rules between the levels of gene expression/proteins synthesis and the level of cognitive processing. Novel combinations of approaches, including multiple single-cell recording technology, patch-clamp electrophysiology, neuroanatomy/neurochemistry at the cellular and subcellular levels, and computational models are employed to test specific hypotheses about that mapping process (such as local circuit anatomy and activity-dependent short-term and long-term synaptic plasticity). Collaborations within the Institute allows the group to also incorporate gene targeting methods and behavioral learning/memory tests in their methodological repertoire. The laboratory has been focusing on the normal and pathological (epileptic, ischemic) activity of cortical networks, with particular attention to the generation of behaviour-dependent population discharge patterns (theta and gamma oscillations, hippocampal sharp waves). Anatomical, in vitro and in vivo electrophysiological, pharmacological and molecular techniques and modeling are combined to elucidate the functional roles of inhibitory cell types in the control of population synchrony and synaptic plasticity in the hippocampus, their local and subcortical modulation via selective afferent pathways (GABAergic and cholinergic septal, as well as serotonergic raphe input) and pre- or postsynaptic receptors. An expanding new direction of research is related to the role of endocannabinoid signaling in the activity-dependent modulation of GABAergic and glutamatergic transmission, and its involvement in anxiety-like behavior.
Proper citation: Institute of Experimental Medicine of the Hungarian Academy of Sciences: Laboratory of Cerebral Cortext Reserach (RRID:SCR_008041) Copy
https://wiki.med.harvard.edu/SysBio/Megason/GoFigure
GoFigure is a software platform for quantitating complex 4d in vivo microscopy based data in high-throughput at the level of the cell. A prime goal of GoFigure is the automatic segmentation of nuclei and cell membranes and in temporally tracking them across cell migration and division to create cell lineages. GoFigure v2.0 is a major new release of our software package for quantitative analysis of image data. The research focuses on analyzing cells in intact, whole zebrafish embryos using 4d (xyzt) imaging which tends to make automatic segmentation more difficult than with 2d or 2d+time imaging of cells in culture. This resource has developed an automatic segmentation pipeline that includes ICA based channel unmixing, membrane nuclear channel subtraction, Gaussian correlation, shape models, and level set based variational active contours. GoFigure was designed to meet the challenging requirements of in toto imaging. In toto imaging is a technology that we are developing in which we seek to track all the cell movements and divisions that form structures during embryonic development of zebrafish and to quantitate protein expression and localization on top of this digital lineage. For in toto imaging, GoFigure uses zebrafish embryos in which the nuclei and cell membranes have been marked with 2 different color fluorescent proteins to allow cells to be segmented and tracked. A transgenic line in a third color can be used to mark protein expression and localization using a genetic approach that this resource developed called FlipTraps or using traditional transgenic approaches. Embryos are imaged using confocal or 2-photon microscopy to capture high-resolution xyzt image sets used for cell tracking. The GoFigure GUI will provide many tools for visualization and analysis of bioimages. Since fully automatic segmentation of cells is never perfect, GoFigure will provide easy to use tools for semi-automatically and manually adding, deleting, and editing traces in 2d (figures-xy, xz, or yz), 3d (meshes- xyz), 4d (tracks- xyzt) and 4d+cell division (lineages). GoFigure will also provide a number of views into complex image data sets including 3d XYZ and XYT image views, tabular list views of traces, histograms, and scattergrams. Importantly, all these views will be linked together to allow the user to explore their data from multiple angles. Data will be easily sorted and color-coded in many ways to explore correlations in higher dimensional data. The GoFigure architecture is designed to allow additional segmentation, visualization, and analysis filters to be plugged in. Sponsors: GoFigure is developed by Harvard University., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: Harvard Medical School, Department of Systems Biology: The Megason Lab -GoFigure Software (RRID:SCR_008037) Copy
http://www.ebi.ac.uk/parasites/parasite-genome.html
This website contains information about the genomic sequence of parasites. It also contains multiple search engines to search six frame translations of parasite nucleotide databases for motifs, parasite protein databases for motifs, and parasite protein databases for keywords and text terms. * Guide to Internet Access to Parasite Genome Information * Guide to web-based analysis tools * Parasite Genome BLAST Server: Search a range of parasite specific nucleotide sequence databases with your own sequence. * Parasite Proteome Keyword Search Facility: Search parasite protein databases for keywords and text terms * Parasite Proteome Motif Search Facility: Search parasite protein databases for motifs * Parasite Six Frame Translation Motif Search Facility: Search six frame translations of parasite nucleotide databases for motifs * Genome computing resources: A list of ftp and gopher sites where genome computing applications and other resources can be found.
Proper citation: Parasite genome databases and genome research resources (RRID:SCR_008150) Copy
THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. PDBfun is a web server for structural and functional analysis of proteins at the residue level. pdbFun gives fast access to the whole Protein Data Bank (PDB) organized as a database of annotated residues. The available data (features) range from solvent exposure to ligand binding ability, location in a protein cavity, secondary structure, residue type, sequence functional pattern, protein domain and catalytic activity. PDBfun is an integrated web tool for querying the PDB at the residue level and for local structural comparison. It integrates knowledge on single residues in protein structures coming from other databases or calculated with available or in-house developed instruments for structural analysis. Each set of different annotations represents a feature. Features are listed in PDBfun main page in orange. Features can be used for building residues selections.
Proper citation: Protein Databank Fun (RRID:SCR_008226) Copy
http://www.poissonboltzmann.org/apbs/
APBS is a software package for modeling biomolecular solvation through solution of the Poisson-Boltzmann equation (PBE), one of the most popular continuum models for describing electrostatic interactions between molecular solutes in salty, aqueous media. APBS was designed to efficiently evaluate electrostatic properties for such simulations for a wide range of length scales to enable the investigation of molecules with tens to millions of atoms. It also provides implicit solvent models of nonpolar solvation which accurately account for both repulsive and attractive solute-solvent interactions. APBS uses FEtk (the Finite Element ToolKit) to solve the Poisson-Boltzmann equation numerically. FEtk is a portable collection of finite element modeling class libraries written in an object-oriented version of C. It is designed to solve general coupled systems of nonlinear partial differential equations using adaptive finite element methods, inexact Newton methods, and algebraic multilevel methods.
Proper citation: Adaptive Poisson-Boltzmann Solver (RRID:SCR_008387) Copy
http://ophid.utoronto.ca/navigator/
A software package for visualizing and analyzing protein-protein interaction networks. NAViGaTOR can query OPHID / I2D - online databases of interaction data - and display networks in 2D or 3D. To improve scalability and performance, NAViGaTOR combines Java with OpenGL to provide a 2D/3D visualization system on multiple hardware platforms. NAViGaTOR also provides analytical capabilities and supports standard import and export formats such as GO and the Proteomics Standards Initiative (PSI). NAViGaTOR can be installed and run on Microsoft Windows, Linux / UNIX, and Mac OS systems. NAViGaTOR is written in Java and uses JOGL (Java bindings for OpenGL) to support scalability, highlighting or suppressing of information, and other advanced graphic approaches.
Proper citation: Network Analysis, Visualization and Graphing TORonto (RRID:SCR_008373) Copy
The JCSG is a multi-institutional consortium that aims to explore the expanding protein universe to find new challenges and opportunities to significantly contribute to new biology, chemistry and medicine through development of HT approaches to structural genomics. The mission of JCSG is to to operate a robust HT protein structure determination pipeline as a large-scale production center for PSI-2. A major goal is to ensure that innovative high-throughput approaches are developed that advance not only structural genomics, but also structural biology in general, via investigation of large numbers of high-value structures that populate protein fold and family space and by increasing the efficiency of structure determination at substantially reduced cost. The JCSG centralizes each core activity into single dedicated sites, each handling distinct, but interconnected objectives. This unique approach allows each specialized group to focus on its own area of expertise and provides well-defined interfaces among the groups. In addition, this approach addresses the requirements for the scalability needed to process large numbers of targets at a greatly reduced cost per target. JCSG production groups are: - Administrative Core - Bioinformatics Core - Crystallomics Core - Structure Determination Core - NMR Core JCSG is deeply committed to the development of new technologies that facilitate high throughput structural genomics. The areas of development include hardware, software, new experimental methods, and adaptation of existing technologies to advance genome research. In the hardware arena, their commitment is to the development of technologies that accelerate structure solution by increasing throughput rates at every stage of the production pipeline. Therefore, one major area of hardware development has been the implementation of robotics. In the software arena, they have developed enterprise resource software that track success, failures, and sample histories from target selection to PDB deposition, annotation and target management tools, and helper applications aimed at facilitating and automating multiple steps in the pipeline. Sponsors: The Joint Center for Structural Genomics is funded by the National Institute of General Medical Sciences (NIGMS), as part of the second phase of the Protein Structure Initiative (PSI) of the National Institutes of Health (U54 GM074898).
Proper citation: Joint Center for Structural Genomics (RRID:SCR_008251) Copy
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