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https://biosyntax.org/

Software for syntax highlighting for computational biology.

Proper citation: bioSyntax (RRID:SCR_016207) Copy   

•   Source: SciCrunch Registry

http://gmod.org/wiki/Chado

Relational database schema that underlies many GMOD installations. It is capable of representing many of the general classes of data frequently encountered in modern biology such as sequence, sequence comparisons, phenotypes, genotypes, ontologies, publications, and phylogeny. It has been designed to handle complex representations of biological knowledge and should be considered one of the most sophisticated relational schemas currently available in molecular biology. The price of this capability is that the new user must spend some time becoming familiar with its fundamentals.

Proper citation: Chado (RRID:SCR_024073) Copy   

•   Source: SciCrunch Registry

https://github.com/BD2KGenomics/toil

Software pipeline management system, written in Python. Enables reproducible, open source, big biomedical data analyses.

Proper citation: toil (RRID:SCR_024391) Copy   

•   Source: SciCrunch Registry

https://www.open-emr.org/

Open source software for electronic health records and medical practice management solution.

Proper citation: OpenEMR (RRID:SCR_024144) Copy   

•   Source: SciCrunch Registry

http://idash.ucsd.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023. National Center for Biomedical Computing (NCBC) that develops new algorithms, opensource tools, computational infrastructure, and services for biomedical and behavioral researchers nationwide to promote the secure sharing and consuming of biomedical and behavioral resources (software, data, and computing systems) with iDASH collaborators. The center addresses fundamental challenges to research progress by providing a secure, privacypreserving environment in which researchers can analyze genomic, transcriptomic, clinical, behavioral, and social data relevant to health. Three driving biological projects in iDASH (Molecular Phenotyping of Kawasaki Disease, Post-Marketing Surveillance of Hematologic Medications, and Individualized Intervention to Enhance Physical Activity) span the molecular-individualpopulation spectrum, and they will motivate, inform, and support tool development. iDASH will collaborate with other NCBCs and will disseminate tools via annual workshops, presentations at major conferences, and scientific publications.

Proper citation: iDASH (RRID:SCR_003524) Copy   

•   Source: SciCrunch Registry

http://phenoscape.org/

Project to create a scalable infrastructure that enables linking phenotypes across different fields of biology by the semantic similarity of their descriptions.

Proper citation: Phenoscape (RRID:SCR_003799) Copy   

•   Source: SciCrunch Registry

http://www.ensembl.org/info/docs/tools/vep/index.html

Data analysis service to predict the functional consequences of known and unknown variants.

Proper citation: Variant Effect Predictor (RRID:SCR_007931) Copy   

•   Source: SciCrunch Registry

https://amoebadb.org/amoeba/

Integrated genomic and functional genomic database for Entamoeba and Acanthamoeba parasites. Contains genomes of three Entamoeba species and microarray expression data for E. histolytica. Integrates whole genome sequence and annotation and includes experimental data and environmental isolate sequences provided by community researchers.

Proper citation: AmoebaDB (RRID:SCR_017592) Copy   

•   Source: SciCrunch Registry

https://github.com/theislab/anndata

Software tool that provides scalable way of keeping track of data and learned annotations. Initially built for Scanpy. Used as generic class for handling annotated data matrices. Stores data matrix with annotations of observations (samples, cells) and variables (features, genes), and unstructured annotations.

Proper citation: Anndata (RRID:SCR_018209) Copy   

•   Source: SciCrunch Registry

http://www.mousebook.org/

Databases and portal to data and ordering mouse strains from MRC Harwell including mouse stocks in FESA (Frozen Embryo and Sperm Archive), mutants from the mutagenesis screen, the ENU DNA archive, standardized phenotyping procedures, imprinting genes and chromosome anomalies. The portal integrates curated information from the MRC Harwell stock resource, and other Harwell databases, with information from external data resources to provide added value information above and beyond what is available through other routes such as IMSR (International Mouse Stain Resource). MouseBook can be searched either using an intuitive Google-style free text search or using the Mammalian Phenotype Ontology (MP) tree structure. Text searches can be on gene, allele, strain identifier (e.g. MGI ID) or phenotype term and are assisted by automatic recognition of term types and autocompletion of gene and allele names covered by the database. Results are returned in a tabbed format providing categorized results identified from each of the catalogs in MouseBook. Individual results lines from each catalog include information on gene, allele, chromosomal location and phenotype and provide a simple click-through link to further information as well as ordering the strain. The infrastructure underlying MouseBook has been designed to be extensible, allowing additional data sources to be added enabling other sites to make their data directly available through MouseBook.

Proper citation: MouseBook (RRID:SCR_006358) Copy   

•   Source: SciCrunch Registry

http://www.hudsen.org/

Forum for researchers in human developmental biology and related fields to meet and establish links.

Proper citation: HUDSEN (RRID:SCR_006324) Copy   

•   Source: SciCrunch Registry

http://ahdb.ee.ncku.edu.tw/

Database of apo and holo structure pairs of proteins before and after binding. Various protein functions have been shown directly associated with conformational transitions triggered by binding other molecules. Tertiary structures determined in the unbound and bound state are usually named apo and holo structures, respectively. AH-DB is the largest database of apo-holo structure pairs and provides a sophisticated interface to search and view the collected data. It contains 746314 apo-holo pairs of 3638 proteins from 702 organisms.

Proper citation: Apo and Holo structures DataBase (RRID:SCR_004800) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/bioproject

Database of biological data related to a single initiative, originating from a single organization or from a consortium. A BioProject record provides users a single place to find links to the diverse data types generated for that project. It is a searchable collection of complete and incomplete (in-progress) large-scale sequencing, assembly, annotation, and mapping projects for cellular organisms. Submissions are supported by a web-based Submission Portal. The database facilitates organization and classification of project data submitted to NCBI, EBI and DDBJ databases that captures descriptive information about research projects that result in high volume submissions to archival databases, ties together related data across multiple archives and serves as a central portal by which to inform users of data availability. BioProject records link to corresponding data stored in archival repositories. The BioProject resource is a redesigned, expanded, replacement of the NCBI Genome Project resource. The redesign adds tracking of several data elements including more precise information about a project''''s scope, material, and objectives. Genome Project identifiers are retained in the BioProject as the ID value for a record, and an Accession number has been added. Database content is exchanged with other members of the International Nucleotide Sequence Database Collaboration (INSDC). BioProject is accessible via FTP.

Proper citation: NCBI BioProject (RRID:SCR_004801) Copy   

•   Source: SciCrunch Registry

http://swift.cmbi.ru.nl/gv/hssp/

HSSP (homology-derived structures of proteins) is a derived database merging structural (2-D and 3-D) and sequence information (1-D). For each protein of known 3D structure from the Protein Data Bank, the database has a file with all sequence homologues, properly aligned to the PDB protein. Homologues are very likely to have the same 3D structure as the PDB protein to which they have been aligned. As a result, the database is not only a database of sequence aligned sequence families, but it is also a database of implied secondary and tertiary structures. Likely secondary structure are carried over from the PDB protein to each homologous protein. Tertiary structure models can be built by fitting the sequence of the homologue as aligned into the 3D template of the protein of known structure. Special software is needed to construct 3D models by homology, such WHATIF by Gert Vriend or MaxSprout by Liisa Holm and Chris Sander. The command rsync can be used to obtain a local copy of the HSSP. We appreciate receiving an Email from people who do so, but there are no strings attached. Everybody can freely download the files, academia and industry alike. If your institute''s firewall doesn''t allow you to use the (preferred) rsync way of obtaining HSSP files, feel free to work with FTP. The files are in that case available from: ftp://ftp.cmbi.ru.nl//pub/molbio/data/hssp/

Proper citation: HSSP (RRID:SCR_004953) Copy   

•   Source: SciCrunch Registry

http://sms.cbi.cnptia.embrapa.br/SMS/STINGm/SMSReport/

Sting Report is a database of amino acid sequences, structures, functions, and parameters. It allows users to easily extract from the Blue Star Sting Database detailed but focused information about an individual amino acid, which belongs to a structure described in a PDB file. The extracted information is presented as a series of GIF images and a table, which are generated by Blue Star Sting modules and contain values of up to 125 sequence/structure/function descriptors/parameters. The HTML page resulting from a query on Sting Report, containing the GIF images and the table, is printable, and can also be composed and visualized at a computer platform with elementary configuration.

Proper citation: STING Report (RRID:SCR_005121) Copy   

•   Source: SciCrunch Registry

http://www.glycome-db.org/

GlycomeDB is a database of all known carbohydrate structures. This was achieved by crosslinking several other databases of carbohydrate structures by using the GlycoCT XML language specification. We have analyzed all of the existing public databases and defined a sequence format based on XML (GlycoCT) capable of storing all structural information of carbohydrate sequences. We have implemented a library of parsers for the interpretation of the different encoding schemes for carbohydrates. With this library we have translated the carbohydrate sequences of all freely available databases (CFG , KEGG, GLYCOSCIENCES.de, BCSDB and Carbbank) to GlycoCT, and created a new database (GlycomeDB) containing all structures and annotations. During the process of data integration we found multiple inconsistencies in the existing databases which were corrected in collaboration with the responsible curators. With the new database, GlycomeDB, it is possible to get an overview of all carbohydrate structures in the different databases and to crosslink common structures in the different databases. Scientists are now able to search for a particular structure in the meta database and get information about the occurrence of this structure in the five carbohydrate structure databases.

Proper citation: glycomedb (RRID:SCR_005717) Copy   

•   Source: SciCrunch Registry

http://tropgenedb.cirad.fr/tropgene/JSP/index.jsp

A database that manages genetic and genomic information about tropical crops studied by Cirad. The database is organised into crop specific modules. Each module includes data on genetic ressources (agro-morphological data, parentages, allelic diversity), information on molecular markers, genetics maps, result of QTL analyses, data from physical mapping, sequences, genes, as well as corresponding references. GENE DB interface has been designed to allow quick consultations as well as complex queries. Nine modules are presently on line.

Proper citation: TropGENE DB (RRID:SCR_005716) 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

http://www.neuroepigenomics.org/methylomedb/

A database containing genome-wide brain DNA methylation profiles for human and mouse brains. The DNA methylation profiles were generated by Methylation Mapping Analysis by Paired-end Sequencing (Methyl-MAPS) method and analyzed by Methyl-Analyzer software package. The methylation profiles cover over 80% CpG dinucleotides in human and mouse brains in single-CpG resolution. The integrated genome browser (modified from UCSC Genome Browser allows users to browse DNA methylation profiles in specific genomic loci, to search specific methylation patterns, and to compare methylation patterns between individual samples. Two species were included in the Brain Methylome Database: human and mouse. Human postmortem brain samples were obtained from three distinct cortical regions, i.e., dorsal lateral prefrontal cortex (dlPFC), ventral prefrontal cortex (vPFC), and auditory cortex (AC). Human samples were selected from our postmortem brain collection with extensive neuropathological and psychopathological data, as well as brain toxicology reports. The Department of Psychiatry of Columbia University and the New York State Psychiatric Institute have assembled this brain collection, where a validated psychological autopsy method is used to generate Axis I and II DSM IV diagnoses and data are obtained on developmental history, history of psychiatric illness and treatment, and family history for each subject. The mouse sample (strain 129S6/SvEv) DNA was collected from the entire left cerebral hemisphere. The three human brain regions were selected because they have been implicated in the neuropathology of depression and schizophrenia. Within each cortical region, both disease and non-psychiatric samples have been profiled (matching subjects by age and sex in each group). Such careful matching of subjects allows one to perform a wide range of queries with the ability to characterize methylation features in non-psychiatric controls, as well as detect differentially methylated domains or features between disease and non-psychiatric samples. A total of 14 non-psychiatric, 9 schizophrenic, and 6 depression methylation profiles are included in the database.

Proper citation: MethylomeDB (RRID:SCR_005583) Copy   

•   Source: SciCrunch Registry