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http://www.suiplus.com

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on December 1, 2023. System developed under guidance of experts in reproduction and andrology from Andrological Branch of Chinese Medical Association and Research Institute of National Health Planning Commission. Designed according to standard of 5th edition of World Health Organization laboratory manual for examination and processing of human semen.

Proper citation: SSA-II sperm analysis system (RRID:SCR_017387) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/homology.shtml

MGI contains homology information for mouse, human, rat, chimp, dog and other species. Complete set of human, chimpanzee, rhesus macaque, dog, cattle, rat, chicken, western clawed frog and zebrafish Homology Classes for mouse genes. Report includes Chromosome and EntrezGene and OMIM IDs. Report of Human and Mouse Homology Classes sorted by HomoloGene ID includes associated nucleotide and protein sequences, Chromosome and OMIM IDs. Report of Human and Mouse Homology with phenotype annotations. Several additional MGI reports are available, including those for Gene Ontology, Phenotypes and Nomenclature.

Proper citation: Vertebrate Homology (RRID:SCR_017517) Copy   

•   Source: SciCrunch Registry

https://anvilproject.org/

Portal to facilitate integration and computing on and across large datasets generated by NHGRI programs, as well as initiatives funded by National Institutes of Health or by other agencies that support human genomics research. Resource for genomic scientific community, that leverages cloud based infrastructure for democratizing genomic data access, sharing and computing across large genomic, and genomic related data sets. Component of federated data ecosystem, and is expected to collaborate and integrate with other genomic data resources through adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles, as their specifications emerge from scientific community. Will provide collaborative environment, where datasets and analysis workflows can be shared within consortium and be prepared for public release to broad scientific community through AnVIL user interfaces.

Proper citation: Analysis, Visualization, and Informatics Lab-space (AnVIL) (RRID:SCR_017469) Copy   

•   Source: SciCrunch Registry

https://portal.brain-map.org/atlases-and-data/rnaseq

Software tool to visualize and analyze transcriptomics data and transcriptomic cell types for mouse and human, all directly in web browser. To explore gene expression heatmap across cell types in datasets, search for genes of interest, explore tSNE visualization, colored by cell types or expression of genes of interest, visualize dataset’s sampling strategy to see how cells and nuclei were sampled across brain areas, cortical layer, and other dimensions, find cell type of interest in one visualization and see its characteristics in different visualization.Used for Allen Brain Map Cell Types Database to Browse Data: Human - Multiple Cortical Areas, and Mouse - Cortex and Hippocampus.

Proper citation: Transcriptomics Explorer (RRID:SCR_017567) Copy   

•   Source: SciCrunch Registry

https://kg.ebrains.eu/

Metadata management system built for EBRAINS. Multi modal metadata store which brings together information from different areas of Human Brain Project as well as from external partners. Graph database tracks linkage between experimental data and neuroscientific data science supporting more extensive data reuse and complex computational research.Supports rich terminologies, ontologies and controlled vocabularies. Built by design to support iterative elaborations of common standards and supports these by probabilistic suggestion and review systems.

Proper citation: EBRAINS Knowledge Graph (RRID:SCR_017612) Copy   

•   Source: SciCrunch Registry

https://scdevdb.deepomics.org/

Database for insights into single cell gene expression profiles during human developmental processes. Interactive database provides DE gene lists in each developmental pathway, t-SNE map, and GO and KEGG enrichment analysis based on these differential genes.

Proper citation: Single Cell Developmental Database (RRID:SCR_017546) Copy   

•   Source: SciCrunch Registry

http://ccg.vital-it.ch/snp2tfbs

Collection of text files providing specific annotations for human single nucleotide polymorphisms (SNPs), namely whether they are predicted to abolish, create or change the affinity of one or several transcription factor (TF) binding sites. Used to investigate the molecular mechanisms underlying regulatory variation in the human genome. SNP2TFBS is also accessible over a web interface, enabling users to view the information provided for an individual SNP, to extract SNPs based on various search criteria, to annotate uploaded sets of SNPs or to display statistics about the frequencies of binding sites affected by selected SNPs.

Proper citation: SNP2TFBS (RRID:SCR_016885) Copy   

•   Source: SciCrunch Registry

https://omicc.niaid.nih.gov

Community based, biologist friendly web platform for creating and meta analyzing annotated gene expression data compendia., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: OMiCC (RRID:SCR_016604) Copy   

•   Source: SciCrunch Registry

https://www.humancellatlas.org

Software tool as a catalog of comprehensive reference of human cells based on their stable properties, transient features, locations and abundances. Map to show the relationships among its elements. Open data international collaborative project involving diverse scientific communities to provide a framework for understanding cellular dysregulation in human disease.

Proper citation: Human Cell Atlas (RRID:SCR_016530) Copy   

•   Source: SciCrunch Registry

http://www.broadcvdi.org/

Platform for analysis of the genetics of cardiovascular disease.Used for searching and analysis of human genetic information linked to myocardial infarction, atrial fibrillation and related traits while protecting the integrity and confidentiality of the data.

Proper citation: Cardiovascular Disease Knowledge Portal (RRID:SCR_016536) Copy   

•   Source: SciCrunch Registry

https://commonfund.nih.gov/hubmap

Project to facilitate research on single cells within tissues by supporting data generation and technology development to explore the relationship between cellular organization and function, as well as variability in normal tissue organization at the level of individual cells. Framework for functional mapping the human body with cellular resolution.Designed to support diverse spatial and non-spatial omics and imaging data types and to integrate with a wide range of analysis workflows.

Proper citation: The Human BioMolecular Atlas Program (RRID:SCR_016922) Copy   

•   Source: SciCrunch Registry

https://kpmp.org

Project to ethically obtain and evaluate human kidney biopsies from participants with Acute Kidney Injury (AKI) or Chronic Kidney Disease (CKD), create a kidney tissue atlas, define disease subgroups, and identify critical cells, pathways, and targets for novel therapies. Used to develop the next generation of software tools to visualize and understand the various components of kidney diseases and to optimize data collection. Multi site collaboration comprised of patients, clinicians, and investigators from across the United States.

Proper citation: Kidney Precision Medicine Project (RRID:SCR_016920) Copy   

•   Source: SciCrunch Registry

http://bcb.cs.tufts.edu/dflat/

We are an interdisciplinary team dedicated to annotating gene function related to human fetal development. We are contributing new functional annotation to the Gene Ontology, curating and mining gene sets suitable for the interpretation of developmental genomic data, and creating the computational tools needed to apply genomics for better understanding the molecular mechanisms of human development. Our GO annotation is in the process of being incorporated into the GOA public release. The GONE (Gene Ontology Non-Eligible) database is where we store annotations relevant to our research but that don''t quite meet GOA''s standards. Usually an annotation falls into this category because either the gene/protein described is a family of genes/proteins rather than a specific one, there is no UniProt ID to identify the gene/protein in the system, a GO term does not yet exist to describe the particular function, process, or location of the gene/protein, the species is not clearly identifiable in the paper, or the evidence is not as reliable (GO evidence codes TAS and NAS). As individual annotations these are more suspect than current GO annotation. However, for functional analysis of expression data, these gene sets can be valuable even with a certain amount of noise. We also include here a link to the supplementary data from our forthcoming PSB 2011 paper on gene set mining.

Proper citation: DFLAT (RRID:SCR_010738) Copy   

•   Source: SciCrunch Registry

http://bio-bwa.sourceforge.net/

Software for aligning sequencing reads against large reference genome. Consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM. First for sequence reads up to 100bp, and other two for longer sequences ranged from 70bp to 1Mbp.

Proper citation: BWA (RRID:SCR_010910) Copy   

•   Source: SciCrunch Registry

http://www.kabatdatabase.com/

The Kabat Database determines the combining site of antibodies based on the available amino acid sequences. The precise delineation of complementarity determining regions (CDR) of both light and heavy chains provides the first example of how properly aligned sequences can be used to derive structural and functional information of biological macromolecules. The Kabat database now includes nucleotide sequences, sequences of T cell receptors for antigens (TCR), major histocompatibility complex (MHC) class I and II molecules, and other proteins of immunological interest. The Kabat Database searching and analysis tools package is an ASP.NET web-based portal containing lookup tools, sequence matching tools, alignment tools, length distribution tools, positional correlation tools and much more. The searching and analysis tools are custom made for the aligned data sets contained in both the SQL Server and ASCII text flat file formats. The searching and analysis tools may be run on a single PC workstation or in a distributed environment. The analysis tools are written in ASP.NET and C# and are available in Visual Studio .NET 2003/2005/2008 formats. The Kabat Database was initially started in 1970 to determine the combining site of antibodies based on the available amino acid sequences at that time. Bence Jones proteins, mostly from human, were aligned, using the now-known Kabat numbering system, and a quantitative measure, variability, was calculated for every position. Three peaks, at positions 24-34, 50-56 and 89-97, were identified and proposed to form the complementarity determining regions (CDR) of light chains. Subsequently, antibody heavy chain amino acid sequences were also aligned using a different numbering system, since the locations of their CDRs (31-35B, 50-65 and 95-102) are different from those of the light chains. CDRL1 starts right after the first invariant Cys 23 of light chains, while CDRH1 is eight amino acid residues away from the first invariant Cys 22 of heavy chains. During the past 30 years, the Kabat database has grown to include nucleotide sequences, sequences of T cell receptors for antigens (TCR), major histocompatibility complex (MHC) class I and II molecules and other proteins of immunological interest. It has been used extensively by immunologists to derive useful structural and functional information from the primary sequences of these proteins.

Proper citation: Kabat Database of Sequences of Proteins of Immunological Interest (RRID:SCR_006465) Copy   

•   Source: SciCrunch Registry

http://bioapps.rit.albany.edu/MITOPRED/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. It predicts nuclear-encoded mitochondrial proteins from all eukaryotic species including plants. Prediction is based on the occurrence patterns of Pfam domains (version 16.0) in different cellular locations, amino acid composition and pI value differences between mitochondrial and non-mitochondrial locations. Additionally, you may download MITOPRED predictions for complete proteomes. Re-calculated predictions are instantly accessible for proteomes of Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila, Homo sapiens, Mus musculus and Arabidopsis species as well as all the eukaryotic sequences in the Swiss-Prot and TrEMBL databases. Queries, at different confidence levels, can be made through four distinct options: (i) entering Swiss-Prot/TrEMBL accession numbers; (ii) uploading a local file with such accession numbers; (iii) entering protein sequences; (iv) uploading a local file containing protein sequences in FASTA format. The Mitopred algorithm works based on the differences in the Pfam domain occurrence patters and amino acid composition differences in different cellular compartments. Location specific Pfam domains have been determined from the entire eukaryotic set of Swissprot database. Similarly, differences in the amino acid composition between mitochondrial and non-mitochondrial sequences were pre-calculated. This information is used to calculate location-specific amino acid weights that are used to calculate amino acid score. Similarly, pI average values of the N-terminal 25 residues in different cellular location were also determined. This knowledge-base is accessed by the program during execution.

Proper citation: mitopred (RRID:SCR_006135) Copy   

•   Source: SciCrunch Registry

http://neurocritic.blogspot.com/

The Neurocritic is a blog deconstructing the most sensationalistic recent findings in Human Brain Imaging, Cognitive Neuroscience, and Psychopharmacology. Born in West Virginia in 1980, The Neurocritic embarked upon a roadtrip across America at the age of thirteen with his mother. She abandoned him when they reached San Francisco and The Neurocritic descended into a spiral of drug abuse and prostitution. At fifteen, The Neurocritic''s psychiatrist encouraged him to start writing as a form of therapy.

Proper citation: Neurocritic (RRID:SCR_006528) Copy   

•   Source: SciCrunch Registry

http://igs-server.cnrs-mrs.fr/mgdb/Rickettsia/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 18, 2016. Rickettsia are obligate intracellular bacteria living in arthropods. They occasionally cause diseases in humans. To understand their pathogenicity, physiologies and evolutionary mechanisms, RicBase is sequencing different species of Rickettsia. Up to now we have determined the genome sequences of R. conorii, R. felis, R. bellii, R. africae, and R. massiliae. The RicBase aims to organize the genomic data to assist followup studies of Rickettsia. This website contains information on R. conorii and R. prowazekii. A R. conorii and R. prowazekii comparative genome map is also available. Images of genome maps, dendrogram, and sequence alignment allow users to gain a visualization of the diagrams.

Proper citation: Rickettsia Genome Database (RRID:SCR_007102) Copy   

•   Source: SciCrunch Registry

http://bond.unleashedinformatics.com/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented on August 19,2019.BOND, which requires registration of a free account, is a resource used to perform cross-database searches of available sequence, interaction, complex and pathway information. BOND integrates a range of component databases including GenBank and BIND, the Biomolecular Interaction Network Database. BOND contains 70+ million biological sequences, 33,000 structures, 38,000 GO terms, and over 200,000 human curated interactions contained in BIND, and is open access. BOND serves the interests of the developing global interactome effort encompassing the genomic, proteomic and metabolomic research communities. BOND is the first open access search resource to integrate sequence and interaction information. BOND integrates BLAST functionality, and contains a well-documented API. BOND also stores annotation links for sequences, including links to Genome Ontology descriptions, MedLine abstracts, taxon identifiers, associated structures, redundant sequences, sequence neighbors, conserved domains, data base cross-references, Online Mendalian Inheritance in Man identifiers, LocusLink identifiers and complete genomes. BIND on BOND The Biomolecular Interaction Network Database (BIND), a component database of BOND, is a collection of records documenting molecular interactions. The contents of BIND include high-throughput data submissions and hand-curated information gathered from the scientific literature. BIND is an interaction database with three classifications for molecular associations: molecules that associate with each other to form interactions, molecular complexes that are formed from one or more interaction(s) and pathways that are defined by a specific sequence of two or more interactions.Interactions A BIND record represents an interaction between two or more objects that is believed to occur in a living organism. A biological object can be a protein, DNA, RNA, ligand, molecular complex, gene, photon or an unclassified biological entity. BIND records are created for interactions which have been shown experimentally and published in at least one peer-reviewed journal. A record also references any papers with experimental evidence that support or dispute the associated interaction. Interactions are the basic units of BIND and can be linked together to form molecular complexes or pathways. The BIND interaction viewer is a tool to visualize and analyze molecular interactions, complexes and pathways. The BIND interaction viewer uses Ontoglyphs to display information about a protein via attributes such as molecular function, biological process and sub-cellular localization. Ontoglyphs allow to graphically and interactively explore interaction networks, by visualizing interactions in the context of 34 functional, 25 binding specificity and 24 sub-cellular localization Ontoglyphs categories. We will continue to provide an open access version of BOND, providing its subscribers with free, unlimited access to a core content set. But we are confident you will soon want to upgrade to BONDplus.

Proper citation: Biomolecular Object Network Databank (RRID:SCR_007433) Copy   

•   Source: SciCrunch Registry

http://mips.gsf.de/genre/proj/ustilago/

The MIPS Ustilago maydis Genome Database aims to present information on the molecular structure and functional network of the entirely sequenced, filamentous fungus Ustilago maydis. The underlying sequence is the initial release of the high quality draft sequence of the Broad Institute. The goal of the MIPS database is to provide a comprehensive genome database in the Genome Research Environment in parallel with other fungal genomes to enable in depth fungal comparative analysis. The specific aims are to: 1. Generate and assemble Whole Genome Shotgun sequence reads yielding 10X coverage of the U. maydis genome 2. Integrate the genomic sequence assembly with physical maps generated by Bayer CropScience 3. Perform automated annotation of the sequence assembly 4. Align the strain 521 assembly with the FB1 assembly provided by Exelixis 5. Release the sequence assembly and results of our annotation and analysis to public Ustilago maydis is a basidiomycete fungal pathogen of maize and teosinte. The genome size is approximately 20 Mb. The fungus induces tumors on host plants and forms masses of diploid teliospores. These spores germinate and form haploid meiotic products that can be propagated in culture as yeast-like cells. Haploid strains of opposite mating type fuse and form a filamentous, dikaryotic cell type that invades plant tissue to reinitiate infection. Ustilago maydis is an important model system for studying pathogen-host interactions and has been studied for more than 100 years by plant pathologists. Molecular genetic research with U. maydis focuses on recombination, the role of mating in pathogenesis, and signaling pathways that influence virulence. Recently, the fungus has emerged as an excellent experimental model for the molecular genetic analysis of phytopathogenesis, particularly in the characterization of infection-specific morphogenesis in response to signals from host plants. Ustilago maydis also serves as an important model for other basidiomycete plant pathogens that are more difficult to work with in the laboratory, such as the rust and bunt fungi. Genomic sequence of U. maydis will also be valuable for comparative analysis of other fungal genomes, especially with respect to understanding the host range of fungal phytopathogens. The analysis of U. maydis would provide a framework for studying the hundreds of other Ustilago species that attack important crops, such as barley, wheat, sorghum, and sugarcane. Comparisons would also be possible with other basidiomycete fungi, such as the important human pathogen C. neoformans. Commercially, U. maydis is an excellent model for the discovery of antifungal drugs. In addition, maize tumors caused by U. maydis are prized in Hispanic cuisine and there is interest in improving commercial production. The complete putative gene set of the Broad Institute''s second release is loaded into the database and in addition all deviating putative genes from a putative gene set produced by MIPS with different gene prediction parameters are also loaded. The complete dataset will then be analysed, gene predictions will be manually corrected due to combined information derived from different gene prediction algorithms and, more important, protein and EST comparisons. Gene prediction will be restricted to ORFs larger than 50 codons; smaller ORFs will be included only if similarities to other proteins or EST matches confirm their existence or if a coding region was postulated by all prediction programs used. The resulting proteins will be annotated. They will be classified according to the MIPS classification catalogue receiving appropriate descriptions. All proteins with a known, characterized homolog will be automatically assigned to functional categories using the MIPS functional catalog. All extracted proteins are in addition automatically analysed and annotated by the PEDANT suite.

Proper citation: MIPS Ustilago maydis Database (RRID:SCR_007563) Copy   

•   Source: SciCrunch Registry