Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
http://compbio.cs.queensu.ca/F-SNP/
F-SNP database provides integrated information about the functional effects of SNPs obtained from 16 bioinformatics tools and databases. The functional effects are predicted and indicated at the splicing, transcriptional, translational, and post-translational level. As such, the F-SNP database helps identify and focus on SNPs with potential pathological effect to human health. Users can find SNP's based on ID, associated disease, gene, or chromosomal region.
Proper citation: F-SNP: a collection of functional SNPs, specifically prioritized for disease association studies (RRID:SCR_007653) Copy
http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=mirgenv3
An integrated database of positional relationships between animal miRNAs and genomic annotation sets and animal miRNA targets according to combinations of widely used target prediction programs. miRGen has three connected interfaces which query this data. The Genomics interface allows the user to explore where whole-genome collections of miRNAs are located with respect to UCSC genome browser annotation sets such as Known Genes, Refseq Genes, Genscan predicted genes, CpG islands, and pseudogenes. The Targets interface provides access to unions and intersections of four widely used target prediction programs, and experimentally supported targets from TarBase. The Clusters interface provides predicted miRNA clusters at any given inter-miRNA distance, and provides specific functional information on the targets of miRNAs within each cluster.
Proper citation: miRGen (RRID:SCR_007796) Copy
A manually curated database, aims at providing a comprehensive resource of miRNA deregulation in various human diseases. Each entry in the miR2Disease contains detailed information on a miRNA-disease relationship, including miRNA ID, disease name, a brief description of the miRNA-disease relationship, miRNA expression pattern in the disease state, detection method for miRNA expression, experimentally verified miRNA target gene(s), and literature reference . All entries can be retrieved by miRNA ID, disease name or target gene. miR2Disease will be updated bimonthly. miR2Disease sincerely looks forward to recently established relationship between miRNA and human diseases to be submitted.
Proper citation: miR2Disease (RRID:SCR_007792) Copy
Database of compiled, public, deep sequencing miRNA data and several novel tools to facilitate exploration of massive data. The miR-seq browser supports users to examine short read alignment with the secondary structure and read count information available in concurrent windows. Features such as sequence editing, sorting, ordering, import and export of user data are of great utility for studying iso-miRs, miRNA editing and modifications. miRNA����??target relation is essential for understanding miRNA function. Coexpression analysis of miRNA and target mRNAs, based on miRNA-seq and RNA-seq data from the same sample, is visualized in the heat-map and network views where users can investigate the inverse correlation of gene expression and target relations, compiled from various databases of predicted and validated targets.
Proper citation: miRGator (RRID:SCR_007793) Copy
Collection of non-coding RNAs (excluding tRNAs and rRNAs) as an integrated knowledge database. Used to get text information such as class,name,location,related publication,mechanism through which it exerts its function, view figures which show their location in the genome or in a specific DNA fragment, and the regulation elements flanking the ncRNA gene sequences.
Proper citation: NONCODE (RRID:SCR_007822) Copy
A method for predicting in vivo kinase-substrate relationships, that augments consensus motifs with context for kinases and phosphoproteins. This website allows a user to browse/search and investigate predictions made using the NetworKIN algorithm. The site is powered by the latest phosphoproteome in Phospho.ELM. Alternatively users can submit their own protein sequences and phosphorylation sites and obtain new NetworKIN predictions.
Proper citation: NetworKIN (RRID:SCR_007818) Copy
http://www.tigr.org/tdb/humgen/bac_end_search/bac_end_intro.html
The Human BAC Ends Database is a database of sequences from the ends of bacterial artificial chromosome (BAC) clones. A whole genome sequencing approach has been described in a map-as-you-go strategy. The complete sequence of a seed BAC is searched against a BAC end database and the minimally overlapping clones in each direction are selected for sequencing. As coverage increases, BAC end sequences provide samples for whole genome survey. It currently contains 743,000 end sequences from 470,000 clones (20 X clone coverage and 12% sequence coverage), generated by TIGR, UofWashington and CalTech, providing a sequence marker every 5 kb across the genome. The coverage by paired-ends on chromosome 22 is over 5X. The project is funded by DOE.
Proper citation: Human BAC Ends Database (RRID:SCR_007727) Copy
It was established with an overall objective to provide a resource of protein phosphorylation data from multiple plants. P3DB was constructed with a dataset from oilseed rape. The data was obtained using a combination of data-dependent neutral loss and multistage activation mass spectrometry. The dataset includes 14,670 non-redundant phosphorylation sites from 8,894 phospho-peptides in 6,382 substrate proteins.
Proper citation: Plant Protein Phosphorylation Database (RRID:SCR_007841) Copy
http://www.comparative-legumes.org/
LIS is a publicly accessible legume resource that integrates genetic and molecular data from multiple legume species and enables cross-species genomic, transcript and map comparisons. The intent of the LIS is to help researchers leverage data-rich model plants to fill knowledge gaps across crop plant species and provide the ability to traverse between interrelated data types. LIS, a component of the Model Plant Initiative (MPI), is being developed as part of a cooperative research agreement between the National Center for Genome Resources (NCGR) and the USDA Agricultural Research Service (ARS).
Proper citation: Legume Information System (RRID:SCR_007761) Copy
http://supfam.org/SUPERFAMILY/
SUPERFAMILY is a database of structural and functional protein annotations for all completely sequenced organisms. The SUPERFAMILY annotation is based on a collection of hidden Markov models, which represent structural protein domains at the SCOP superfamily level. A superfamily groups together domains which have an evolutionary relationship. The annotation is produced by scanning protein sequences from over 1,700 completely sequenced genomes against the hidden Markov models.
Proper citation: SUPERFAMILY (RRID:SCR_007952) Copy
Database to explore known and predicted interactions of chemicals and proteins. It integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug-target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. The database contains interaction information for over 68,000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database.
Proper citation: Search Tool for Interactions of Chemicals (RRID:SCR_007947) Copy
http://www.bioinfodatabase.com/pint/
A protein-protein interactions thermodynamic database which contains data of several thermodynamic parameters along with sequence and structural information experimental conditions and literature information. Each entry contains numerical data for features of the interacting proteins such as the free energy change, dissociation constant, association constant, enthalpy change, and heat capacity change. PINT includes: the name and source of the proteins involved in binding, SWISS-PROT and Protein Data Bank (PDB) codes, secondary structure and solvent accessibility of residues at mutant positions, measuring methods, and experimental conditions such as buffers, ions and additives, and literature information. PINT is cross-linked with other related databases such as PIR, SWISS-PROT, PDB and the NCBI PUBMED literature database.
Proper citation: PINT (RRID:SCR_007856) Copy
A database of mRNA polyadenylation sites. PolyA_DB version 1 contains human and mouse poly(A) sites that are mapped by cDNA/EST sequences. PolyA_DB version 2 contains poly(A) sites in human, mouse, rat, chicken and zebrafish that are mapped by cDNA/EST and Trace sequences. Sequence alignments between orthologous sites are available. PolyA_SVM predicts poly(A) sites using 15 cis elements identified for human poly(A) sites.
Proper citation: PolyA DB (RRID:SCR_007867) Copy
A web analysis system and resource, which provides comprehensive information on piRNAs in the widely studied mammals. It compiles all the possible clusters of piRNAs and also depicts piRNAs along with the associated genomic elements like genes and repeats on a genome wide map. piRNABank mainly provides data onnamely Human, Mouse, Rat, Zebrafish, Platypus and a fruit fly, Drosophila.Search options have been designed to query and obtain useful data from this online resource. It also facilitates abstraction of sequences and structural features from piRNA data. piRNABank provides the following features: * Simple search * Search piRNA clusters * Search homologous piRNAs * piRNA visualization map * Analysis tools, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: piRNABank (RRID:SCR_007858) Copy
The official compendium for the Anatomical Therapeutic Chemical Classification System (ATC)-code descriptions. The Centre's main tasks are development and maintenance of the ATC/DDD system, including: * To classify drugs according to the ATC system. * Priority will be given to the classification of single substances, while combination products available internationally (i.e. important fixed combinations) will be dealt with as far as possible. * To establish DDDs for drugs which have been assigned an ATC code. * To review and revise as necessary the ATC classification system and DDDs. * To stimulate and influence the practical use of the ATC system by co-operating with researchers in the drug utilization field. Support: The WHO Collaborating Centre for Drug Statistics Methodology was established in 1982. The Centre is situated in Oslo at the Norwegian Institute of Public Health. The Centre is funded by the Norwegian government.
Proper citation: WHO Collaborating Centre for Drug Statistics Methodology (RRID:SCR_000677) Copy
http://cmbi.bjmu.edu.cn/mirsnp
Database of human SNPs in predicted miRNA-mRNA binding sites, based on information from dbSNP135 and mirBASE18. MirSNP is highly sensitive and covers most experiments confirmed SNPs that affect miRNA function. MirSNP may be combined with researchers' own GWAS or eQTL positive data sets to identify the putative miRNA-related SNPs from traits/diseases associated variants. They aim to update the MirSNP database as new versions of mirBASE and dbSNP database become available.
Proper citation: MirSNP (RRID:SCR_001629) Copy
http://www.bioguo.org/AnimalTFDB/
A comprehensive transcription factor (TF) database in which they identified and classified all the genome-wide TFs in 50 sequenced animal genomes (Ensembl release version 60). In addition to TFs, it also collects transcription co-factors and chromatin remodeling factors of those genomes, which play regulatory roles in transcription. Here they defined the TFs as proteins containing a sequence-specific DNA-binding domain (DBD) and regulating target gene expression. Currently, the AnimalTFDB classifies all the animal TFs into 72 families according to their conserved DBDs. Gene lists of transcription factors, transcription co-factors and chromatin remodeling factors of each species are available for downloading., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: AnimalTFDB (RRID:SCR_001624) Copy
Database of information of spa-typing of MRSA, or Staphylococcus aureus, that can be used to collate and harmonize data from various geographic regions.
Proper citation: Ridom SpaServer (RRID:SCR_001460) Copy
A database that focuses on experimentally verified protein-protein interactions mined from the scientific literature by expert curators. The curated data can be analyzed in the context of the high throughput data and viewed graphically with the MINT Viewer. This collection of molecular interaction databases can be used to search for, analyze and graphically display molecular interaction networks and pathways from a wide variety of species. MINT is comprised of separate database components. HomoMINT, is an inferred human protein interatction database. Domino, is database of domain peptide interactions. VirusMINT explores the interactions of viral proteins with human proteins. The MINT connect viewer allows you to enter a list of proteins (e.g. proteins in a pathway) to retrieve, display and download a network with all the interactions connecting them.
Proper citation: MINT (RRID:SCR_001523) Copy
A web-based genome analysis platform that integrates proprietary functional genomic data, metabolic reconstructions, expression profiling, and biochemical and microbiological data with publicly available information. Focused on microbial genomics, it provides better and faster identification of gene function across all organisms. Building upon a comprehensive genomic database integrated with a collection of microbial metabolic and non-metabolic pathways and using proprietary algorithms, it assigns functions to genes, integrates genes into pathways, and identifies previously unknown or mischaracterized genes, cryptic pathways and gene products. . * Automated and manual annotation of genes and genomes * Analysis of metabolic and non-metabolic pathways to understand organism physiology * Comparison of multiple genomes to identify shared and unique features and SNPs * Functional analysis of gene expression microarray data * Data-mining for target gene discovery * In silico metabolic engineering and strain improvement
Proper citation: ERGO (RRID:SCR_001243) Copy
Can't find your Tool?
We recommend that you click next to the search bar to check some helpful tips on searches and refine your search firstly. Alternatively, please register your tool with the SciCrunch Registry by adding a little information to a web form, logging in will enable users to create a provisional RRID, but it not required to submit.
Welcome to the NIF Resources search. From here you can search through a compilation of resources used by NIF and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that NIF has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on NIF then you can log in from here to get additional features in NIF such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
You can save any searches you perform for quick access to later from here.
We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.
If you are logged into NIF you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the sources that were queried against in your search that you can investigate further.
Here are the categories present within NIF that you can filter your data on
Here are the subcategories present within this category that you can filter your data on
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.