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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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.mir2disease.org/

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   

•   Source: SciCrunch Registry

http://mirgator.kobic.re.kr/

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   

•   Source: SciCrunch Registry

http://www.noncode.org/

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   

•   Source: SciCrunch Registry

http://networkin.info/

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.p3db.org

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://stitch.embl.de

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://polya.umdnj.edu/

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   

•   Source: SciCrunch Registry

http://pirnabank.ibab.ac.in/

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   

•   Source: SciCrunch Registry

http://sideeffects.embl.de/

Database containing information on marketed medicines and their recorded adverse drug reactions. The information is extracted from public documents and package inserts. The available information include side effect frequency, drug and side effect classifications as well as links to further information, for example drug-target relations. The SIDER Side Effect Resource represents an effort to aggregate dispersed public information on side effects. To our knowledge, no such resource exist in machine-readable form despite the importance of research on drugs and their effects. The creation of this resource was motivated by the many requests for data that we received related to our paper (Campillos, Kuhn et al., Science, 2008, 321(5886):263-6.) on the utilization of side effects for drug target prediction. Inclusion of side effects as readouts for drug treatment should have many applications and we hope to be able to enhance the respective research with this resource. You may browse the drugs by name, browse the side effects by name, download the current version of SIDER, or use the search interface.

Proper citation: SIDER (RRID:SCR_004321) Copy   

•   Source: SciCrunch Registry

http://exac.broadinstitute.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. An aggregated data platform for genome sequencing data created by a coalition of investigators seeking to aggregate and harmonize exome sequencing data from a variety of large-scale sequencing projects, and to make summary data available for the wider scientific community. The data set provided on this website spans 61,486 unrelated individuals sequenced as part of various disease-specific and population genetic studies. They have removed individuals affected by severe pediatric disease, so this data set should serve as a useful reference set of allele frequencies for severe disease studies. All of the raw data from these projects have been reprocessed through the same pipeline, and jointly variant-called to increase consistency across projects. They ask that you not publish global (genome-wide) analyses of these data until after the ExAC flagship paper has been published, estimated to be in early 2015. If you''re uncertain which category your analyses fall into, please email them. The aggregation and release of summary data from the exomes collected by the Exome Aggregation Consortium has been approved by the Partners IRB (protocol 2013P001477, Genomic approaches to gene discovery in rare neuromuscular diseases).

Proper citation: ExAc (RRID:SCR_004068) Copy   

•   Source: SciCrunch Registry

http://www.yeastgenome.org/

A curated database that provides comprehensive integrated biological information for Saccharomyces cerevisiae along with search and analysis tools to explore these data. SGD allows researchers to discover functional relationships between sequence and gene products in fungi and higher organisms. The SGD also maintains the S. cerevisiae Gene Name Registry, a complete list of all gene names used in S. cerevisiae which includes a set of general guidelines to gene naming. Protein Page provides basic protein information calculated from the predicted sequence and contains links to a variety of secondary structure and tertiary structure resources. Yeast Biochemical Pathways allows users to view and search for biochemical reactions and pathways that occur in S. cerevisiae as well as map expression data onto the biochemical pathways. Literature citations are provided where available.

Proper citation: SGD (RRID:SCR_004694) Copy   

•   Source: SciCrunch Registry

http://pfam.xfam.org/

A database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).

Proper citation: Pfam (RRID:SCR_004726) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/jpopgen/dbNSFP

A database for functional prediction and annotation of all potential non-synonymous single-nucleotide variants (nsSNVs) in the human genome. Version 2.0 is based on the Gencode release 9 / Ensembl version 64 and includes a total of 87,347,043 nsSNVs and 2,270,742 essential splice site SNVs. It compiles prediction scores from six prediction algorithms (SIFT, Polyphen2, LRT, MutationTaster, MutationAssessor and FATHMM), three conservation scores (PhyloP, GERP++ and SiPhy) and other related information including allele frequencies observed in the 1000 Genomes Project phase 1 data and the NHLBI Exome Sequencing Project, various gene IDs from different databases, functional descriptions of genes, gene expression and gene interaction information, etc. Some dbNSFP contents (may not be up-to-date though) can also be accessed through variant tools, ANNOVAR, KGGSeq, UCSC Genome Browser''s Variant Annotation Integrator, Ensembl Variant Effect Predictor and HGMD.

Proper citation: dbNSFP (RRID:SCR_005178) Copy   

•   Source: SciCrunch Registry

http://solgenomics.net/

A clade oriented, community curated database containing genomic, genetic, phenotypic and taxonomic information for plant genomes. Genomic information is presented in a comparative format and tied to important plant model species such as Arabidopsis. SGN provides tools such as: BLAST searches, the SolCyc biochemical pathways database, a CAPS experiment designer, an intron detection tool, an advanced Alignment Analyzer, and a browser for phylogenetic trees. The SGN code and database are developed as an open source project, and is based on database schemas developed by the GMOD project and SGN-specific extensions.

Proper citation: SGN (RRID:SCR_004933) Copy   

•   Source: SciCrunch Registry