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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC165503/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 17, 2022. Designed to capture protein function, defined at molecular level as set of other molecules with which protein interacts or reacts along with molecular outcome. Archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display.

Proper citation: BIND (RRID:SCR_003576) Copy   

•   Source: SciCrunch Registry

http://caps.ncbs.res.in/3dswap/index.html

Curated knowledegbase of protein structures that are reported to be involved in 3-dimensional domain swapping. 3DSwap provides literature curated information and structure related information about 3D domain swapping in proteins. Information about swapping, hinge region, swapped region, extent of swapping, etc. are extracted from original research publications after extensive literature curation.

Proper citation: 3DSwap (RRID:SCR_004133) Copy   

•   Source: SciCrunch Registry

http://integromedb.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 26, 2016. Search engine that integrates over 100 curated and publicly contributed data sources and provides integrated views on the genomic, proteomic, transcriptomic, genetic and functional information currently available. Information featured in the database includes gene function, orthologies, gene expression, pathways and protein-protein interactions, mutations and SNPs, disease relationships, related drugs and compounds.

Proper citation: IntegromeDB (RRID:SCR_004620) Copy   

•   Source: SciCrunch Registry

http://amphoranet.pitgroup.org/

Webserver implementation of the AMPHORA2 workflow for phylogenetic analysis of metagenomic shotgun sequencing data. It is capable of assigning a probability-weighted taxonomic group for each phylogenetic marker gene found in the input metagenomic sample.

Proper citation: AmphoraNet (RRID:SCR_005009) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/biosamples/

Database that aggregates sample information for reference samples (e.g. Coriell Cell lines) and samples for which data exist in one of the EBI''''s assay databases such as ArrayExpress, the European Nucleotide Archive or PRoteomics Identificates DatabasE. It provides links to assays for specific samples, and accepts direct submissions of sample information. The goals of the BioSample Database include: # recording and linking of sample information consistently within EBI databases such as ENA, ArrayExpress and PRIDE; # minimizing data entry efforts for EBI database submitters by enabling submitting sample descriptions once and referencing them later in data submissions to assay databases and # supporting cross database queries by sample characteristics. The database includes a growing set of reference samples, such as cell lines, which are repeatedly used in experiments and can be easily referenced from any database by their accession numbers. Accession numbers for the reference samples will be exchanged with a similar database at NCBI. The samples in the database can be queried by their attributes, such as sample types, disease names or sample providers. A simple tab-delimited format facilitates submissions of sample information to the database, initially via email to biosamples (at) ebi.ac.uk. Current data sources: * European Nucleotide Archive (424,811 samples) * PRIDE (17,001 samples) * ArrayExpress (1,187,884 samples) * ENCODE cell lines (119 samples) * CORIELL cell lines (27,002 samples) * Thousand Genome (2,628 samples) * HapMap (1,417 samples) * IMSR (248,660 samples)

Proper citation: BioSample Database at EBI (RRID:SCR_004856) Copy   

•   Source: SciCrunch Registry

http://www.biosino.org/bodyfluid/

A database of bodily fluid proteome data. It contains information on proteins from humanplasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk, and amniotic fluid. Our body fluid protein database, Sys-BodyFluid, contains 11 body fluid proteomes, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk, and amniotic fluid. Over 10,000 proteins are included in the Sys-BodyFluid. These body fluid proteome data come from 50 peer-review publications of different laboratories all over the world. Protein annotation are provided including protein description, Gene ontology, Domain information, Protein sequence and involved pathway. User can access the proteome data by protein name, protein accession number, sequence similarity. In addition, user could perform query cross different body fluids to get more comprehensive understanding. The difference and similarity between these 11 body fluids are also analyzed. Thus , the Sys-BodyFluid database could serve as a reference database for body fluid research and disease proteomics. plasm, serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk, and amniotic fluid, protein, proteomics

Proper citation: Sys-BodyFluid (RRID:SCR_005335) Copy   

•   Source: SciCrunch Registry

http://wishart.biology.ualberta.ca/polysearch/index.htm

A web-based tool that supports more than 50 different classes of queries against nearly a dozen different types of text, scientific abstract or bioinformatic databases. The typical query supported by PolySearch is Given X, find all Y''s where X or Y can be diseases, tissues, cell compartments, gene/protein names, SNPs, mutations, drugs and metabolites. PolySearch also exploits a variety of techniques in text mining and information retrieval to identify, highlight and rank informative abstracts, paragraphs or sentences.

Proper citation: PolySearch (RRID:SCR_005291) Copy   

•   Source: SciCrunch Registry

http://ssd.rbvi.ucsf.edu/

The SSD has been developed to address the need for resources and tools for understanding large sets of superpositions in order to understand evolutionary relationships and to make predictions of function. We have therefore created the Structure Superposition Database (SSD) for accessing, viewing and understanding large sets of structure superposition data. It contains the results of pairwise, all-by-all superpositions of a representative set of 115 (beta/alpha) barrel structures (TIM barrels). The initial implementation of the SSD contains the results of pairwise, all-by-all superpositions of a representative set of 115 (/alpha)8 barrel structures (TIM barrels). Future plans call for extending the database to include representative structure superpositions for many additional folds. The SSD can be browsed with a user interface module developed as an extension to Chimera, an extensible molecular modeling program. Features of the user interface module facilitate viewing multiple superpositions together.

Proper citation: Structure Superposition Database (RRID:SCR_005236) Copy   

•   Source: SciCrunch Registry

http://transpogene.tau.ac.il/

A publicly available database of Transposed elements (TEs) which are located within protein-coding genes of 7 organisms: human, mouse, chicken, zebrafish, fruilt fly, nematode and sea squirt. Using TranspoGene the user can learn about the many aspects of the effect these TEs have on their hosting genes, such as: exonization events (including alternative splicing-related data), insertion of TEs into introns, exons, and promoters, specific location of the TE over the gene, evolutionary divergence of the TE from its consensus sequence and involvement in diseases. TranspoGene database is quickly searchable through its website, enables many kinds of searches and is available for download. TranspoGene contains information regarding specific type and family of the TEs, genomic and mRNA location, sequence, supporting transcript accession and alignment to the TE consensus sequence. The database also contains host gene specific data: gene name, genomic location, Swiss-Prot and RefSeq accessions, diseases associated with the gene and splicing pattern. The TranspoGene and microTranspoGene databases can be used by researchers interested in the effect of TE insertion on the eukaryotic transcriptome.

Proper citation: TranspoGene (RRID:SCR_005634) Copy   

•   Source: SciCrunch Registry

http://bigg.ucsd.edu/

A knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.

Proper citation: BiGG Database (RRID:SCR_005809) Copy   

•   Source: SciCrunch Registry

http://the_brain.bwh.harvard.edu/uniprobe/

Database that hosts experimental data from universal protein binding microarray (PBM) experiments (Berger et al., 2006) and their accompanying statistical analyses from prokaryotic and eukaryotic organisms, malarial parasites, yeast, worms, mouse, and human. It provides a centralized resource for accessing comprehensive data on the preferences of proteins for all possible sequence variants ("words") of length k ("k-mers"), as well as position weight matrix (PWM) and graphical sequence logo representations of the k-mer data. The database's web tools include a text-based search, a function for assessing motif similarity between user-entered data and database PWMs, and a function for locating putative binding sites along user-entered nucleotide sequences.

Proper citation: UniPROBE (RRID:SCR_005803) Copy   

•   Source: SciCrunch Registry

http://mutationassessor.org/

A web server that predicts the functional impact of amino-acid substitutions in proteins, such as mutations discovered in cancer or nonsynonymous polymorphisms. The functional impact is assessed based on evolutionary conservation of the affected amino acid in protein homologs. The method has been validated on a large set (51k) of disease associated (OMIM) and polymorphic variants., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: MutationAssessor (RRID:SCR_005762) Copy   

•   Source: SciCrunch Registry

http://indel.bioinfo.sdu.edu.cn/gridsphere/gridsphere

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. Indel Flanking Region Database is an online resource for indels and the flanking regions of proteins in SCOP superfamilies, including amino acid sequences, lengths, locations, secondary structure constitutions, hydrophilicity / hydrophobicity, domain information, 3D structures and so on. It aims at providing a comprehensive dataset for analyzing the qualities of amino acid insertion/deletions(indels), substitutions and the relationship between them. The indels were obtained through the pairwise alignment of homologous structures in SCOP superfamilies. The IndelFR database contains 2,925,017 indels with flanking regions extracted from 373,402 structural alignment pairs of 12,573 non-redundant domains from 1053 superfamilies. IndelFR has already been used for molecular evolution studies and may help to promote future functional studies of indels and their flanking regions.

Proper citation: IndelFR - Indel Flanking Region Database (RRID:SCR_006050) Copy   

•   Source: SciCrunch Registry

http://gst.ornl.gov/

We are the Computational Biology and Bioinformatics Group of the Biosciences Division of Oak Ridge National Laboratory. We conduct genetics research and system development in genomic sequencing, computational genome analysis, and computational protein structure analysis. We provide bioinformatics and analytic services and resources to collaborators, predict prospective gene and protein models for analysis, provide user services for the general community, including computer-annotated genomes in Genome Channel. Our collaborators include the Joint Genome Institute, ORNL''s Computer Science and Mathematics Division, the Tennessee Mouse Genome Consortium, the Joint Institute for Biological Sciences, and ORNL''s Genome Science and Technology Graduate Program.

Proper citation: Computational Biology at ORNL (RRID:SCR_005710) Copy   

•   Source: SciCrunch Registry

http://www.compbio.dundee.ac.uk/gotcha/gotcha.php

GOtcha provides a prediction of a set of GO terms that can be associated with a given query sequence. Each term is scored independently and the scores calibrated against reference searches to give an accurate percentage likelihood of correctness. These results can be displayed graphically. Why is GOtcha different to what is already out there and why should you be using it? * GOtcha uses a method where it combines information from many search hits, up to and including E-values that are normally discarded. This gives much better sensitivity than other methods. * GOtcha provides a score for each individual term, not just the leaf term or branch. This allows the discrimination between confident assignments that one would find at a more general level and the more specific terms that one would have lower confidence in. * The scores GOtcha provides are calibrated to give a real estimate of correctness. This is expressed as a percentage, giving a result that non-experts are comfortable in interpreting. * GOtcha provides graphical output that gives an overview of the confidence in, or potential alternatives for, particular GO term assignments. The tool is currently web-based; contact David Martin for details of the standalone version. Platform: Online tool

Proper citation: GOtcha (RRID:SCR_005790) Copy   

•   Source: SciCrunch Registry

http://xldb.fc.ul.pt/biotools/rebil/goa/

A tool for assisting the GO annotation of UniProt entries by linking the GO terms present in the uncurated annotations with evidence text automatically extracted from the documents linked to UniProt entries. Platform: Online tool

Proper citation: GoAnnotator (RRID:SCR_005792) Copy   

•   Source: SciCrunch Registry

http://possum.cbrc.jp/PoSSuM/

Relational database of all the discovered similar pairs in a huge number of protein-ligand binding sites with annotations of various types (e.g., CATH, SCOP, EC number, Gene ontology). They used a tremendously fast algorithm called SketchSort that enables the enumeration of similar pairs in a huge number of protein-ligand binding sites. They conducted all-pair similarity searches for 3.4 million known and potential binding sites using the proposed method and discovered over 24 million similar pairs of binding sites. PoSSuM enables rapid exploration of similar binding sites among structures with different global folds as well as similar ones. Moreover, PoSSuM is useful for predicting the binding ligand for unbound structures. Basically, the users can search similar binding pockets using two search modes: # Search K is useful for finding similar binding sites for a known ligand-binding site. Post a known ligand-binding site (a pair of PDB ID and HET code) in the PDB, and PoSSuM will search similar sites for the query site. # Search P is useful for predicting ligands that potentially bind to a structure of interest. Post a known protein structure (PDB ID) in the PDB, and PoSSuM will search similar known-ligand binding sites for the query structure.

Proper citation: PoSSuM (RRID:SCR_006109) Copy   

•   Source: SciCrunch Registry

http://www.agbase.msstate.edu/cgi-bin/tools/GOanna.cgi

GOanna is used to find annotations for proteins using a similarity search. The input can be a list of IDs or it can be a list of sequences in FASTA format. GOanna will retrieve the sequences if necessary and conduct the specified BLAST search against a user-specified database of GO annotated proteins. The resulting file contains GO annotations of the top BLAST hits. The sequence alignments are also provided so the user can use these to access the quality of the match. Platform: Online tool

Proper citation: GOanna (RRID:SCR_005684) Copy   

•   Source: SciCrunch Registry

http://mint.bio.uniroma2.it/virusmint/

A virus protein interactions database that collects and annotates all the interactions between human and viral proteins and integrates this information in the human protein interaction network. It uses the PSI-MI standard and is fully integrated with the MINT database. You can search for any viral or human protein by entering either common names or database identifiers or display a complete viral interactome.

Proper citation: VirusMINT (RRID:SCR_005987) Copy   

•   Source: SciCrunch Registry

http://xldb.fc.ul.pt/biotools/rebil/ssm/

FuSSiMeG is being discontinued, may not be working properly. Please use our new tool ProteinOn. Functional Semantic Similarity Measure between Gene Products (FuSSiMeG) provides a functional similarity measure between two proteins using the semantic similarity between the GO terms annotated with the proteins. Platform: Online tool

Proper citation: FuSSiMeG: Functional Semantic Similarity Measure between Gene-Products (RRID:SCR_005738) Copy   

•   Source: SciCrunch Registry