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http://integrativebiology.org/

Database for molecular interaction information integrated with various other bio-entity information, including pathways, diseases, gene ontology (GO) terms, species and molecular types. The information is obtained from several manually curated databases and automatic extraction from literature. There are protein-protein interaction, gene/protein regulation and protein-small molecule interaction information stored in the database. The interaction information is linked with relevant GO terms, pathway, disease and species names. Interactions are also linked to the PubMed IDs of the corresponding abstracts the interactions were obtained from. Manually curated molecular interaction information was obtained from BioGRID, IntAct, NCBI Gene, and STITCH database. Pathway related information was obtained from KEGG database, Pathway Interaction database and Reactome. Disease information was obtained from PharmGKB and KEGG database. Gene ontology terms and related information was obtained from Gene Ontology database and GOA database.

Proper citation: Integrated Molecular Interaction Database (RRID:SCR_003546) Copy   

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http://cbdm.mdc-berlin.de/~amer/cgi-bin/nyce

Data analysis service that predicts subcellular location (either Nuclear, Nucleo-cytoplasmic, Cytoplasmic or Extracellular) of eukaryotic proteins using the predicted exposure value of their amino acids.

Proper citation: NYCE (RRID:SCR_003144) Copy   

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https://netbio.bgu.ac.il/labwebsite/software/responsenet/

WebServer that identifies high-probability signaling and regulatory paths that connect input data sets. The input includes two weighted lists of condition-related proteins and genes, such as a set of disease-associated proteins and a set of differentially expressed disease genes, and a molecular interaction network (i.e., interactome). The output is a sparse, high-probability interactome sub-network connecting the two sets that is biased toward signaling pathways. This sub-network exposes additional proteins that are potentially involved in the studied condition and their likely modes of action. Computationally, it is formulated as a minimum-cost flow optimization problem that is solved using linear programming.

Proper citation: ResponseNet (RRID:SCR_003176) Copy   

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http://www.jci-bioinfo.cn/iLoc-Animal

Data analysis service for predicting subcellular localization of animal proteins with single or multiple sites.

Proper citation: iLoc-Animal (RRID:SCR_003173) Copy   

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http://signalink.org/

An integrated resource to analyze signaling pathway cross-talks, transcription factors, miRNAs and regulatory enzymes. The multi-layered database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The website allows the interactive exploration of how each signaling protein is regulated. Features * experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; * combines manual curation with large-scale datasets; * provides confidence scores for each interaction; * operates a customizable download page with multiple file formats (e.g., BioPAX, Cytoscape, SBML).

Proper citation: SignaLink (RRID:SCR_003569) Copy   

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http://abc.med.cornell.edu/pdzbase

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022.A manually curated protein-protein interaction database developed specifically for interactions involving PDZ domains. It currently contains 339 experimentally determined protein protein interactions.

Proper citation: PDZBase (RRID:SCR_003568) Copy   

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http://www.ebi.ac.uk/goldman-srv/pandit

PANDIT is a collection of multiple sequence alignments and phylogenetic trees covering many common protein domains. It contains: * the seed protein sequence alignments from the Pfam-A (curated families) database (version 17.0) * nucleotide sequence alignments derived from sequences available for the above and using the protein alignments as "templates"; * protein sequence alignments restricted to the family members for which nucleotide sequences are available * inferred phylogenetic trees for each alignment The data in PANDIT and the dataset's development have been frozen owing to a lack of funding support. The existing data, version 17.0 corresponding to Pfam 17.0, remain stable and, we hope, useful. The entire database is also available for download as a flatfile from this website.

Proper citation: PANDIT : Protein and Associated Nucleotide Domains with Inferred Trees (RRID:SCR_003321) Copy   

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http://www.ihop-net.org/UniPub/iHOP/

Information system that provides a network of concurring genes and proteins extends through the scientific literature touching on phenotypes, pathologies and gene function. It provides this network as a natural way of accessing millions of PubMed abstracts. By using genes and proteins as hyperlinks between sentences and abstracts, the information in PubMed can be converted into one navigable resource, bringing all advantages of the internet to scientific literature research. Moreover, this literature network can be superimposed on experimental interaction data (e.g., yeast-two hybrid data from Drosophila melanogaster and Caenorhabditis elegans) to make possible a simultaneous analysis of new and existing knowledge. The network contains half a million sentences and 30,000 different genes from humans, mice, D. melanogaster, C. elegans, zebrafish, Arabidopsis thaliana, yeast and Escherichia coli.

Proper citation: Information Hyperlinked Over Proteins (RRID:SCR_004829) Copy   

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

Database of three-dimensional structures of macromolecules that allows the user to retrieve structures for specific molecule types as well as structures for genes and proteins of interest. Three main databases comprise Structure-The Molecular Modeling Database; Conserved Domains and Protein Classification; and the BioSystems Database. Structure also links to the PubChem databases to connect biological activity data to the macromolecular structures. Users can locate structural templates for proteins and interactively view structures and sequence data to closely examine sequence-structure relationships. * Macromolecular structures: The three-dimensional structures of biomolecules provide a wealth of information on their biological function and evolutionary relationships. The Molecular Modeling Database (MMDB), as part of the Entrez system, facilitates access to structure data by connecting them with associated literature, protein and nucleic acid sequences, chemicals, biomolecular interactions, and more. It is possible, for example, to find 3D structures for homologs of a protein of interest by following the Related Structure link in an Entrez Protein sequence record. * Conserved domains and protein classification: Conserved domains are functional units within a protein that act as building blocks in molecular evolution and recombine in various arrangements to make proteins with different functions. The Conserved Domain Database (CDD) brings together several collections of multiple sequence alignments representing conserved domains, in addition to NCBI-curated domains that use 3D-structure information explicitly to define domain boundaries and provide insights into sequence/structure/function relationships. * Small molecules and their biological activity: The PubChem project provides information on the biological activities of small molecules and is a component of NIH''''s Molecular Libraries Roadmap Initiative. PubChem includes three databases: PCSubstance, PCBioAssay, and PCCompound. The PubChem data are linked to other data types (illustrated example) in the Entrez system, making it possible, for example, to retrieve information about a compound and then Link to its biological activity data, retrieve 3D protein structures bound to the compound and interactively view their active sites, and find biosystems that include the compound as a component. * Biological Systems: A biosystem, or biological system, is a group of molecules that interact directly or indirectly, where the grouping is relevant to the characterization of living matter. The NCBI BioSystems Database provides centralized access to biological pathways from several source databases and connects the biosystem records with associated literature, molecular, and chemical data throughout the Entrez system. BioSystem records list and categorize components (illustrated example), such as the genes, proteins, and small molecules involved in a biological system. The companion FLink icon FLink tool, in turn, allows you to input a list of proteins, genes, or small molecules and retrieve a ranked list of biosystems.

Proper citation: NCBI Structure (RRID:SCR_004218) Copy   

•   Source: SciCrunch Registry

http://metagenomics.anl.gov/

An automated analysis platform for metagenomes providing quantitative insights into microbial populations based on sequence data. The server primarily provides upload, quality control, automated annotation and analysis for prokaryotic metagenomic shotgun samples.

Proper citation: MG-RAST (RRID:SCR_004814) Copy   

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http://www.jbldesign.com/jmogil/enter.html

Database of genes regulated by pain derived from published manuscripts describing results of pain-relevant knockout studies. The database has two levels of exploration: across-gene and within-gene. The across-gene level, the PainGenesdbSelector, is encountered first. All genes in the database can be accessed and sorted by their gene name, protein name, common names and acronyms, or genomic position (by navigating a graphic representation of the mouse genome). The gene and protein names can be selected from an alphabetical list, or by typing a text string into a search box.

Proper citation: Pain Genes database (RRID:SCR_004771) Copy   

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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   

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. Web tool for an organized view of the transcriptome. Collection of the computationally identified transcripts from the same locus. Information on protein similarities, gene expression, cDNA clones, and genomic location. System for automatically partitioning GenBank sequences into a non redundant set of gene oriented clusters.

Proper citation: UniGene (RRID:SCR_004405) Copy   

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http://floresta.eead.csic.es/footprintdb/index.php

Database with 2797 unique DNA-binding proteins (mostly transcription factors, TFs), 4196 Position Weight Matrices (PWMs) and 13161 DNA Binding Sites extracted from the literature and other repositories. The binding interfaces of (most) proteins in the database are inferred from the collection of protein-DNA complexes described in 3D-footprint. The database predicts transcription factors which bind a specific DNA site or motif and DNA motifs or sites likely to be recognized by a specific DNA-binding protein.

Proper citation: footprintDB (RRID:SCR_005368) Copy   

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http://deepbase.sysu.edu.cn/chipbase/

A database for decoding transcription factor binding maps, expression profiles and transcriptional regulation of long non-coding RNAs (lncRNAs, lincRNAs), microRNAs, other ncRNAs (snoRNAs, tRNAs, snRNAs, etc.) and protein-coding genes from ChIP-Seq data. ChIPBase currently includes millions of transcription factor binding sites (TFBSs) among 6 species. ChIPBase provides several web-based tools and browsers to explore TF-lncRNA, TF-miRNA, TF-mRNA, TF-ncRNA and TF-miRNA-mRNA regulatory networks., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ChIPBase (RRID:SCR_005404) Copy   

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http://www.ncbi.nlm.nih.gov/CBBresearch/Wilbur/IRET/PIE/

A web service to extract Protein-protein interaction (PPI)-relevant articles from MEDLINE that provides protein interaction information (PPI) articles for biologists, baseline system performance for bio-text mining researchers and a compact PubMed-search environment for PubMed users. It accepts PubMed input formats including All Fields, Author, Journal, MeSH Terms, Publication Date, Title, and Title/Abstract with Boolean operations (AND, OR, and NOT). However, the output is the list of articles prioritized by PPI confidence rates. Some words (mostly gene/protein names) which contributed for PPI prediction are underlined and linked to Entrez or Entrez Gene. Even though our system focuses on a PubMed search environment, it also provides a CGI access for bio-text mining researchers. Using the CGI program, a list of PubMed IDs can be obtained as a query result, thus it can be utilized as a baseline system performance. PIE the search is based on a winning approach in the BioCreative III ACT competition (BC3)1. For input queries, MEDLINE articles are first retrieved through the PubMed service. PPI scores are calculated for the retrieved articles, and the articles are re-ranked based on scores. To effectively capture PPI patterns from biomedical literature, their approach utilizes both word and syntactic features for machine learning classifiers. Dependency parsing, gene mention tagging, and term-based features are utilized along with a Huber classifier.

Proper citation: PIE the search (RRID:SCR_005296) Copy   

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http://gila.bioengr.uic.edu/snp/toposnp

A topographic database for analyzing non-synonymous SNPs (nsSNPs) that can be mapped onto known 3D structures of proteins. These include disease- associated nsSNPs derived from the Online Mendelian Inheritance in Man (OMIM) database and other nsSNPs derived from dbSNP, a resource at the National Center for Biotechnology Information that catalogs SNPs. TopoSNP further classifies each nsSNP site into three categories based on their geometric location: those located in a surface pocket or an interior void of the protein, those on a convex region or a shallow depressed region, and those that are completely buried in the interior of the protein structure. These unique geometric descriptions provide more detailed mapping of nsSNPs to protein structures. It also includes relative entropy of SNPs calculated from multiple sequence alignment as obtained from the Pfam database (a database of protein families and conserved protein motifs) as well as manually adjusted multiple alignments obtained from ClustalW. These structural and conservational data can be useful for studying whether nsSNPs in coding regions are likely to lead to phenotypic changes. TopoSNP includes an interactive structural visualization web interface, as well as downloadable batch data.

Proper citation: TopoSNP (RRID:SCR_005572) Copy   

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http://www.mooneygroup.org/stop/input

STOP is a multi-ontology enrichment analysis tool. It is intended to be used to help from hypothesis about large sets of genes or proteins. The annoations used for enrichment analysis are obtained automatically applying text descriptions of genes and proteins to the NCBO annotator. Text for genes is found using NCBI entrez gene, and text for proteins is found using UniProt. The text is then run though NCBO annotator with all the available ontologies. For more information about the NCBO annotator please visit: http://bioportal.bioontology.org/ The goal of National Center for Biomedical Ontology (NCBO) is to support biomedical researchers in their knowledge-intensive work, by providing online tools and a Web portal enabling them to access, review, and integrate disparate ontological resources in all aspects of biomedical investigation and clinical practice. A major focus of our work involves the use of biomedical ontologies to aid in the management and analysis of data derived from complex experiments. This work is an expansion of the work of Rob Tirrell and others on RANSUM This probject would not be possible without the contributions of Emily Howe, Uday Evani, Corey Powell, Mathew Fleisch, Tobias Wittkop, Ari Berman, Nigam Shah and Sean Mooney An account is required.

Proper citation: STOP (RRID:SCR_005322) Copy   

•   Source: SciCrunch Registry

http://www.snp-nexus.org/

A web server for functional annotation of novel and publicly known genetic variants that was developed to assess the potential significance of known and novel SNPs on the major transcriptome, proteome, regulatory and structural variation models in order to identify the phenotypically important variants. A broader range of variations have been incorporated such as insertions / deletions, block substitutions, IUPAC codes submission and region-based analysis, expanding the query size limit, and most importantly including additional categories for the assessment of functional impact. SNPnexus provides a comprehensive set of annotations for genomic variation data by characterizing related functional consequences at the transcriptome/proteome levels of seven major annotation systems with in-depth analysis of potential deleterious effects, inferring physical and cytogenetic mapping, reporting information on HapMap genotype/allele data, finding overlaps with potential regulatory elements, structural variations and conserved elements, and retrieving links with previously reported genetic disease studies.

Proper citation: SNPnexus (RRID:SCR_005192) Copy   

•   Source: SciCrunch Registry

http://string.embl.de/

Database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations and are derived from four sources: Genomic Context, High-throughput experiments, (Conserved) Coexpression, and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5''214''234 proteins from 1133 organisms. (2013)

Proper citation: STRING (RRID:SCR_005223) Copy   

•   Source: SciCrunch Registry