Since the cloning of the first γ-aminobutyric acid (GABA) transporter (GAT1; SLC6A1) from rat brain in 1990, more than 50 published studies have provided structure-function information on investigator-designed rat and mouse GAT1 mutants. To date, more than 200 of 599 GAT1 residues have been subjected to mutagenesis experiments by substitution with different amino acids, and the resulting transporter functional properties have significantly advanced our understanding of the mechanism of Na+- and Cl⁻-coupled GABA transport by this important member of the neurotransmitter:sodium symporter family. Moreover, many studies have addressed the functional consequences of amino acid deletion or insertion at various positions along the primary sequence. The enormity of this growing body of structure-function information has prompted us to develop GABA Transporter Mutagenesis Database (GATMD), a web-accessible, relational database of manually annotated biochemical, functional and pharmacological data reported on GAT1-the most intensely studied GABA transporter isoform. As of the last update of GATMD, 52 GAT1 mutagenesis papers have yielded 3360 experimental records, which collectively contain a total of ∼100 000 annotated parameters. Database URL: http://physiology.sci.csupomona.edu/GATMD/

Since the publication of their longtime predecessor The Atlas of Protein Sequences and Structures in 1965 by Margaret Dayhoff, scientific databases have become a key factor in the organization of modern science. All the information and knowledge described in the novel scientific literature is translated into entries in many different scientific databases, making it possible to obtain very accurate information on a biological entity like genes or proteins without having to manually review the literature on it. However, even for the databases with the finest annotation procedures, errors or unclear parts sometimes appear in the publicly released version and influence the research of unaware scientists using them. The researcher that finds an error in a database is often left in a uncertain state, and often abandons the effort of reporting it because of a lack of a standard procedure to do so. In the present work, we propose that the simple adoption of a public error tracker application, as in many open software projects, could improve the quality of the annotations in many databases and encourage feedback from the scientific community on the data annotated publicly. In order to illustrate the situation, we describe a series of errors that we found and helped solve on the genes of a very well-known pathway in various biomedically relevant databases. We would like to show that, even if a majority of the most important scientific databases have procedures for reporting errors, these are usually not publicly visible, making the process of reporting errors time consuming and not useful. Also, the effort made by the user that reports the error often goes unacknowledged, putting him in a discouraging position.

We describe the creation of a specialized web-accessible database named the Pigment Cell Gene Resource, which contains information on the genetic pathways that regulate pigment cell development and function. This manually curated database is comprised of two sections, an annotated literature section and an interactive transcriptional network diagram. Initially, this database focuses on the transcription factor SOX10, which has essential roles in pigment cell development and function, but the database has been designed with the capacity to expand in the future, allowing inclusion of many more pigmentation genes. Database URL: http://research.nhgri.nih.gov/pigment_cell/

The Indian Genome Variation Consortium (IGVC) project, an initiative of the Council for Scientific and Industrial Research, has been the first large-scale comprehensive study of the Indian population. One of the major aims of the project is to study and catalog the variations in nearly thousand candidate genes related to diseases and drug response for predictive marker discovery, founder identification and also to address questions related to ethnic diversity, migrations, extent and relatedness with other world population. The Phase I of the project aimed at providing a set of reference populations that would represent the entire genetic spectrum of India in terms of language, ethnicity and geography and Phase II in providing variation data on candidate genes and genome wide neutral markers on these reference set of populations. We report here development of the IGVBrowser that provides allele and genotype frequency data generated in the IGVC project. The database harbors 4229 SNPs from more than 900 candidate genes in contrasting Indian populations. Analysis shows that most of the markers are from genic regions. Further, a large fraction of genes are implicated in cardiovascular, metabolic, cancer and immune system-related diseases. Thus, the IGVC data provide a basal level variation data in Indian population to study genetic diseases and pharmacology. Additionally, it also houses data on ∼50,000 (Affy 50 K array) genome wide neutral markers in these reference populations. In IGVBrowser one can analyze and compare genomic variations in Indian population with those reported in HapMap along with annotation information from various primary data sources. Database URL: http://igvbrowser.igib.res.in.

Prion diseases reflect conformational conversion of benign isoforms of prion protein (PrP(C)) to malignant PrP(Sc) isoforms. Networks perturbed by PrP(Sc) accumulation and their ties to pathological events are poorly understood. Time-course transcriptomic and phenotypic data in animal models are critical for understanding prion-perturbed networks in systems biology studies. Here, we present the Prion Disease Database (PDDB), the most comprehensive data resource on mouse prion diseases to date. The PDDB contains: (i) time-course mRNA measurements spanning the interval from prion inoculation through appearance of clinical signs in eight mouse strain-prion strain combinations and (ii) histoblots showing temporal PrP(Sc) accumulation patterns in brains from each mouse-prion combination. To facilitate prion research, the PDDB also provides a suite of analytical tools for reconstructing dynamic networks via integration of temporal mRNA and interaction data and for analyzing these networks to generate hypotheses.Database URL:http://prion.systemsbiology.net.

Peptidases are enzymes that hydrolyse peptide bonds in proteins and peptides. Peptidases are important in pathological conditions such as Alzheimer's disease, tumour and parasite invasion, and for processing viral polyproteins. The MEROPS database is an Internet resource containing information on peptidases, their substrates and inhibitors. The database now includes details of cleavage positions in substrates, both physiological and non-physiological, natural and synthetic. There are 39 118 cleavages in the collection; including 34 606 from a total of 10 513 different proteins and 2677 cleavages in synthetic substrates. The number of cleavages designated as 'physiological' is 13 307. The data are derived from 6095 publications. At least one substrate cleavage is known for 45% of the 2415 different peptidases recognized in the MEROPS database. The website now has three new displays: two showing peptidase specificity as a logo and a frequency matrix, the third showing a dynamically generated alignment between each protein substrate and its most closely related homologues. Many of the proteins described in the literature as peptidase substrates have been studied only in vitro. On the assumption that a physiologically relevant cleavage site would be conserved between species, the conservation of every site in terms of peptidase preference has been examined and a number have been identified that are not conserved. There are a number of cogent reasons why a site might not be conserved. Each poorly conserved site has been examined and a reason postulated. Some sites are identified that are very poorly conserved where cleavage is more likely to be fortuitous than of physiological relevance. This data-set is freely available via the Internet and is a useful training set for algorithms to predict substrates for peptidases and cleavage positions within those substrates. The data may also be useful for the design of inhibitors and for engineering novel specificities into peptidases.Database URL:http://merops.sanger.ac.uk.

UniProtKB/Swiss-Prot, a curated protein database, and dictyBase, the Model Organism Database for Dictyostelium discoideum, have established a collaboration to improve data sharing. One of the major steps in this effort was the 'Dicty annotation marathon', a week-long exercise with 30 annotators aimed at achieving a major increase in the number of D. discoideum proteins represented in UniProtKB/Swiss-Prot. The marathon led to the annotation of over 1000 D. discoideum proteins in UniProtKB/Swiss-Prot. Concomitantly, there were a large number of updates in dictyBase concerning gene symbols, protein names and gene models. This exercise demonstrates how UniProtKB/Swiss-Prot can work in very close cooperation with model organism databases and how the annotation of proteins can be accelerated through those collaborations.

This article reports on a detailed investigation of PubMed users' needs and behavior as a step toward improving biomedical information retrieval. PubMed is providing free service to researchers with access to more than 19 million citations for biomedical articles from MEDLINE and life science journals. It is accessed by millions of users each day. Efficient search tools are crucial for biomedical researchers to keep abreast of the biomedical literature relating to their own research. This study provides insight into PubMed users' needs and their behavior. This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries. Multiple aspects of users' interactions with PubMed are characterized in detail with evidence from these logs. Despite having many features in common with general Web searches, biomedical information searches have unique characteristics that are made evident in this study. PubMed users are more persistent in seeking information and they reformulate queries often. The three most frequent types of search are search by author name, search by gene/protein, and search by disease. Use of abbreviation in queries is very frequent. Factors such as result set size influence users' decisions. Analysis of characteristics such as these plays a critical role in identifying users' information needs and their search habits. In turn, such an analysis also provides useful insight for improving biomedical information retrieval.Database URL:http://www.ncbi.nlm.nih.gov/PubMed.

A number of databases on the plant metabolome describe the chemistry and biosynthesis of plant chemicals. However, no such database is specifically focused on foods and more precisely on polyphenols, one of the major classes of phytochemicals. As antioxidants, polyphenols influence human health and may play a role in the prevention of a number of chronic diseases such as cardiovascular diseases, some cancers or type 2 diabetes. To determine polyphenol intake in populations and study their association with health, it is essential to have detailed information on their content in foods. However this information is not easily collected due to the variety of their chemical structures and the variability of their content in a given food. Phenol-Explorer is the first comprehensive web-based database on polyphenol content in foods. It contains more than 37,000 original data points collected from 638 scientific articles published in peer-reviewed journals. The quality of these data has been evaluated before they were aggregated to produce final representative mean content values for 502 polyphenols in 452 foods. The web interface allows making various queries on the aggregated data to identify foods containing a given polyphenol or polyphenols present in a given food. For each mean content value, it is possible to trace all original content values and their literature sources. Phenol-Explorer is a major step forward in the development of databases on food constituents and the food metabolome. It should help researchers to better understand the role of phytochemicals in the technical and nutritional quality of food, and food manufacturers to develop tailor-made healthy foods. Database URL: http://www.phenol-explorer.eu.

Ciliopathies, pleiotropic diseases provoked by defects in the structure or function of cilia or flagella, reflect the multiple roles of cilia during development, in stem cells, in somatic organs and germ cells. High throughput studies have revealed several hundred proteins that are involved in the composition, function or biogenesis of cilia. The corresponding genes are potential candidates for orphan ciliopathies. To study ciliary genes, model organisms are used in which particular questions on motility, sensory or developmental functions can be approached by genetics. In the course of high throughput studies of cilia in Paramecium tetraurelia, we were confronted with the problem of comparing our results with those obtained in other model organisms. We therefore developed a novel knowledgebase, Cildb, that integrates ciliary data from heterogeneous sources. Cildb links orthology relationships among 18 species to high throughput ciliary studies, and to OMIM data on human hereditary diseases. The web interface of Cildb comprises three tools, BioMart for complex queries, BLAST for sequence homology searches and GBrowse for browsing the human genome in relation to OMIM information for human diseases. Cildb can be used for interspecies comparisons, building candidate ciliary proteomes in any species, or identifying candidate ciliopathy genes.Database URL:http://cildb.cgm.cnrs-gif.fr.

Video tutorials are an effective way for researchers to quickly learn how to use online tools offered by biological databases. At MaizeGDB, we have developed a number of video tutorials that demonstrate how to use various tools and explicitly outline the caveats researchers should know to interpret the information available to them. One such popular video currently available is 'Using the MaizeGDB Genome Browser', which describes how the maize genome was sequenced and assembled as well as how the sequence can be visualized and interacted with via the MaizeGDB Genome Browser. Database

Laccases and their homologues form the protein superfamily of multicopper oxidases (MCO). They catalyze the oxidation of many, particularly phenolic substances, and, besides playing an important role in many cellular activities, are of interest in biotechnological applications. The Laccase Engineering Database (LccED, http://www.lcced.uni-stuttgart.de) was designed to serve as a tool for a systematic sequence-based classification and analysis of the diverse multicopper oxidase protein family. More than 2200 proteins were classified into 11 superfamilies and 56 homologous families. For each family, the LccED provides multiple sequence alignments, phylogenetic trees and family-specific HMM profiles. The integration of structures for 14 different proteins allows a comprehensive comparison of sequences and structures to derive biochemical properties. Among the families, the distribution of the proteins regarding different kingdoms was investigated. The database was applied to perform a comprehensive analysis by MCO- and laccase-specific patterns. The LccED combines information of sequences and structures of MCOs. It serves as a classification tool to assign new proteins to a homologous family and can be applied to investigate sequence-structure-function relationship and to guide protein engineering. Database URL: http://www.lcced.uni-stuttgart.de.

In recent years, genomes from an increasing number of organisms have been sequenced, but their annotation remains a time-consuming process. The BioCyc databases offer a framework for the integrated analysis of metabolic networks. The Pathway tool software suite allows the automated construction of a database starting from an annotated genome, but it requires prior integration of all annotations into a specific summary file or into a GenBank file. To allow the easy creation and update of a BioCyc database starting from the multiple genome annotation resources available over time, we have developed an ad hoc data management system that we called Cyc Annotation Database System (CycADS). CycADS is centred on a specific database model and on a set of Java programs to import, filter and export relevant information. Data from GenBank and other annotation sources (including for example: KAAS, PRIAM, Blast2GO and PhylomeDB) are collected into a database to be subsequently filtered and extracted to generate a complete annotation file. This file is then used to build an enriched BioCyc database using the PathoLogic program of Pathway Tools. The CycADS pipeline for annotation management was used to build the AcypiCyc database for the pea aphid (Acyrthosiphon pisum) whose genome was recently sequenced. The AcypiCyc database webpage includes also, for comparative analyses, two other metabolic reconstruction BioCyc databases generated using CycADS: TricaCyc for Tribolium castaneum and DromeCyc for Drosophila melanogaster. Linked to its flexible design, CycADS offers a powerful software tool for the generation and regular updating of enriched BioCyc databases. The CycADS system is particularly suited for metabolic gene annotation and network reconstruction in newly sequenced genomes. Because of the uniform annotation used for metabolic network reconstruction, CycADS is particularly useful for comparative analysis of the metabolism of different organisms. Database URL: http://www.cycadsys.org.

The Rat Genome Database (RGD) is the premier repository of rat genomic and genetic data and currently houses over 40,000 rat gene records as well as human and mouse orthologs, 1771 rat and 1911 human quantitative trait loci (QTLs) and 2209 rat strains. Biological information curated for these data objects includes disease associations, phenotypes, pathways, molecular functions, biological processes and cellular components. A suite of tools has been developed to aid curators in acquiring and validating data objects, assigning nomenclature, attaching biological information to objects and making connections among data types. The software used to assign nomenclature, to create and edit objects and to make annotations to the data objects has been specifically designed to make the curation process as fast and efficient as possible. The user interfaces have been adapted to the work routines of the curators, creating a suite of tools that is intuitive and powerful. Database URL: http://rgd.mcw.edu.

The CEREALAB database aims to store genotypic and phenotypic data obtained by the CEREALAB project and to integrate them with already existing data sources in order to create a tool for plant breeders and geneticists. The database can help them in unravelling the genetics of economically important phenotypic traits; in identifying and choosing molecular markers associated to key traits; and in choosing the desired parentals for breeding programs. The database is divided into three sub-schemas corresponding to the species of interest: wheat, barley and rice; each sub-schema is then divided into two sub-ontologies, regarding genotypic and phenotypic data, respectively. Database URL: http://www.cerealab.unimore.it/jws/cerealab.jnlp.

The success of community projects such as Wikipedia has recently prompted a discussion about the applicability of such tools in the life sciences. Currently, there are several such 'science-wikis' that aim to collect specialist knowledge from the community into centralized resources. However, there is no consensus about how to achieve this goal. For example, it is not clear how to best integrate data from established, centralized databases with that provided by 'community annotation'. We created PDBWiki, a scientific wiki for the community annotation of protein structures. The wiki consists of one structured page for each entry in the the Protein Data Bank (PDB) and allows the user to attach categorized comments to the entries. Additionally, each page includes a user editable list of cross-references to external resources. As in a database, it is possible to produce tabular reports and 'structure galleries' based on user-defined queries or lists of entries. PDBWiki runs in parallel to the PDB, separating original database content from user annotations. PDBWiki demonstrates how collaboration features can be integrated with primary data from a biological database. It can be used as a system for better understanding how to capture community knowledge in the biological sciences. For users of the PDB, PDBWiki provides a bug-tracker, discussion forum and community annotation system. To date, user participation has been modest, but is increasing. The user editable cross-references section has proven popular, with the number of linked resources more than doubling from 17 originally to 39 today. Database URL: http://www.pdbwiki.org.

The proliferation of biological databases and the easy access enabled by the Internet is having a beneficial impact on biological sciences and transforming the way research is conducted. There are approximately 1100 molecular biology databases dispersed throughout the Internet. To assist in the functional, structural and evolutionary analysis of the abundant number of novel proteins continually identified from whole-genome sequencing, we introduce the PROFESS (PROtein Function, Evolution, Structure and Sequence) database. Our database is designed to be versatile and expandable and will not confine analysis to a pre-existing set of data relationships. A fundamental component of this approach is the development of an intuitive query system that incorporates a variety of similarity functions capable of generating data relationships not conceived during the creation of the database. The utility of PROFESS is demonstrated by the analysis of the structural drift of homologous proteins and the identification of potential pancreatic cancer therapeutic targets based on the observation of protein-protein interaction networks. Database URL: http://cse.unl.edu/~profess/

The Bacillus cereus group of bacteria includes species that are of significant medical and economic importance. We previously developed the SuperCAT database, which integrates data from all five multilocus sequence typing (MLST) schemes available to infer the genetic relatedness within this group. Since large numbers of isolates have been typed by other techniques, these should be incorporated in order to provide the most comprehensive and truly global view of the B. cereus group population. The SuperCAT system has been extended into a new database, HyperCAT, with two main additions. First, an extended supertree approach was applied to combine the phylogenetic information available from MLST, amplified fragment length polymorphism and multilocus enzyme electrophoresis. Secondly, a tree-independent clustering algorithm was designed to build superclusters of genetically closely related isolates sharing identical genotyping data. The superclusters were then mapped onto the supertree to generate an integrative genetic and phylogenetic snapshot of the B. cereus group population currently incorporating 2143 isolates. HyperCAT is freely accessible at the University of Oslo's typing website, which has also been upgraded with TNT software, allowing improved and ultra-fast supertree reconstructions. In addition, novel and advanced tools have been included for interactive viewing and navigation of trees, clusters and networks. Database URL: http://mlstoslo.uio.no/

For a number of years the BioMart data warehousing system has proven to be a valuable resource for scientists seeking a fast and versatile means of accessing the growing volume of genomic data provided by the Ensembl project. The launch of the Ensembl Genomes project in 2009 complemented the Ensembl project by utilizing the same visualization, interactive and programming tools to provide users with a means for accessing genome data from a further five domains: protists, bacteria, metazoa, plants and fungi. The Ensembl and Ensembl Genomes BioMarts provide a point of access to the high-quality gene annotation, variation data, functional and regulatory annotation and evolutionary relationships from genomes spanning the taxonomic space. This article aims to give a comprehensive overview of the Ensembl and Ensembl Genomes BioMarts as well as some useful examples and a description of current data content and future objectives. Database URLs: http://www.ensembl.org/biomart/martview/; http://metazoa.ensembl.org/biomart/martview/; http://plants.ensembl.org/biomart/martview/; http://protists.ensembl.org/biomart/martview/; http://fungi.ensembl.org/biomart/martview/; http://bacteria.ensembl.org/biomart/martview/.

PreDREM is a database of DNA regulatory motifs and motifs modules predicted from DNase I hypersensitive sites in 349 human cell and tissue samples. It contains 845-1325 predicted motifs in each sample, which result in a total of 2684 non-redundant motifs. In comparison with seven large collections of known motifs, more than 84% of the 2684 predicted motifs are similar to the known motifs, and 54-76% of the known motifs are similar to the predicted motifs. PreDREM also stores 43 663-20 13 288 motif modules in each sample, which provide the cofactor motifs of each predicted motif. Compared with motifs of known interacting transcription factor (TF) pairs in eight resources, on average, 84% of motif pairs corresponding to known interacting TF pairs are included in the predicted motif modules. Through its web interface, PreDREM allows users to browse motif information by tissues, datasets, individual non-redundant motifs, etc. Users can also search motifs, motif modules, instances of motifs and motif modules in given genomic regions, tissue or cell types a motif occurs, etc. PreDREM thus provides a useful resource for the understanding of cell- and tissue-specific gene regulation in the human genome. Database URL: http://server.cs.ucf.edu/predrem/.