The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the management, display, annotation and manipulation of phylogenetic and other trees. It is freely available and open to everyone. iTOL version 6 introduces a modernized and completely rewritten user interface, together with numerous new features. A new dataset type has been introduced (colored/labeled ranges), greatly upgrading the functionality of the previous simple colored range annotation function. Additional annotation options have been implemented for several existing dataset types. Dataset template files now support simple assignment of annotations to multiple tree nodes through substring matching, including full regular expression support. Node metadata handling has been greatly extended with novel display and exporting options, and it can now be edited interactively or bulk updated through annotation files. Tree labels can be displayed using multiple simultaneous font styles, with precise positioning, sizing and styling of each individual label part. Various bulk label editing functions have been implemented, simplifying large scale changes of all tree node labels. iTOL's automatic taxonomy assignment functions now support trees based on the Genome Taxonomy Database (GTDB), in addition to the NCBI taxonomy. The functionality of the optional user account pages has been expanded, simplifying the management, navigation and sharing of projects and trees. iTOL currently handles more than one and a half million trees from >130 000 individual user accounts.

Meta'omic data on microbial diversity and function accrue exponentially in public repositories, but derived information is often siloed according to data type, study or sampled microbial environment. Here we present SPIRE, a Searchable Planetary-scale mIcrobiome REsource that integrates various consistently processed metagenome-derived microbial data modalities across habitats, geography and phylogeny. SPIRE encompasses 99 146 metagenomic samples from 739 studies covering a wide array of microbial environments and augmented with manually-curated contextual data. Across a total metagenomic assembly of 16 Tbp, SPIRE comprises 35 billion predicted protein sequences and 1.16 million newly constructed metagenome-assembled genomes (MAGs) of medium or high quality. Beyond mapping to the high-quality genome reference provided by proGenomes3 (http://progenomes.embl.de), these novel MAGs form 92 134 novel species-level clusters, the majority of which are unclassified at species level using current tools. SPIRE enables taxonomic profiling of these species clusters via an updated, custom mOTUs database (https://motu-tool.org/) and includes several layers of functional annotation, as well as crosslinks to several (micro-)biological databases. The resource is accessible, searchable and browsable via http://spire.embl.de.

The interpretation of genomic, transcriptomic and other microbial 'omics data is highly dependent on the availability of well-annotated genomes. As the number of publicly available microbial genomes continues to increase exponentially, the need for quality control and consistent annotation is becoming critical. We present proGenomes3, a database of 907 388 high-quality genomes containing 4 billion genes that passed stringent criteria and have been consistently annotated using multiple functional and taxonomic databases including mobile genetic elements and biosynthetic gene clusters. proGenomes3 encompasses 41 171 species-level clusters, defined based on universal single copy marker genes, for which pan-genomes and contextual habitat annotations are provided. The database is available at http://progenomes.embl.de/.

The eggNOG (evolutionary gene genealogy Non-supervised Orthologous Groups) database is a bioinformatics resource providing orthology data and comprehensive functional information for organisms from all domains of life. Here, we present a major update of the database and website (version 6.0), which increases the number of covered organisms to 12 535 reference species, expands functional annotations, and implements new functionality. In total, eggNOG 6.0 provides a hierarchy of over 17M orthologous groups (OGs) computed at 1601 taxonomic levels, spanning 10 756 bacterial, 457 archaeal and 1322 eukaryotic organisms. OGs have been thoroughly annotated using recent knowledge from functional databases, including KEGG, Gene Ontology, UniProtKB, BiGG, CAZy, CARD, PFAM and SMART. eggNOG also offers phylogenetic trees for all OGs, maximising utility and versatility for end users while allowing researchers to investigate the evolutionary history of speciation and duplication events as well as the phylogenetic distribution of functional terms within each OG. Furthermore, the eggNOG 6.0 website contains new functionality to mine orthology and functional data with ease, including the possibility of generating phylogenetic profiles for multiple OGs across species or identifying single-copy OGs at custom taxonomic levels. eggNOG 6.0 is available at http://eggnog6.embl.de.

The InterPro database (https://www.ebi.ac.uk/interpro/) provides an integrative classification of protein sequences into families, and identifies functionally important domains and conserved sites. Here, we report recent developments with InterPro (version 90.0) and its associated software, including updates to data content and to the website. These developments extend and enrich the information provided by InterPro, and provide a more user friendly access to the data. Additionally, we have worked on adding Pfam website features to the InterPro website, as the Pfam website will be retired in late 2022. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB. Moreover, we report the development of a card game as a method of engaging the non-scientific community. Finally, we discuss the benefits and challenges brought by the use of artificial intelligence for protein structure prediction.

Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modalities and levels of mechanistic detail. The STRING database (https://string-db.org/) systematically collects and integrates protein-protein interactions-both physical interactions as well as functional associations. The data originate from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All of these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information. The data can be accessed via the website, but also programmatically and via bulk downloads. The most recent developments in STRING (version 12.0) are: (i) it is now possible to create, browse and analyze a full interaction network for any novel genome of interest, by submitting its complement of encoded proteins, (ii) the co-expression channel now uses variational auto-encoders to predict interactions, and it covers two new sources, single-cell RNA-seq and experimental proteomics data and (iii) the confidence in each experimentally derived interaction is now estimated based on the detection method used, and communicated to the user in the web-interface. Furthermore, STRING continues to enhance its facilities for functional enrichment analysis, which are now fully available also for user-submitted genomes.

Prokaryotic Mobile Genetic Elements (MGEs) such as transposons, integrons, phages and plasmids, play important roles in prokaryotic evolution and in the dispersal of cargo functions like antibiotic resistance. However, each of these MGE types is usually annotated and analysed individually, hampering a global understanding of phylogenetic and environmental patterns of MGE dispersal. We thus developed a computational framework that captures diverse MGE types, their cargos and MGE-mediated horizontal transfer events, using recombinases as ubiquitous MGE marker genes and pangenome information for MGE boundary estimation. Applied to ∼84k genomes with habitat annotation, we mapped 2.8 million MGE-specific recombinases to six operational MGE types, which together contain on average 13% of all the genes in a genome. Transposable elements (TEs) dominated across all taxa (∼1.7 million occurrences), outnumbering phages and phage-like elements (<0.4 million). We recorded numerous MGE-mediated horizontal transfer events across diverse phyla and habitats involving all MGE types, disentangled and quantified the extent of hitchhiking of TEs (17%) and integrons (63%) with other MGE categories, and established TEs as dominant carriers of antibiotic resistance genes. We integrated all these findings into a resource (proMGE.embl.de), which should facilitate future studies on the large mobile part of genomes and its horizontal dispersal.

The InterPro database (https://www.ebi.ac.uk/interpro/) provides an integrative classification of protein sequences into families, and identifies functionally important domains and conserved sites. InterProScan is the underlying software that allows protein and nucleic acid sequences to be searched against InterPro's signatures. Signatures are predictive models which describe protein families, domains or sites, and are provided by multiple databases. InterPro combines signatures representing equivalent families, domains or sites, and provides additional information such as descriptions, literature references and Gene Ontology (GO) terms, to produce a comprehensive resource for protein classification. Founded in 1999, InterPro has become one of the most widely used resources for protein family annotation. Here, we report the status of InterPro (version 81.0) in its 20th year of operation, and its associated software, including updates to database content, the release of a new website and REST API, and performance improvements in InterProScan.

OGEE is an Online GEne Essentiality database. Gene essentiality is not a static and binary property, rather a context-dependent and evolvable property in all forms of life. In OGEE we collect not only experimentally tested essential and non-essential genes, but also associated gene properties that contributes to gene essentiality. We tagged conditionally essential genes that show variable essentiality statuses across datasets to highlight complex interplays between gene functions and environmental/experimental perturbations. OGEE v3 contains gene essentiality datasets for 91 species; almost doubled from 48 species in previous version. To accommodate recent advances on human cancer essential genes (as known as tumor dependency genes) that could serve as targets for cancer treatment and/or drug development, we expanded the collection of human essential genes from 16 cell lines in previous to 581. These human cancer cell lines were tested with high-throughput experiments such as CRISPR-Cas9 and RNAi; in total, 150 of which were tested by both techniques. We also included factors known to contribute to gene essentiality for these cell lines, such as genomic mutation, methylation and gene expression, along with extensive graphical visualizations for ease of understanding of these factors. OGEE v3 can be accessible freely at https://v3.ogee.info.

The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the display, manipulation and annotation of phylogenetic and other trees. It is freely available and open to everyone. iTOL version 5 introduces a completely new tree display engine, together with numerous new features. For example, a new dataset type has been added (MEME motifs), while annotation options have been expanded for several existing ones. Node metadata display options have been extended and now also support non-numerical categorical values, as well as multiple values per node. Direct manual annotation is now available, providing a set of basic drawing and labeling tools, allowing users to draw shapes, labels and other features by hand directly onto the trees. Support for tree and dataset scales has been extended, providing fine control over line and label styles. Unrooted tree displays can now use the equal-daylight algorithm, proving a much greater display clarity. The user account system has been streamlined and expanded with new navigation options and currently handles >1 million trees from >70 000 individual users.

SMART (Simple Modular Architecture Research Tool) is a web resource (https://smart.embl.de) for the identification and annotation of protein domains and the analysis of protein domain architectures. SMART version 9 contains manually curated models for more than 1300 protein domains, with a topical set of 68 new models added since our last update article (1). All the new models are for diverse recombinase families and subfamilies and as a set they provide a comprehensive overview of mobile element recombinases namely transposase, integrase, relaxase, resolvase, cas1 casposase and Xer like cellular recombinase. Further updates include the synchronization of the underlying protein databases with UniProt (2), Ensembl (3) and STRING (4), greatly increasing the total number of annotated domains and other protein features available in architecture analysis mode. Furthermore, SMART's vector-based protein display engine has been extended and updated to use the latest web technologies and the domain architecture analysis components have been optimized to handle the increased number of protein features available.

Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein-protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.

Microbiology depends on the availability of annotated microbial genomes for many applications. Comparative genomics approaches have been a major advance, but consistent and accurate annotations of genomes can be hard to obtain. In addition, newer concepts such as the pan-genome concept are still being implemented to help answer biological questions. Hence, we present proGenomes2, which provides 87 920 high-quality genomes in a user-friendly and interactive manner. Genome sequences and annotations can be retrieved individually or by taxonomic clade. Every genome in the database has been assigned to a species cluster and most genomes could be accurately assigned to one or multiple habitats. In addition, general functional annotations and specific annotations of antibiotic resistance genes and single nucleotide variants are provided. In short, proGenomes2 provides threefold more genomes, enhanced habitat annotations, updated taxonomic and functional annotation and improved linkage to the NCBI BioSample database. The database is available at http://progenomes.embl.de/.

eggNOG is a public database of orthology relationships, gene evolutionary histories and functional annotations. Here, we present version 5.0, featuring a major update of the underlying genome sets, which have been expanded to 4445 representative bacteria and 168 archaea derived from 25 038 genomes, as well as 477 eukaryotic organisms and 2502 viral proteomes that were selected for diversity and filtered by genome quality. In total, 4.4M orthologous groups (OGs) distributed across 379 taxonomic levels were computed together with their associated sequence alignments, phylogenies, HMM models and functional descriptors. Precomputed evolutionary analysis provides fine-grained resolution of duplication/speciation events within each OG. Our benchmarks show that, despite doubling the amount of genomes, the quality of orthology assignments and functional annotations (80% coverage) has persisted without significant changes across this update. Finally, we improved eggNOG online services for fast functional annotation and orthology prediction of custom genomics or metagenomics datasets. All precomputed data are publicly available for downloading or via API queries at http://eggnog.embl.de.

The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the display, manipulation and annotation of phylogenetic and other trees. It is freely available and open to everyone. The current version introduces four new dataset types, together with numerous new features. Annotation options have been expanded and new control options added for many display elements. An interactive spreadsheet-like editor has been implemented, providing dataset creation and editing directly in the web interface. Font support has been rewritten with full support for UTF-8 character encoding throughout the user interface. Google Web Fonts are now fully supported in the tree text labels. iTOL v4 is the first tool which supports direct visualization of Qiime 2 trees and associated annotations. The user account system has been streamlined and expanded with new navigation options, and currently handles >700 000 trees from more than 40 000 individual users. Full batch access has been implemented allowing programmatic upload and export of trees and annotations.

The InterPro database (http://www.ebi.ac.uk/interpro/) classifies protein sequences into families and predicts the presence of functionally important domains and sites. Here, we report recent developments with InterPro (version 70.0) and its associated software, including an 18% growth in the size of the database in terms on new InterPro entries, updates to content, the inclusion of an additional entry type, refined modelling of discontinuous domains, and the development of a new programmatic interface and website. These developments extend and enrich the information provided by InterPro, and provide greater flexibility in terms of data access. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB, and discuss how our evaluation of residue coverage may help guide future curation activities.

Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

iPath3.0 (http://pathways.embl.de) is a web-application for the visualization and analysis of cellular pathways. It is freely available and open to everyone. Currently it is based on four KEGG global maps, which summarize up to 158 traditional KEGG pathway maps, 192 KEGG modules and other metabolic elements into one connected and manually curated metabolic network. Users can fully customize these networks and interactively explore them through its redesigned, fast and lightweight interface, which highlights general metabolic trends in multi-omics data. It also offers navigation at various levels of details to help users further investigate those trends and ultimately uncover novel biological insights. Support for multiple experimental conditions and time-series datasets, tools for generation of customization data, programmatic access, and a free user accounts system were introduced in this version to further streamline its workflow.

Phages invade microbes, accomplish host lysis and are of vital importance in shaping the community structure of environmental microbiota. More importantly, most phages have very specific hosts; they are thus ideal tools to manipulate environmental microbiota at species-resolution. The main purpose of MVP (Microbe Versus Phage) is to provide a comprehensive catalog of phage-microbe interactions and assist users to select phage(s) that can target (and potentially to manipulate) specific microbes of interest. We first collected 50 782 viral sequences from various sources and clustered them into 33 097 unique viral clusters based on sequence similarity. We then identified 26 572 interactions between 18 608 viral clusters and 9245 prokaryotes (i.e. bacteria and archaea); we established these interactions based on 30 321 evidence entries that we collected from published datasets, public databases and re-analysis of genomic and metagenomic sequences. Based on these interactions, we calculated the host range for each of the phage clusters and accordingly grouped them into subgroups such as 'species-', 'genus-' and 'family-' specific phage clusters. MVP is equipped with a modern, responsive and intuitive interface, and is freely available at: http://mvp.medgenius.info.

SMART (Simple Modular Architecture Research Tool) is a web resource (http://smart.embl.de) for the identification and annotation of protein domains and the analysis of protein domain architectures. SMART version 8 contains manually curated models for more than 1300 protein domains, with approximately 100 new models added since our last update article (1). The underlying protein databases were synchronized with UniProt (2), Ensembl (3) and STRING (4), doubling the total number of annotated domains and other protein features to more than 200 million. In its 20th year, the SMART analysis results pages have been streamlined again and its information sources have been updated. SMART's vector based display engine has been extended to all protein schematics in SMART and rewritten to use the latest web technologies. The internal full text search engine has been redesigned and updated, resulting in greatly increased search speed.

OGEE is an Online GEne Essentiality database. To enhance our understanding of the essentiality of genes, in OGEE we collected experimentally tested essential and non-essential genes, as well as associated gene properties known to contribute to gene essentiality. We focus on large-scale experiments, and complement our data with text-mining results. We organized tested genes into data sets according to their sources, and tagged those with variable essentiality statuses across data sets as conditionally essential genes, intending to highlight the complex interplay between gene functions and environments/experimental perturbations. Developments since the last public release include increased numbers of species and gene essentiality data sets, inclusion of non-coding essential sequences and genes with intermediate essentiality statuses. In addition, we included 16 essentiality data sets from cancer cell lines, corresponding to 9 human cancers; with OGEE, users can easily explore the shared and differentially essential genes within and between cancer types. These genes, especially those derived from cell lines that are similar to tumor samples, could reveal the oncogenic drivers, paralogous gene expression pattern and chromosomal structure of the corresponding cancer types, and can be further screened to identify targets for cancer therapy and/or new drug development. OGEE is freely available at http://ogee.medgenius.info.

InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPro's predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences.

The availability of microbial genomes has opened many new avenues of research within microbiology. This has been driven primarily by comparative genomics approaches, which rely on accurate and consistent characterization of genomic sequences. It is nevertheless difficult to obtain consistent taxonomic and integrated functional annotations for defined prokaryotic clades. Thus, we developed proGenomes, a resource that provides user-friendly access to currently 25 038 high-quality genomes whose sequences and consistent annotations can be retrieved individually or by taxonomic clade. These genomes are assigned to 5306 consistent and accurate taxonomic species clusters based on previously established methodology. proGenomes also contains functional information for almost 80 million protein-coding genes, including a comprehensive set of general annotations and more focused annotations for carbohydrate-active enzymes and antibiotic resistance genes. Additionally, broad habitat information is provided for many genomes. All genomes and associated information can be downloaded by user-selected clade or multiple habitat-specific sets of representative genomes. We expect that the availability of high-quality genomes with comprehensive functional annotations will promote advances in clinical microbial genomics, functional evolution and other subfields of microbiology. proGenomes is available at http://progenomes.embl.de.

A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein-protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein-protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.

We developed a comprehensive resource for the genome-reduced bacterium Mycoplasma pneumoniae comprising 1748 consistently generated '-omics' data sets, and used it to quantify the power of antisense non-coding RNAs (ncRNAs), lysine acetylation, and protein phosphorylation in predicting protein abundance (11%, 24% and 8%, respectively). These factors taken together are four times more predictive of the proteome abundance than of mRNA abundance. In bacteria, post-translational modifications (PTMs) and ncRNA transcription were both found to increase with decreasing genomic GC-content and genome size. Thus, the evolutionary forces constraining genome size and GC-content modify the relative contributions of the different regulatory layers to proteome homeostasis, and impact more genomic and genetic features than previously appreciated. Indeed, these scaling principles will enable us to develop more informed approaches when engineering minimal synthetic genomes.

eggNOG is a public resource that provides Orthologous Groups (OGs) of proteins at different taxonomic levels, each with integrated and summarized functional annotations. Developments since the latest public release include changes to the algorithm for creating OGs across taxonomic levels, making nested groups hierarchically consistent. This allows for a better propagation of functional terms across nested OGs and led to the novel annotation of 95 890 previously uncharacterized OGs, increasing overall annotation coverage from 67% to 72%. The functional annotations of OGs have been expanded to also provide Gene Ontology terms, KEGG pathways and SMART/Pfam domains for each group. Moreover, eggNOG now provides pairwise orthology relationships within OGs based on analysis of phylogenetic trees. We have also incorporated a framework for quickly mapping novel sequences to OGs based on precomputed HMM profiles. Finally, eggNOG version 4.5 incorporates a novel data set spanning 2605 viral OGs, covering 5228 proteins from 352 viral proteomes. All data are accessible for bulk downloading, as a web-service, and through a completely redesigned web interface. The new access points provide faster searches and a number of new browsing and visualization capabilities, facilitating the needs of both experts and less experienced users. eggNOG v4.5 is available at http://eggnog.embl.de.

Unwanted side effects of drugs are a burden on patients and a severe impediment in the development of new drugs. At the same time, adverse drug reactions (ADRs) recorded during clinical trials are an important source of human phenotypic data. It is therefore essential to combine data on drugs, targets and side effects into a more complete picture of the therapeutic mechanism of actions of drugs and the ways in which they cause adverse reactions. To this end, we have created the SIDER ('Side Effect Resource', http://sideeffects.embl.de) database of drugs and ADRs. The current release, SIDER 4, contains data on 1430 drugs, 5880 ADRs and 140 064 drug-ADR pairs, which is an increase of 40% compared to the previous version. For more fine-grained analyses, we extracted the frequency with which side effects occur from the package inserts. This information is available for 39% of drug-ADR pairs, 19% of which can be compared to the frequency under placebo treatment. SIDER furthermore contains a data set of drug indications, extracted from the package inserts using Natural Language Processing. These drug indications are used to reduce the rate of false positives by identifying medical terms that do not correspond to ADRs.

Interactive Tree Of Life (http://itol.embl.de) is a web-based tool for the display, manipulation and annotation of phylogenetic trees. It is freely available and open to everyone. The current version was completely redesigned and rewritten, utilizing current web technologies for speedy and streamlined processing. Numerous new features were introduced and several new data types are now supported. Trees with up to 100,000 leaves can now be efficiently displayed. Full interactive control over precise positioning of various annotation features and an unlimited number of datasets allow the easy creation of complex tree visualizations. iTOL 3 is the first tool which supports direct visualization of the recently proposed phylogenetic placements format. Finally, iTOL's account system has been redesigned to simplify the management of trees in user-defined workspaces and projects, as it is heavily used and currently handles already more than 500,000 trees from more than 10,000 individual users.

Interactions between proteins and small molecules are an integral part of biological processes in living organisms. Information on these interactions is dispersed over many databases, texts and prediction methods, which makes it difficult to get a comprehensive overview of the available evidence. To address this, we have developed STITCH ('Search Tool for Interacting Chemicals') that integrates these disparate data sources for 430 000 chemicals into a single, easy-to-use resource. In addition to the increased scope of the database, we have implemented a new network view that gives the user the ability to view binding affinities of chemicals in the interaction network. This enables the user to get a quick overview of the potential effects of the chemical on its interaction partners. For each organism, STITCH provides a global network; however, not all proteins have the same pattern of spatial expression. Therefore, only a certain subset of interactions can occur simultaneously. In the new, fifth release of STITCH, we have implemented functionality to filter out the proteins and chemicals not associated with a given tissue. The STITCH database can be downloaded in full, accessed programmatically via an extensive API, or searched via a redesigned web interface at http://stitch.embl.de.

Systematic interrogation of mutation or protein modification data is important to identify sites with functional consequences and to deduce global consequences from large data sets. Mechismo (mechismo.russellab.org) enables simultaneous consideration of thousands of 3D structures and biomolecular interactions to predict rapidly mechanistic consequences for mutations and modifications. As useful functional information often only comes from homologous proteins, we benchmarked the accuracy of predictions as a function of protein/structure sequence similarity, which permits the use of relatively weak sequence similarities with an appropriate confidence measure. For protein-protein, protein-nucleic acid and a subset of protein-chemical interactions, we also developed and benchmarked a measure of whether modifications are likely to enhance or diminish the interactions, which can assist the detection of modifications with specific effects. Analysis of high-throughput sequencing data shows that the approach can identify interesting differences between cancers, and application to proteomics data finds potential mechanistic insights for how post-translational modifications can alter biomolecular interactions.

SMART (Simple Modular Architecture Research Tool) is a web resource (http://smart.embl.de/) providing simple identification and extensive annotation of protein domains and the exploration of protein domain architectures. In the current version, SMART contains manually curated models for more than 1200 protein domains, with ∼200 new models since our last update article. The underlying protein databases were synchronized with UniProt, Ensembl and STRING, bringing the total number of annotated domains and other protein features above 100 million. SMART's 'Genomic' mode, which annotates proteins from completely sequenced genomes was greatly expanded and now includes 2031 species, compared to 1133 in the previous release. SMART analysis results pages have been completely redesigned and include links to several new information sources. A new, vector-based display engine has been developed for protein schematics in SMART, which can also be exported as high-resolution bitmap images for easy inclusion into other documents. Taxonomic tree displays in SMART have been significantly improved, and can be easily navigated using the integrated search engine.

The post-translational regulation of proteins is mainly driven by two molecular events, their modification by several types of moieties and their interaction with other proteins. These two processes are interdependent and together are responsible for the function of the protein in a particular cell state. Several databases focus on the prediction and compilation of protein-protein interactions (PPIs) and no less on the collection and analysis of protein post-translational modifications (PTMs), however, there are no resources that concentrate on describing the regulatory role of PTMs in PPIs. We developed several methods based on residue co-evolution and proximity to predict the functional associations of pairs of PTMs that we apply to modifications in the same protein and between two interacting proteins. In order to make data available for understudied organisms, PTMcode v2 (http://ptmcode.embl.de) includes a new strategy to propagate PTMs from validated modified sites through orthologous proteins. The second release of PTMcode covers 19 eukaryotic species from which we collected more than 300 000 experimentally verified PTMs (>1 300 000 propagated) of 69 types extracting the post-translational regulation of >100 000 proteins and >100 000 interactions. In total, we report 8 million associations of PTMs regulating single proteins and over 9.4 million interplays tuning PPIs.

The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.

The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

The thermophilic fungus Chaetomium thermophilum holds great promise for structural biology. To increase the efficiency of its biochemical and structural characterization and to explore its thermophilic properties beyond those of individual proteins, we obtained transcriptomics and proteomics data, and integrated them with computational annotation methods and a multitude of biochemical experiments conducted by the structural biology community. We considerably improved the genome annotation of Chaetomium thermophilum and characterized the transcripts and expression of thousands of genes. We furthermore show that the composition and structure of the expressed proteome of Chaetomium thermophilum is similar to its mesophilic relatives. Data were deposited in a publicly available repository and provide a rich source to the structural biology community.

STITCH is a database of protein-chemical interactions that integrates many sources of experimental and manually curated evidence with text-mining information and interaction predictions. Available at http://stitch.embl.de, the resulting interaction network includes 390 000 chemicals and 3.6 million proteins from 1133 organisms. Compared with the previous version, the number of high-confidence protein-chemical interactions in human has increased by 45%, to 367 000. In this version, we added features for users to upload their own data to STITCH in the form of internal identifiers, chemical structures or quantitative data. For example, a user can now upload a spreadsheet with screening hits to easily check which interactions are already known. To increase the coverage of STITCH, we expanded the text mining to include full-text articles and added a prediction method based on chemical structures. We further changed our scheme for transferring interactions between species to rely on orthology rather than protein similarity. This improves the performance within protein families, where scores are now transferred only to orthologous proteins, but not to paralogous proteins. STITCH can be accessed with a web-interface, an API and downloadable files.

With the increasing availability of various 'omics data, high-quality orthology assignment is crucial for evolutionary and functional genomics studies. We here present the fourth version of the eggNOG database (available at http://eggnog.embl.de) that derives nonsupervised orthologous groups (NOGs) from complete genomes, and then applies a comprehensive characterization and analysis pipeline to the resulting gene families. Compared with the previous version, we have more than tripled the underlying species set to cover 3686 organisms, keeping track with genome project completions while prioritizing the inclusion of high-quality genomes to minimize error propagation from incomplete proteome sets. Major technological advances include (i) a robust and scalable procedure for the identification and inclusion of high-quality genomes, (ii) provision of orthologous groups for 107 different taxonomic levels compared with 41 in eggNOGv3, (iii) identification and annotation of particularly closely related orthologous groups, facilitating analysis of related gene families, (iv) improvements of the clustering and functional annotation approach, (v) adoption of a revised tree building procedure based on the multiple alignments generated during the process and (vi) implementation of quality control procedures throughout the entire pipeline. As in previous versions, eggNOGv4 provides multiple sequence alignments and maximum-likelihood trees, as well as broad functional annotation. Users can access the complete database of orthologous groups via a web interface, as well as through bulk download.

Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made-particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks.

Post-translational modifications (PTMs) are involved in the regulation and structural stabilization of eukaryotic proteins. The combination of individual PTM states is a key to modulate cellular functions as became evident in a few well-studied proteins. This combinatorial setting, dubbed the PTM code, has been proposed to be extended to whole proteomes in eukaryotes. Although we are still far from deciphering such a complex language, thousands of protein PTM sites are being mapped by high-throughput technologies, thus providing sufficient data for comparative analysis. PTMcode (http://ptmcode.embl.de) aims to compile known and predicted PTM associations to provide a framework that would enable hypothesis-driven experimental or computational analysis of various scales. In its first release, PTMcode provides PTM functional associations of 13 different PTM types within proteins in 8 eukaryotes. They are based on five evidence channels: a literature survey, residue co-evolution, structural proximity, PTMs at the same residue and location within PTM highly enriched protein regions (hotspots). PTMcode is presented as a protein-based searchable database with an interactive web interface providing the context of the co-regulation of nearly 75 000 residues in >10 000 proteins.

OGEE is an Online GEne Essentiality database. Its main purpose is to enhance our understanding of the essentiality of genes. This is achieved by collecting not only experimentally tested essential and non-essential genes, but also associated gene features such as expression profiles, duplication status, conservation across species, evolutionary origins and involvement in embryonic development. We focus on large-scale experiments and complement our data with text-mining results. Genes are organized into data sets according to their sources. Genes with variable essentiality status across data sets are tagged as conditionally essential, highlighting the complex interplay between gene functions and environments. Linked tools allow the user to compare gene essentiality among different gene groups, or compare features of essential genes to non-essential genes, and visualize the results. OGEE is freely available at http://ogeedb.embl.de.

Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.

InterPro (http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains and functional sites, and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in characterizing individual protein sequences. Herein we give an overview of new developments in the database and its associated software since 2009, including updates to database content, curation processes and Web and programmatic interfaces.

To facilitate the study of interactions between proteins and chemicals, we have created STITCH, an aggregated database of interactions connecting over 300 000 chemicals and 2.6 million proteins from 1133 organisms. Compared to the previous version, the number of chemicals with interactions and the number of high-confidence interactions both increase 4-fold. The database can be accessed interactively through a web interface, displaying interactions in an integrated network view. It is also available for computational studies through downloadable files and an API. As an extension in the current version, we offer the option to switch between two levels of detail, namely whether stereoisomers of a given compound are shown as a merged entity or as separate entities. Separate display of stereoisomers is necessary, for example, for carbohydrates and chiral drugs. Combining the isomers increases the coverage, as interaction databases and publications found through text mining will often refer to compounds without specifying the stereoisomer. The database is accessible at http://stitch.embl.de/.

SMART (Simple Modular Architecture Research Tool) is an online resource (http://smart.embl.de/) for the identification and annotation of protein domains and the analysis of protein domain architectures. SMART version 7 contains manually curated models for 1009 protein domains, 200 more than in the previous version. The current release introduces several novel features and a streamlined user interface resulting in a faster and more comfortable workflow. The underlying protein databases were greatly expanded, resulting in a 2-fold increase in number of annotated domains and features. The database of completely sequenced genomes now includes 1133 species, compared to 630 in the previous release. Domain architecture analysis results can now be exported and visualized through the iTOL phylogenetic tree viewer. 'metaSMART' was introduced as a novel subresource dedicated to the exploration and analysis of domain architectures in various metagenomics data sets. An advanced full text search engine was implemented, covering the complete annotations for SMART and Pfam domains, as well as the complete set of protein descriptions, allowing users to quickly find relevant information.

Orthologous relationships form the basis of most comparative genomic and metagenomic studies and are essential for proper phylogenetic and functional analyses. The third version of the eggNOG database (http://eggnog.embl.de) contains non-supervised orthologous groups constructed from 1133 organisms, doubling the number of genes with orthology assignment compared to eggNOG v2. The new release is the result of a number of improvements and expansions: (i) the underlying homology searches are now based on the SIMAP database; (ii) the orthologous groups have been extended to 41 levels of selected taxonomic ranges enabling much more fine-grained orthology assignments; and (iii) the newly designed web page is considerably faster with more functionality. In total, eggNOG v3 contains 721 801 orthologous groups, encompassing a total of 4 396 591 genes. Additionally, we updated 4873 and 4850 original COGs and KOGs, respectively, to include all 1133 organisms. At the universal level, covering all three domains of life, 101 208 orthologous groups are available, while the others are applicable at 40 more limited taxonomic ranges. Each group is amended by multiple sequence alignments and maximum-likelihood trees and broad functional descriptions are provided for 450 904 orthologous groups (62.5%).

Interactive Tree Of Life (http://itol.embl.de) is a web-based tool for the display, manipulation and annotation of phylogenetic trees. It is freely available and open to everyone. In addition to classical tree viewer functions, iTOL offers many novel ways of annotating trees with various additional data. Current version introduces numerous new features and greatly expands the number of supported data set types. Trees can be interactively manipulated and edited. A free personal account system is available, providing management and sharing of trees in user defined workspaces and projects. Export to various bitmap and vector graphics formats is supported. Batch access interface is available for programmatic access or inclusion of interactive trees into other web services.

An essential prerequisite for any systems-level understanding of cellular functions is to correctly uncover and annotate all functional interactions among proteins in the cell. Toward this goal, remarkable progress has been made in recent years, both in terms of experimental measurements and computational prediction techniques. However, public efforts to collect and present protein interaction information have struggled to keep up with the pace of interaction discovery, partly because protein-protein interaction information can be error-prone and require considerable effort to annotate. Here, we present an update on the online database resource Search Tool for the Retrieval of Interacting Genes (STRING); it provides uniquely comprehensive coverage and ease of access to both experimental as well as predicted interaction information. Interactions in STRING are provided with a confidence score, and accessory information such as protein domains and 3D structures is made available, all within a stable and consistent identifier space. New features in STRING include an interactive network viewer that can cluster networks on demand, updated on-screen previews of structural information including homology models, extensive data updates and strongly improved connectivity and integration with third-party resources. Version 9.0 of STRING covers more than 1100 completely sequenced organisms; the resource can be reached at http://string-db.org.

iPath2.0 is a web-based tool (http://pathways.embl.de) for the visualization and analysis of cellular pathways. Its primary map summarizes the metabolism in biological systems as annotated to date. Nodes in the map correspond to various chemical compounds and edges represent series of enzymatic reactions. In two other maps, iPath2.0 provides an overview of secondary metabolite biosynthesis and a hand-picked selection of important regulatory pathways and other functional modules, allowing a more general overview of protein functions in a genome or metagenome. iPath2.0's main interface is an interactive Flash-based viewer, which allows users to easily navigate and explore the complex pathway maps. In addition to the default pre-computed overview maps, iPath offers several data mapping tools. Users can upload various types of data and completely customize all nodes and edges of iPath2.0's maps. These customized maps give users an intuitive overview of their own data, guiding the analysis of various genomics and metagenomics projects.

Over the last years, the publicly available knowledge on interactions between small molecules and proteins has been steadily increasing. To create a network of interactions, STITCH aims to integrate the data dispersed over the literature and various databases of biological pathways, drug-target relationships and binding affinities. In STITCH 2, the number of relevant interactions is increased by incorporation of BindingDB, PharmGKB and the Comparative Toxicogenomics Database. The resulting network can be explored interactively or used as the basis for large-scale analyses. To facilitate links to other chemical databases, we adopt InChIKeys that allow identification of chemicals with a short, checksum-like string. STITCH 2.0 connects proteins from 630 organisms to over 74 000 different chemicals, including 2200 drugs. STITCH can be accessed at http://stitch.embl.de/.

The identification of orthologous relationships forms the basis for most comparative genomics studies. Here, we present the second version of the eggNOG database, which contains orthologous groups (OGs) constructed through identification of reciprocal best BLAST matches and triangular linkage clustering. We applied this procedure to 630 complete genomes (529 bacteria, 46 archaea and 55 eukaryotes), which is a 2-fold increase relative to the previous version. The pipeline yielded 224 847 OGs, including 9724 extended versions of the original COG and KOG. We computed OGs for different levels of the tree of life; in addition to the species groups included in our first release (i.e. fungi, metazoa, insects, vertebrates and mammals), we have now constructed OGs for archaea, fishes, rodents and primates. We automatically annotate the non-supervised orthologous groups (NOGs) with functional descriptions, protein domains, and functional categories as defined initially for the COG/KOG database. In-depth analysis is facilitated by precomputed high-quality multiple sequence alignments and maximum-likelihood trees for each of the available OGs. Altogether, eggNOG covers 2 242 035 proteins (built from 2 590 259 proteins) and provides a broad functional description for at least 1 966 709 (88%) of them. Users can access the complete set of orthologous groups via a web interface at: http://eggnog.embl.de.

Cis-acting short sequence motifs play important roles in alternative splicing. It is now possible to identify such sequence motifs as conserved sequence patterns in genome sequence alignments. Here, we report the systematic search for motifs in the neighboring introns of alternatively spliced exons by using comparative analysis of mammalian genome alignments. We identified 11 conserved sequence motifs that might be involved in the regulation of alternative splicing. These motifs are not only significantly overrepresented near alternatively spliced exons, but they also co-occur with each other, thus, forming a network of cis-elements, likely to be the basis for context-dependent regulation. Based on this finding, we applied the motif co-occurrence to predict alternatively skipped exons. We verified exon skipping in 29 cases out of 118 predictions (25%) by EST and mRNA sequences in the databases. For the predictions not verified by the database sequences, we confirmed exon skipping in 10 additional cases by using both RT-PCR experiments and the publicly available RNA-Seq data. These results indicate that even more alternative splicing events will be found with the progress of large-scale and high-throughput analyses for various tissue samples and developmental stages.

About 15% of human colorectal cancers and, at varying degrees, other tumor entities as well as nearly all tumors related to Lynch syndrome are hallmarked by microsatellite instability (MSI) as a result of a defective mismatch repair system. The functional impact of resulting mutations depends on their genomic localization. Alterations within coding mononucleotide repeat tracts (MNRs) can lead to protein truncation and formation of neopeptides, whereas alterations within untranslated MNRs can alter transcription level or transcript stability. These mutations may provide selective advantage or disadvantage to affected cells. They may further concern the biology of microsatellite unstable cells, e.g. by generating immunogenic peptides induced by frameshifts mutations. The Selective Targets database (http://www.seltarbase.org) is a curated database of a growing number of public MNR mutation data in microsatellite unstable human tumors. Regression calculations for various MSI-H tumor entities indicating statistically deviant mutation frequencies predict TGFBR2, BAX, ACVR2A and others that are shown or highly suspected to be involved in MSI tumorigenesis. Many useful tools for further analyzing genomic DNA, derived wild-type and mutated cDNAs and peptides are integrated. A comprehensive database of all human coding, untranslated, non-coding RNA- and intronic MNRs (MNR_ensembl) is also included. Herewith, SelTarbase presents as a plenty instrument for MSI-carcinogenesis-related research, diagnostics and therapy.

The InterPro database (http://www.ebi.ac.uk/interpro/) integrates together predictive models or 'signatures' representing protein domains, families and functional sites from multiple, diverse source databases: Gene3D, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. Integration is performed manually and approximately half of the total approximately 58 000 signatures available in the source databases belong to an InterPro entry. Recently, we have started to also display the remaining un-integrated signatures via our web interface. Other developments include the provision of non-signature data, such as structural data, in new XML files on our FTP site, as well as the inclusion of matchless UniProtKB proteins in the existing match XML files. The web interface has been extended and now links out to the ADAN predicted protein-protein interaction database and the SPICE and Dasty viewers. The latest public release (v18.0) covers 79.8% of UniProtKB (v14.1) and consists of 16 549 entries. InterPro data may be accessed either via the web address above, via web services, by downloading files by anonymous FTP or by using the InterProScan search software (http://www.ebi.ac.uk/Tools/InterProScan/).

Functional partnerships between proteins are at the core of complex cellular phenotypes, and the networks formed by interacting proteins provide researchers with crucial scaffolds for modeling, data reduction and annotation. STRING is a database and web resource dedicated to protein-protein interactions, including both physical and functional interactions. It weights and integrates information from numerous sources, including experimental repositories, computational prediction methods and public text collections, thus acting as a meta-database that maps all interaction evidence onto a common set of genomes and proteins. The most important new developments in STRING 8 over previous releases include a URL-based programming interface, which can be used to query STRING from other resources, improved interaction prediction via genomic neighborhood in prokaryotes, and the inclusion of protein structures. Version 8.0 of STRING covers about 2.5 million proteins from 630 organisms, providing the most comprehensive view on protein-protein interactions currently available. STRING can be reached at http://string-db.org/.

Simple modular architecture research tool (SMART) is an online tool (http://smart.embl.de/) for the identification and annotation of protein domains. It provides a user-friendly platform for the exploration and comparative study of domain architectures in both proteins and genes. The current release of SMART contains manually curated models for 784 protein domains. Recent developments were focused on further data integration and improving user friendliness. The underlying protein database based on completely sequenced genomes was greatly expanded and now includes 630 species, compared to 191 in the previous release. As an initial step towards integrating information on biological pathways into SMART, our domain annotations were extended with data on metabolic pathways and links to several pathways resources. The interaction network view was completely redesigned and is now available for more than 2 million proteins. In addition to the standard web access to the database, users can now query SMART using distributed annotation system (DAS) or through a simple object access protocol (SOAP) based web service.

The molecular basis of drug action is often not well understood. This is partly because the very abundant and diverse information generated in the past decades on drugs is hidden in millions of medical articles or textbooks. Therefore, we developed a one-stop data warehouse, SuperTarget that integrates drug-related information about medical indication areas, adverse drug effects, drug metabolization, pathways and Gene Ontology terms of the target proteins. An easy-to-use query interface enables the user to pose complex queries, for example to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target the same protein but are metabolized by different enzymes. Furthermore, we provide tools for 2D drug screening and sequence comparison of the targets. The database contains more than 2500 target proteins, which are annotated with about 7300 relations to 1500 drugs; the vast majority of entries have pointers to the respective literature source. A subset of these drugs has been annotated with additional binding information and indirect interactions and is available as a separate resource called Matador. SuperTarget and Matador are available at http://insilico.charite.de/supertarget and http://matador.embl.de.

In the major animal model species like mouse, fish or fly, detailed spatial information on gene expression over time can be acquired through whole mount in situ hybridization experiments. In these species, expression patterns of many genes have been studied and data has been integrated into dedicated model organism databases like ZFIN for zebrafish, MEPD for medaka, BDGP for Drosophila or GXD for mouse. However, a central repository that allows users to query and compare gene expression patterns across different species has not yet been established. Therefore, we have integrated expression patterns for zebrafish, Drosophila, medaka and mouse into a central public repository called 4DXpress (expression database in four dimensions). Users can query anatomy ontology-based expression annotations across species and quickly jump from one gene to the orthologues in other species. Genes are linked to public microarray data in ArrayExpress. We have mapped developmental stages between the species to be able to compare developmental time phases. We store the largest collection of gene expression patterns available to date in an individual resource, reflecting 16 505 annotated genes. 4DXpress will be an invaluable tool for developmental as well as for computational biologists interested in gene regulation and evolution. 4DXpress is available at http://ani.embl.de/4DXpress.

The identification of orthologous genes forms the basis for most comparative genomics studies. Existing approaches either lack functional annotation of the identified orthologous groups, hampering the interpretation of subsequent results, or are manually annotated and thus lag behind the rapid sequencing of new genomes. Here we present the eggNOG database ('evolutionary genealogy of genes: Non-supervised Orthologous Groups'), which contains orthologous groups constructed from Smith-Waterman alignments through identification of reciprocal best matches and triangular linkage clustering. Applying this procedure to 312 bacterial, 26 archaeal and 35 eukaryotic genomes yielded 43 582 course-grained orthologous groups of which 9724 are extended versions of those from the original COG/KOG database. We also constructed more fine-grained groups for selected subsets of organisms, such as the 19 914 mammalian orthologous groups. We automatically annotated our non-supervised orthologous groups with functional descriptions, which were derived by identifying common denominators for the genes based on their individual textual descriptions, annotated functional categories, and predicted protein domains. The orthologous groups in eggNOG contain 1 241 751 genes and provide at least a broad functional description for 77% of them. Users can query the resource for individual genes via a web interface or download the complete set of orthologous groups at http://eggnog.embl.de.

The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH ('search tool for interactions of chemicals') 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. Our 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. STITCH is available at http://stitch.embl.de/

Protein kinases control cellular responses by phosphorylating specific substrates. Recent proteome-wide mapping of protein phosphorylation sites by mass spectrometry has discovered thousands of in vivo sites. Systematically assigning all 518 human kinases to all these sites is a challenging problem. The NetworKIN database (http://networkin.info) integrates consensus substrate motifs with context modelling for improved prediction of cellular kinase-substrate relations. Based on the latest human phosphoproteome from the Phospho.ELM and PhosphoSite databases, the resource offers insight into phosphorylation-modulated interaction networks. Here, we describe how NetworKIN can be used for both global and targeted molecular studies. Via the web interface users can query the database of precomputed kinase-substrate relations or obtain predictions on novel phosphoproteins. The database currently contains a predicted phosphorylation network with 20 224 site-specific interactions involving 3978 phosphoproteins and 73 human kinases from 20 families.

KEGG Atlas is a new graphical interface to the KEGG suite of databases, especially to the systems information in the PATHWAY and BRITE databases. It currently consists of a single global map and an associated viewer for metabolism, covering about 120 KEGG metabolic pathway maps and about 10 BRITE hierarchies. The viewer allows the user to navigate and zoom the global map under the Ajax technology. The mapping of high-throughput experimental data onto the global map is the main use of KEGG Atlas. In the global metabolism map, the node (circle) is a chemical compound and the edge (line) is a set of reactions linked to a set of KEGG Orthology (KO) entries for enzyme genes. Once gene identifiers in different organisms are converted to the K number identifiers in the KO system, corresponding line segments can be highlighted in the global map, allowing the user to view genome sequence data as organism-specific pathways, gene expression data as up- or down-regulated pathways, etc. Once chemical compounds are converted to the C number identifiers in KEGG, metabolomics data can also be displayed in the global map. KEGG Atlas is available at http://www.genome.jp/kegg/atlas/.

Metabolic networks are a subject that has received much attention, but existing web resources do not include extensive phylogenetic information. Phylogenomic approaches (phylogenetics on a genomic scale) have been shown to be effective in the study of evolution and processes like horizontal gene transfer (HGT). To address the lack of phylogenomic information relating to eukaryotic metabolism, metaTIGER (www.bioinformatics.leeds.ac.uk/metatiger) has been created, using genomic information from 121 eukaryotes and 404 prokaryotes and sensitive sequence search techniques to predict the presence of metabolic enzymes. These enzyme sequences were used to create a comprehensive database of 2257 maximum-likelihood phylogenetic trees, some containing over 500 organisms. The trees can be viewed using iTOL, an advanced interactive tree viewer, enabling straightforward interpretation of large trees. Complex high-throughput tree analysis is also available through user-defined queries, allowing the rapid identification of trees of interest, e.g. containing putative HGT events. metaTIGER also provides novel and easy-to-use facilities for viewing and comparing the metabolic networks in different organisms via highlighted pathway images and tables. metaTIGER is demonstrated through evolutionary analysis of Plasmodium, including identification of genes horizontally transferred from chlamydia.

The past decade has seen the publication of a large number of cell-cycle microarray studies and many more are in the pipeline. However, data from these experiments are not easy to access, combine and evaluate. We have developed a centralized database with an easy-to-use interface, Cyclebase.org, for viewing and downloading these data. The user interface facilitates searches for genes of interest as well as downloads of genome-wide results. Individual genes are displayed with graphs of expression profiles throughout the cell cycle from all available experiments. These expression profiles are normalized to a common timescale to enable inspection of the combined experimental evidence. Furthermore, state-of-the-art computational analyses provide key information on both individual experiments and combined datasets such as whether or not a gene is periodically expressed and, if so, the time of peak expression. Cyclebase is available at http://www.cyclebase.org.

InterPro is an integrated resource for protein families, domains and functional sites, which integrates the following protein signature databases: PROSITE, PRINTS, ProDom, Pfam, SMART, TIGRFAMs, PIRSF, SUPERFAMILY, Gene3D and PANTHER. The latter two new member databases have been integrated since the last publication in this journal. There have been several new developments in InterPro, including an additional reading field, new database links, extensions to the web interface and additional match XML files. InterPro has always provided matches to UniProtKB proteins on the website and in the match XML file on the FTP site. Additional matches to proteins in UniParc (UniProt archive) are now available for download in the new match XML files only. The latest InterPro release (13.0) contains more than 13 000 entries, covering over 78% of all proteins in UniProtKB. The database is available for text- and sequence-based searches via a webserver (http://www.ebi.ac.uk/interpro), and for download by anonymous FTP (ftp://ftp.ebi.ac.uk/pub/databases/interpro). The InterProScan search tool is now also available via a web service at http://www.ebi.ac.uk/Tools/webservices/WSInterProScan.html.

G2D (genes to diseases) is a web resource for prioritizing genes as candidates for inherited diseases. It uses three algorithms based on different prioritization strategies. The input to the server is the genomic region where the user is looking for the disease-causing mutation, plus an additional piece of information depending on the algorithm used. This information can either be the disease phenotype (described as an online Mendelian inheritance in man (OMIM) identifier), one or several genes known or suspected to be associated with the disease (defined by their Entrez Gene identifiers), or a second genomic region that has been linked as well to the disease. In the latter case, the tool uses known or predicted interactions between genes in the two regions extracted from the STRING database. The output in every case is an ordered list of candidate genes in the region of interest. For the first two of the three methods, the candidate genes are first retrieved through sequence homology search, then scored accordingly to the corresponding method. This means that some of them will correspond to well-known characterized genes, and others will overlap with predicted genes, thus providing a wider analysis. G2D is publicly available at http://www.ogic.ca/projects/g2d_2/

Information on protein-protein interactions is still mostly limited to a small number of model organisms, and originates from a wide variety of experimental and computational techniques. The database and online resource STRING generalizes access to protein interaction data, by integrating known and predicted interactions from a variety of sources. The underlying infrastructure includes a consistent body of completely sequenced genomes and exhaustive orthology classifications, based on which interaction evidence is transferred between organisms. Although primarily developed for protein interaction analysis, the resource has also been successfully applied to comparative genomics, phylogenetics and network studies, which are all facilitated by programmatic access to the database backend and the availability of compact download files. As of release 7, STRING has almost doubled to 373 distinct organisms, and contains more than 1.5 million proteins for which associations have been pre-computed. Novel features include AJAX-based web-navigation, inclusion of additional resources such as BioGRID, and detailed protein domain annotation. STRING is available at http://string.embl.de/

The Simple Modular Architecture Research Tool (SMART) is an online resource (http://smart.embl.de/) used for protein domain identification and the analysis of protein domain architectures. Many new features were implemented to make SMART more accessible to scientists from different fields. The new 'Genomic' mode in SMART makes it easy to analyze domain architectures in completely sequenced genomes. Domain annotation has been updated with a detailed taxonomic breakdown and a prediction of the catalytic activity for 50 SMART domains is now available, based on the presence of essential amino acids. Furthermore, intrinsically disordered protein regions can be identified and displayed. The network context is now displayed in the results page for more than 350 000 proteins, enabling easy analyses of domain interactions.

PAL2NAL is a web server that constructs a multiple codon alignment from the corresponding aligned protein sequences. Such codon alignments can be used to evaluate the type and rate of nucleotide substitutions in coding DNA for a wide range of evolutionary analyses, such as the identification of levels of selective constraint acting on genes, or to perform DNA-based phylogenetic studies. The server takes a protein sequence alignment and the corresponding DNA sequences as input. In contrast to other existing applications, this server is able to construct codon alignments even if the input DNA sequence has mismatches with the input protein sequence, or contains untranslated regions and polyA tails. The server can also deal with frame shifts and inframe stop codons in the input models, and is thus suitable for the analysis of pseudogenes. Another distinct feature is that the user can specify a subregion of the input alignment in order to specifically analyze functional domains or exons of interest. The PAL2NAL server is available at http://www.bork.embl.de/pal2nal.

InterPro, an integrated documentation resource of protein families, domains and functional sites, was created to integrate the major protein signature databases. Currently, it includes PROSITE, Pfam, PRINTS, ProDom, SMART, TIGRFAMs, PIRSF and SUPERFAMILY. Signatures are manually integrated into InterPro entries that are curated to provide biological and functional information. Annotation is provided in an abstract, Gene Ontology mapping and links to specialized databases. New features of InterPro include extended protein match views, taxonomic range information and protein 3D structure data. One of the new match views is the InterPro Domain Architecture view, which shows the domain composition of protein matches. Two new entry types were introduced to better describe InterPro entries: these are active site and binding site. PIRSF and the structure-based SUPERFAMILY are the latest member databases to join InterPro, and CATH and PANTHER are soon to be integrated. InterPro release 8.0 contains 11 007 entries, representing 2573 domains, 8166 families, 201 repeats, 26 active sites, 21 binding sites and 20 post-translational modification sites. InterPro covers over 78% of all proteins in the Swiss-Prot and TrEMBL components of UniProt. The database is available for text- and sequence-based searches via a webserver (http://www.ebi.ac.uk/interpro), and for download by anonymous FTP (ftp://ftp.ebi.ac.uk/pub/databases/interpro).

A full description of a protein's function requires knowledge of all partner proteins with which it specifically associates. From a functional perspective, 'association' can mean direct physical binding, but can also mean indirect interaction such as participation in the same metabolic pathway or cellular process. Currently, information about protein association is scattered over a wide variety of resources and model organisms. STRING aims to simplify access to this information by providing a comprehensive, yet quality-controlled collection of protein-protein associations for a large number of organisms. The associations are derived from high-throughput experimental data, from the mining of databases and literature, and from predictions based on genomic context analysis. STRING integrates and ranks these associations by benchmarking them against a common reference set, and presents evidence in a consistent and intuitive web interface. Importantly, the associations are extended beyond the organism in which they were originally described, by automatic transfer to orthologous protein pairs in other organisms, where applicable. STRING currently holds 730,000 proteins in 180 fully sequenced organisms, and is available at http://string.embl.de/.

We suggest an annotation strategy for genes encoded by retroviruses and transposable elements (RETRA genes) based on a set of marker protein domains. Usually RETRA genes are masked in vertebrate genomes prior to the application of automated gene prediction pipelines under the assumption that they provide no selective advantage to the host. Yet, we show that about 1000 genes in four vertebrate gene sets analyzed contain at least one RETRA gene marker domain. Using the conservation of genomic neighborhood (synteny), we were able to discriminate between RETRA genes with putative functionality in the vertebrates and those that probably function only in the context of mobile elements. We identified 35 such genes in human, along with their corresponding mouse and rat orthologs; which included almost all known human genes with similarity to mobile elements. The results also imply that the vast majority of the remaining RETRA genes in current gene sets are unlikely to encode vertebrate functions. To automatically annotate RETRA genes in other vertebrate genomes, we provide as a tool a set of marker protein domains and a manually refined list of domesticated or ancestral RETRA genes for rescuing genes with vertebrate functions.

Three integrated genomic context methods were used to annotate uncharacterized proteins in 102 bacterial genomes. Of 7853 orthologous groups with unknown function containing 45,110 proteins, 1738 groups could be linked to functionally associated partners. In many cases, those partners are uncharacterized themselves (hinting at newly identified modules) or have been described in general terms only. However, we were able to assign pathways, cellular processes or physical complexes for 273 groups (encompassing 3624 previously functionally uncharacterized proteins).

DNA microarray experiments have provided vast amounts of data which can be used for inferring gene function. However, most methods for predicting functional associations between genes from expression data are not suited to simultaneous analysis of multiple datasets, and a comprehensive resource of coexpression-based predictions is currently lacking. Here, we present an interactive web resource of gene associations predicted by applying a novel algorithm to all expression data in the Stanford Microarray Database. The underlying pre-computed database currently contains more than 200 000 high-confidence gene associations in 12 different species sampled from a broad taxonomic range. The resource allows every association to be inspected visually and can be accessed at http://www.bork.embl.de/ArrayProspector.

SMART (Simple Modular Architecture Research Tool) is a web tool (http://smart.embl.de/) for the identification and annotation of protein domains, and provides a platform for the comparative study of complex domain architectures in genes and proteins. The January 2004 release of SMART contains 685 protein domains. New developments in SMART are centred on the integration of data from completed metazoan genomes. SMART now uses predicted proteins from complete genomes in its source sequence databases, and integrates these with predictions of orthology. New visualization tools have been developed to allow analysis of gene intron-exon structure within the context of protein domain structure, and to align these displays to provide schematic comparisons of orthologous genes, or multiple transcripts from the same gene. Other improvements include the ability to query SMART by Gene Ontology terms, improved structure database searching and batch retrieval of multiple entries.

InterPro, an integrated documentation resource of protein families, domains and functional sites, was created in 1999 as a means of amalgamating the major protein signature databases into one comprehensive resource. PROSITE, Pfam, PRINTS, ProDom, SMART and TIGRFAMs have been manually integrated and curated and are available in InterPro for text- and sequence-based searching. The results are provided in a single format that rationalises the results that would be obtained by searching the member databases individually. The latest release of InterPro contains 5629 entries describing 4280 families, 1239 domains, 95 repeats and 15 post-translational modifications. Currently, the combined signatures in InterPro cover more than 74% of all proteins in SWISS-PROT and TrEMBL, an increase of nearly 15% since the inception of InterPro. New features of the database include improved searching capabilities and enhanced graphical user interfaces for visualisation of the data. The database is available via a webserver (http://www.ebi.ac.uk/interpro) and anonymous FTP (ftp://ftp.ebi.ac.uk/pub/databases/interpro).

As scientific literature databases like MEDLINE increase in size, so does the time required to search them. Scientists must frequently inspect long lists of references manually, often just reading the titles. XplorMed is a web tool that aids MEDLINE searching by summarizing the subjects contained in the results, thus allowing users to focus on subjects of interest. Here we describe new features added to XplorMed during the last 2 years (http://www.bork.embl-heidelberg.de/xplormed/).

Multidomain proteins predominate in eukaryotic proteomes. Individual functions assigned to different sequence segments combine to create a complex function for the whole protein. While on-line resources are available for revealing globular domains in sequences, there has hitherto been no comprehensive collection of small functional sites/motifs comparable to the globular domain resources, yet these are as important for the function of multidomain proteins. Short linear peptide motifs are used for cell compartment targeting, protein-protein interaction, regulation by phosphorylation, acetylation, glycosylation and a host of other post-translational modifications. ELM, the Eukaryotic Linear Motif server at http://elm.eu.org/, is a new bioinformatics resource for investigating candidate short non-globular functional motifs in eukaryotic proteins, aiming to fill the void in bioinformatics tools. Sequence comparisons with short motifs are difficult to evaluate because the usual significance assessments are inappropriate. Therefore the server is implemented with several logical filters to eliminate false positives. Current filters are for cell compartment, globular domain clash and taxonomic range. In favourable cases, the filters can reduce the number of retained matches by an order of magnitude or more.

Functional links between proteins can often be inferred from genomic associations between the genes that encode them: groups of genes that are required for the same function tend to show similar species coverage, are often located in close proximity on the genome (in prokaryotes), and tend to be involved in gene-fusion events. The database STRING is a precomputed global resource for the exploration and analysis of these associations. Since the three types of evidence differ conceptually, and the number of predicted interactions is very large, it is essential to be able to assess and compare the significance of individual predictions. Thus, STRING contains a unique scoring-framework based on benchmarks of the different types of associations against a common reference set, integrated in a single confidence score per prediction. The graphical representation of the network of inferred, weighted protein interactions provides a high-level view of functional linkage, facilitating the analysis of modularity in biological processes. STRING is updated continuously, and currently contains 261 033 orthologs in 89 fully sequenced genomes. The database predicts functional interactions at an expected level of accuracy of at least 80% for more than half of the genes; it is online at http://www.bork.embl-heidelberg.de/STRING/.

HGVbase (Human Genome Variation database; http://hgvbase.cgb.ki.se, formerly known as HGBASE) is an academic effort to provide a high quality and non-redundant database of available genomic variation data of all types, mostly comprising single nucleotide polymorphisms (SNPs). Records include neutral polymorphisms as well as disease-related mutations. Online search tools facilitate data interrogation by sequence similarity and keyword queries, and searching by genome coordinates is now being implemented. Downloads are freely available in XML, Fasta, SRS, SQL and tagged-text file formats. Each entry is presented in the context of its surrounding sequence and many records are related to neighboring human genes and affected features therein. Population allele frequencies are included wherever available. Thorough semi-automated data checking ensures internal consistency and addresses common errors in the source information. To keep pace with recent growth in the field, we have developed tools for fully automated annotation. All variants have been uniquely mapped to the draft genome sequence and are referenced to positions in EMBL/GenBank files. Data utility is enhanced by provision of genotyping assays and functional predictions. Recent data structure extensions allow the capture of haplotype and genotype information, and a new initiative (along with BiSC and HUGO-MDI) aims to create a central repository for the broad collection of clinical mutations and associated disease phenotypes of interest.

SMART (Simple Modular Architecture Research Tool, http://smart.embl-heidelberg.de) is a web-based resource used for the annotation of protein domains and the analysis of domain architectures, with particular emphasis on mobile eukaryotic domains. Extensive annotation for each domain family is available, providing information relating to function, subcellular localization, phyletic distribution and tertiary structure. The January 2002 release has added more than 200 hand-curated domain models. This brings the total to over 600 domain families that are widely represented among nuclear, signalling and extracellular proteins. Annotation now includes links to the Online Mendelian Inheritance in Man (OMIM) database in cases where a human disease is associated with one or more mutations in a particular domain. We have implemented new analysis methods and updated others. New advanced queries provide direct access to the SMART relational database using SQL. This database now contains information on intrinsic sequence features such as transmembrane regions, coiled-coils, signal peptides and internal repeats. SMART output can now be easily included in users' documents. A SMART mirror has been created at http://smart.ox.ac.uk.

Human single nucleotide polymorphisms (SNPs) represent the most frequent type of human population DNA variation. One of the main goals of SNP research is to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. Non-synonymous coding SNPs (nsSNPs) comprise a group of SNPs that, together with SNPs in regulatory regions, are believed to have the highest impact on phenotype. Here we present a World Wide Web server to predict the effect of an nsSNP on protein structure and function. The prediction method enabled analysis of the publicly available SNP database HGVbase, which gave rise to a dataset of nsSNPs with predicted functionality. The dataset was further used to compare the effect of various structural and functional characteristics of amino acid substitutions responsible for phenotypic display of nsSNPs. We also studied the dependence of selective pressure on the structural and functional properties of proteins. We found that in our dataset the selection pressure against deleterious SNPs depends on the molecular function of the protein, although it is insensitive to several other protein features considered. The strongest selective pressure was detected for proteins involved in transcription regulation.

Human genome polymorphism is expected to play a key role in defining the etiologic basis of phenotypic differences between individuals in aspects such as drug responses and common disease predisposition. Relevant functional DNA changes will probably be located in or near to transcribed sequences, and include many single nucleotide polymorphisms. To aid the future analysis of such genome variation, HGBASE (Human Genic Bi-Allelic SEquences) was constructed as a means to gather human gene-linked polymorphisms from all possible public sources, and show these as a non-redundant set of records in a standardized and user-friendly database endowed with text and sequence based search facilities. After 1 year of presence on the WWW, the HGBASE project has compiled data for over 22 000 records, and this number continues to triple every 6-12 months with data harvested or submitted from all major public genome databases and published literature from the previous decade. Extensive annotation enhancement, internal consistency checking and manual review of every record is undertaken to address potential errors and deficiencies sometimes present in the original source data. The fully polished and comprehensive database is made freely available to all at http://hgbase.cgr.ki.se

Four years after the original sequence submission, we have re-annotated the genome of Mycoplasma pneumoniae to incorporate novel data. The total number of ORFss has been increased from 677 to 688 (10 new proteins were predicted in intergenic regions, two further were newly identified by mass spectrometry and one protein ORF was dismissed) and the number of RNAs from 39 to 42 genes. For 19 of the now 35 tRNAs and for six other functional RNAs the exact genome positions were re-annotated and two new tRNA(Leu) and a small 200 nt RNA were identified. Sixteen protein reading frames were extended and eight shortened. For each ORF a consistent annotation vocabulary has been introduced. Annotation reasoning, annotation categories and comparisons to other published data on M.pneumoniae functional assignments are given. Experimental evidence includes 2-dimensional gel electrophoresis in combination with mass spectrometry as well as gene expression data from this study. Compared to the original annotation, we increased the number of proteins with predicted functional features from 349 to 458. The increase includes 36 new predictions and 73 protein assignments confirmed by the published literature. Furthermore, there are 23 reductions and 30 additions with respect to the previous annotation. mRNA expression data support transcription of 184 of the functionally unassigned reading frames.

SMART (a Simple Modular Architecture Research Tool) allows the identification and annotation of genetically mobile domains and the analysis of domain architectures (http://SMART.embl-heidelberg.de ). More than 400 domain families found in signalling, extra-cellular and chromatin-associated proteins are detectable. These domains are extensively annotated with respect to phyletic distributions, functional class, tertiary structures and functionally important residues. Each domain found in a non-redundant protein database as well as search parameters and taxonomic information are stored in a relational database system. User interfaces to this database allow searches for proteins containing specific combinations of domains in defined taxa.

The repeated occurrence of genes in each other's neighbourhood on genomes has been shown to indicate a functional association between the proteins they encode. Here we introduce STRING (search tool for recurring instances of neighbouring genes), a tool to retrieve and display the genes a query gene repeatedly occurs with in clusters on the genome. The tool performs iterative searches and visualises the results in their genomic context. By finding the genomically associated genes for a query, it delineates a set of potentially functionally associated genes. The usefulness of STRING is illustrated with an example that suggests a functional context for an RNA methylase with unknown specificity.

SMART is a simple modular architecture research tool and database that provides domain identification and annotation on the WWW (http://coot.embl-heidelberg.de/SMART). The tool compares query sequences with its databases of domain sequences and multiple alignments whilst concurrently identifying compositionally biased regions such as signal peptide, transmembrane and coiled coil segments. Annotated and unannotated regions of the sequence can be used as queries in searches of sequence databases. The SMART alignment collection represents more than 250 signalling and extracellular domains. Each alignment is curated to assign appropriate domain boundaries and to ensure its quality. In addition, each domain is annotated extensively with respect to cellular localisation, species distribution, functional class, tertiary structure and functionally important residues.

We propose a new method for detecting conserved RNA secondary structures in a family of related RNA sequences. Our method is based on a combination of thermodynamic structure prediction and phylogenetic comparison. In contrast to purely phylogenetic methods, our algorithm can be used for small data sets of approximately 10 sequences, efficiently exploiting the information contained in the sequence variability. The procedure constructs a prediction only for those parts of sequences that are consistent with a single conserved structure. Our implementation produces reasonable consensus structures without user interference. As an example we have analysed the complete HIV-1 and hepatitis C virus (HCV) genomes as well as the small segment of hantavirus. Our method confirms the known structures in HIV-1 and predicts previously unknown conserved RNA secondary structures in HCV.

Using computer methods for database search, multiple alignment, protein sequence motif analysis and secondary structure prediction, a putative new RNA-binding motif was identified. The novel motif is conserved in yeast omnipotent translation termination suppressor SUP1, the related DOM34 protein and its pseudogene homologue; three groups of eukaryotic and archaeal ribosomal proteins, namely L30e, L7Ae/S6e and S12e; an uncharacterized Bacillus subtilis protein related to the L7A/S6e group; and Escherichia coli ribosomal protein modification enzyme RimK. We hypothesize that a new type of RNA-binding domain may be utilized to deliver additional activities to the ribosome.