Next-generation sequencing (NGS) is increasingly being adopted as the backbone of biomedical research. With the commercialization of various affordable desktop sequencers, NGS will be reached by increasing numbers of cellular and molecular biologists, necessitating community consensus on bioinformatics protocols to tackle the exponential increase in quantity of sequence data. The current resources for NGS informatics are extremely fragmented. Finding a centralized synthesis is difficult. A multitude of tools exist for NGS data analysis; however, none of these satisfies all possible uses and needs. This gap in functionality could be filled by integrating different methods in customized pipelines, an approach helped by the open-source nature of many NGS programmes. Drawing from community spirit and with the use of the Wikipedia framework, we have initiated a collaborative NGS resource: The NGS WikiBook. We have collected a sufficient amount of text to incentivize a broader community to contribute to it. Users can search, browse, edit and create new content, so as to facilitate self-learning and feedback to the community. The overall structure and style for this dynamic material is designed for the bench biologists and non-bioinformaticians. The flexibility of online material allows the readers to ignore details in a first read, yet have immediate access to the information they need. Each chapter comes with practical exercises so readers may familiarize themselves with each step. The NGS WikiBook aims to create a collective laboratory book and protocol that explains the key concepts and describes best practices in this fast-evolving field.

MOTIVATION: Advancing the search, publication and integration of bioinformatics tools and resources demands consistent machine-understandable descriptions. A comprehensive ontology allowing such descriptions is therefore required. RESULTS: EDAM is an ontology of bioinformatics operations (tool or workflow functions), types of data and identifiers, application domains and data formats. EDAM supports semantic annotation of diverse entities such as Web services, databases, programmatic libraries, standalone tools, interactive applications, data schemas, datasets and publications within bioinformatics. EDAM applies to organizing and finding suitable tools and data and to automating their integration into complex applications or workflows. It includes over 2200 defined concepts and has successfully been used for annotations and implementations. AVAILABILITY: The latest stable version of EDAM is available in OWL format from http://edamontology.org/EDAM.owl and in OBO format from http://edamontology.org/EDAM.obo. It can be viewed online at the NCBO BioPortal and the EBI Ontology Lookup Service. For documentation and license please refer to http://edamontology.org. This article describes version 1.2 available at http://edamontology.org/EDAM_1.2.owl. CONTACT: [email protected].

UNLABELLED: Protein Structural Interactome map (PSIMAP) is a global interaction map that describes domain-domain and protein-protein interaction information for known Protein Data Bank structures. It calculates the Euclidean distance to determine interactions between possible pairs of structural domains in proteins. PSIbase is a database and file server for protein structural interaction information calculated by the PSIMAP algorithm. PSIbase also provides an easy-to-use protein domain assignment module, interaction navigation and visual tools. Users can retrieve possible interaction partners of their proteins of interests if a significant homology assignment is made with their query sequences. AVAILABILITY: http://psimap.org and http://psibase.kaist.ac.kr/

MOTIVATION: Many genomes have been completely sequenced. However, detecting and analyzing their protein-protein interactions by experimental methods such as co-immunoprecipitation, tandem affinity purification and Y2H is not as fast as genome sequencing. Therefore, a computational prediction method based on the known protein structural interactions will be useful to analyze large-scale protein-protein interaction rules within and among complete genomes. RESULTS: We confirmed that all the predicted protein family interactomes (the full set of protein family interactions within a proteome) of 146 species are scale-free networks, and they share a small core network comprising 36 protein families related to indispensable cellular functions. We found two fundamental differences among prokaryotic and eukaryotic interactomes: (1) eukarya had significantly more hub families than archaea and bacteria and (2) certain special hub families determined the topology of the eukaryotic interactomes. Our comparative analysis suggests that a very small number of expansive protein families led to the evolution of interactomes and seemed to have played a key role in species diversification. SUPPLEMENTARY INFORMATION: http://interactomics.org.

BACKGROUND: Most proteins function by interacting with other molecules. Their interaction interfaces are highly conserved throughout evolution to avoid undesirable interactions that lead to fatal disorders in cells. Rational drug discovery includes computational methods to identify the interaction sites of lead compounds to the target molecules. Identifying and classifying protein interaction interfaces on a large scale can help researchers discover drug targets more efficiently. DESCRIPTION: We introduce a large-scale protein domain interaction interface database called InterPare http://interpare.net. It contains both inter-chain (between chains) interfaces and intra-chain (within chain) interfaces. InterPare uses three methods to detect interfaces: 1) the geometric distance method for checking the distance between atoms that belong to different domains, 2) Accessible Surface Area (ASA), a method for detecting the buried region of a protein that is detached from a solvent when forming multimers or complexes, and 3) the Voronoi diagram, a computational geometry method that uses a mathematical definition of interface regions. InterPare includes visualization tools to display protein interior, surface, and interaction interfaces. It also provides statistics such as the amino acid propensities of queried protein according to its interior, surface, and interface region. The atom coordinates that belong to interface, surface, and interior regions can be downloaded from the website. CONCLUSION: InterPare is an open and public database server for protein interaction interface information. It contains the large-scale interface data for proteins whose 3D-structures are known. As of November 2004, there were 10,583 (Geometric distance), 10,431 (ASA), and 11,010 (Voronoi diagram) entries in the Protein Data Bank (PDB) containing interfaces, according to the above three methods. In the case of the geometric distance method, there are 31,620 inter-chain domain-domain interaction interfaces and 12,758 intra-chain domain-domain interfaces.

MOTIVATION: Protein interactions provide an important context for the understanding of function. Experimental approaches have been complemented with computational ones, such as PSIMAP, which computes domain-domain interactions for all multi-domain and multi-chain proteins in the Protein Data Bank (PDB). PSIMAP has been used to determine that superfamilies occurring in many species have many interaction partners, to show examples of convergent evolution through shared interaction partners and to uncover complexes in the interaction map. To determine an interaction, the original PSIMAP algorithm checks all residue pairs of any domain pair defined by classification systems such as SCOP. The computation takes several days for the PDB. The computation of PSIMAP has two shortcomings: first, the original PSIMAP algorithm considers only interactions of residue pairs rather than atom pairs losing information for detailed analysis of contact patterns. At the atomic level the original algorithm would take months. Second, with the superlinear growth of PDB, PSIMAP is not sustainable. RESULTS: We address these two shortcomings by developing a family of new algorithms for the computation of domain-domain interactions based on the idea of bounding shapes, which are used to prune the search space. The best of the algorithms improves on the old PSIMAP algorithm by a factor of 60 on the PDB. Additionally, the algorithms allow a distributed computation, which we carry out on a farm of 80 Linux PCs. Overall, the new algorithms reduce the computation at atomic level from months to 20 min. The combination of pruning and distribution makes the new algorithm scalable and sustainable even with the superlinear growth in PDB.

MOTIVATION: Interacting pairs of proteins should co-evolve to maintain functional and structural complementarity. Consequently, such a pair of protein families shows similarity between their phylogenetic trees. Although the tendency of co-evolution has been known for various ligand-receptor pairs, it has not been studied systematically in the widest possible scope. We investigated the degree of co-evolution for more than 900 family pairs in a global protein structural interactome map (PSIMAP--a map of all the structural domain-domain interactions in the PDB). RESULTS: There was significant correlation in 45% of the total SCOPs Family level pairs, rising to 78% in 454 reliable family interactions. Expectedly, the intra-molecular interactions between protein families showed stronger co-evolution than inter-molecular interactions. However, both types of interaction have a fundamentally similar pattern of co-evolution except for cases where different interfaces are involved. These results validate the use of co-evolution analysis with predictive methods such as PSIMAP to improve the accuracy of prediction based on "homologous interaction". The tendency of co-evolution enabled a nearly 5-fold enrichment in the identification of true interactions among the potential interlogues in PSIMAP. The estimated sensitivity was 79.2%, and the specificity was 78.6%. AVAILABILITY: The results of co-evolution analysis are available online at http://www.biointeraction.org

BACKGROUND: Large-scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in the PDB. PSIMAP incorporates both functional and evolutionary information into a single network. RESULTS: We present a global analysis of PSIMAP using several distinct network measures relating to centrality, interactivity, fault-tolerance, and taxonomic diversity. We found the following results: Centrality: we show that the center and barycenter of PSIMAP do not coincide, and that the superfamilies forming the barycenter relate to very general functions, while those constituting the center relate to enzymatic activity. Interactivity: we identify the P-loop and immunoglobulin superfamilies as the most highly interactive. We successfully use connectivity and cluster index, which characterise the connectivity of a superfamily's neighbourhood, to discover superfamilies of complex I and II. This is particularly significant as the structure of complex I is not yet solved. Taxonomic diversity: we found that highly interactive superfamilies are in general taxonomically very diverse and are thus amongst the oldest. Fault-tolerance: we found that the network is very robust as for the majority of superfamilies removal from the network will not break up the network. CONCLUSIONS: Overall, we can single out the P-loop containing nucleotide triphosphate hydrolases superfamily as it is the most highly connected and has the highest taxonomic diversity. In addition, this superfamily has the highest interaction rank, is the barycenter of the network (it has the shortest average path to every other superfamily in the network), and is an articulation vertex, whose removal will disconnect the network. More generally, we conclude that the graph-theoretic and taxonomic analysis of PSIMAP is an important step towards the understanding of protein function and could be an important tool for tracing the evolution of life at the molecular level.