Automatic synchronization and distribution of biological databases and software over low-bandwidth networks among developing countries

Bioinformatics involves the collection, organization and analysis of large amounts of biological data, using networks of computers and databases. Developing countries in the Asia-Pacific region are just moving into this new field of information-based biotechnology. However, the computational infrastructure and network bandwidths available in these countries are still at a basic level compared to that in developed countries. In this study, we assessed the utility of a BitTorrent-based Peer-to-Peer (btP2P) file distribution model for automatic synchronization and distribution of large amounts of biological data among developing countries. The initial country-level nodes in the Asia-Pacific region comprised Thailand, Korea and Singapore. The results showed a significant improvement in download performance using btP2P--three times faster overall download performance than conventional File Transfer Protocol (FTP). This study demonstrated the reliability of btP2P in the dissemination of continuously growing multi-gigabyte biological databases across the three Asia-Pacific countries. The download performance for btP2P can be further improved by including more nodes from other countries into the network. This suggests that the btP2P technology is appropriate for automatic synchronization and distribution of biological databases and software over low-bandwidth networks among developing countries in the Asia-Pacific region. AVAILABILITY: http://everest.bic.nus.edu.sg/p2p/     

Integrating biological databases

Recent years have seen an explosion in the amount of available biological data. More and more genomes are being sequenced and annotated, and protein and gene interaction data are accumulating. Biological databases have been invaluable for managing these data and for making them accessible. Depending on the data that they contain, the databases fulfil different functions. But, although they are architecturally similar, so far their integration has proved problematic.     

Light-weight integration of molecular biological databases

MOTIVATION: Due to the increasing number of molecular biological databases and the exponential growth of their contents, database integration is an important topic of research in bioinformatics. Existing approaches in this area have in common that considerable efforts are needed to provide integrated access to heterogeneous data sources. RESULTS: This article describes the LIMBO architecture as a light-weight approach to molecular biological database integration. By building systems upon this architecture, the efforts needed for database integration can be significantly lowered. AVAILABILITY: As an illustration of the principle usefulness of the underlying ideas, a prototypical implementation based upon the LIMBO architecture is described. This implementation is exclusively based on freely available open source components like the PostgreSQL database management system and the BioRuby project. Additional files and modified components are available upon request from the author.     

Object-oriented parsing of biological databases with Python

MOTIVATION: While database activities in the biological area are increasing rapidly, rather little is done in the area of parsing them in a simple and object-oriented way. RESULTS: We present here an elegant, simple yet powerful way of parsing biological flat-file databases. We have taken EMBL, SWISSPROT and GENBANK as examples. EMBL and SWISS-PROT do not differ much in the format structure. GENBANK has a very different format structure than EMBL and SWISS-PROT. Extracting the desired fields in an entry (for example a sub-sequence with an associated feature) for later analysis is a constant need in the biological sequence-analysis community: this is illustrated with tools to make new splice-site databases. The interface to the parser is abstract in the sense that the access to all the databases is independent from their different formats, since parsing instructions are hidden.     

Storing biological sequence databases in relational form

SUMMARY: We have created a set of applications using Perl and Java in combination with XML technology to install biological sequence databases into an Oracle RDBMS. An easy-to-use interface using Java has been created for database query and other tools developed to integrate with our in-house bioinformatics applications. AVAILIBILITY: The database schema, DTD file, and source codes are available from the authors via email. CONTACT: guochun_ xie@merck. com     

DBcat: a catalog of 500 biological databases

The DBcat (http://www.infobiogen.fr/services/dbcat ) is a comprehensive catalog of biological databases, maintained and curated at Infobiogen. It contains 500 databases classified by application domains. The DBcat is a structured flat-file library, that can be searched by means of an SRS server or a dedicated Web interface. The files are available for download from Infobiogen anonymous ftp server.     

Dominating biological networks

Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI) networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of "biologically central (BC)" genes (i.e., their protein products), such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network.To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC) role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs) in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its "spine" that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.     

SEMEDA: ontology based semantic integration of biological databases

MOTIVATION: Many molecular biological databases are implemented on relational Database Management Systems, which provide standard interfaces like JDBC and ODBC for data and metadata exchange. By using these interfaces, many technical problems of database integration vanish and issues related to semantics remain, e.g. the use of different terms for the same things, different names for equivalent database attributes and missing links between relevant entries in different databases. RESULTS: In this publication, principles and methods that were used to implement SEMEDA (Semantic Meta Database) are described. Database owners can use SEMEDA to provide semantically integrated access to their databases as well as to collaboratively edit and maintain ontologies and controlled vocabularies. Biologists can use SEMEDA to query the integrated databases in real time without having to know the structure or any technical details of the underlying databases. AVAILABILITY: SEMEDA is available at http://www-bm.ipk-gatersleben.de/semeda/. Database providers who intend to grant access to their databases via SEMEDA are encouraged to contact the authors.     

From biological databases to platforms for biomedical discovery

The use of high-throughput DNA sequencing and proteomic methods has led to an unprecedented increase in the amount of genomic and proteomic data. Application of computing technologies and development of computational tools to analyze and present these data has not kept pace with the accumulation of information. Here, we discuss the use of different database systems to store biological information and mention some of the key emerging computing technologies that are likely to have a key role in the future of bioinformatics.     

Scale-freeness and biological networks

The notion of scale-freeness and its prevalence in both natural and artificial networks have recently attracted much attention. The concept of scale-freeness is enthusiastically applied to almost any conceivable network, usually with affirmative conclusions. Well-known scale-free examples include the internet, electric lines among power plants, the co-starring of movie actors, the co-authorship of researchers, food webs, and neural, protein-protein interactional, genetic, and metabolic networks. The purpose of this review is to clarify the relationship between scale-freeness and power-law distribution, and to assess critically the previous related works, especially on biological networks. In addition, I will focus on the close relationship between power-law distribution and lognormal distribution to show that power-law distribution is not a special characteristic of natural selection.     

microRNA调控的生物网络

生物网络是生物体内各种分子通过相互作用来完成各种复杂的生物功能的一个体系。网络水平的研究,有助于我们从整体上理解生物体内各种复杂事件发生的内在机制。microRNA（miRNA）是一类在转录后水平调控基因表达的小RNA分子。研究结果表明,miRNA调控的靶基因分布范围很广,因此必然与目前所研究的生物网络有着各种各样的联系。对这种关系的揭示,将对阐明miRNA的调控规律起到重要的作用。本文重点讨论了miRNA调控的基因调控网络、蛋白质相互作用网络以及细胞信号传导网络的特征。此外,还总结了miRNA调控的网络模体（motif）和miRNA协同作用网络的特征。     

Integration of metabolic databases for the reconstruction of genome-scale metabolic networks

Background  

Genome-scale metabolic reconstructions have been recognised as a valuable tool for a variety of applications ranging from metabolic engineering to evolutionary studies. However, the reconstruction of such networks remains an arduous process requiring a high level of human intervention. This process is further complicated by occurrences of missing or conflicting information and the absence of common annotation standards between different data sources.     

Semantic search among heterogeneous biological databases based on gene ontology

Various biological database systems including datacapture, data storage, data retrieval and other data pro-cessing methods have been developed. These systems havebecome effective tools for today’s genomics and relatedstudies. However, the highly distribu…     

Functional model of biological neural networks

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.     

Computing topological parameters of biological networks

Rapidly increasing amounts of molecular interaction data are being produced by various experimental techniques and computational prediction methods. In order to gain insight into the organization and structure of the resultant large complex networks formed by the interacting molecules, we have developed the versatile Cytoscape plugin NetworkAnalyzer. It computes and displays a comprehensive set of topological parameters, which includes the number of nodes, edges, and connected components, the network diameter, radius, density, centralization, heterogeneity, and clustering coefficient, the characteristic path length, and the distributions of node degrees, neighborhood connectivities, average clustering coefficients, and shortest path lengths. NetworkAnalyzer can be applied to both directed and undirected networks and also contains extra functionality to construct the intersection or union of two networks. It is an interactive and highly customizable application that requires no expert knowledge in graph theory from the user. AVAILABILITY: NetworkAnalyzer can be downloaded via the Cytoscape web site: http://www.cytoscape.org