Automated data integration for developmental biological research

In an era exploding with genome-scale data, a major challenge for developmental biologists is how to extract significant clues from these publicly available data to benefit our studies of individual genes, and how to use them to improve our understanding of development at a systems level. Several studies have successfully demonstrated new approaches to classic developmental questions by computationally integrating various genome-wide data sets. Such computational approaches have shown great potential for facilitating research: instead of testing 20,000 genes, researchers might test 200 to the same effect. We discuss the nature and state of this art as it applies to developmental research.     

Conceptual data modelling for bioinformatics

Current research in the biosciences depends heavily on the effective exploitation of huge amounts of data. These are in disparate formats, remotely dispersed, and based on the different vocabularies of various disciplines. Furthermore, data are often stored or distributed using formats that leave implicit many important features relating to the structure and semantics of the data. Conceptual data modelling involves the development of implementation-independent models that capture and make explicit the principal structural properties of data. Entities such as a biopolymer or a reaction, and their relations, eg catalyses, can be formalised using a conceptual data model. Conceptual models are implementation-independent and can be transformed in systematic ways for implementation using different platforms, eg traditional database management systems. This paper describes the basics of the most widely used conceptual modelling notations, the ER (entity-relationship) model and the class diagrams of the UML (unified modelling language), and illustrates their use through several examples from bioinformatics. In particular, models are presented for protein structures and motifs, and for genomic sequences.     

The Genome Sequencer FLX System--longer reads, more applications, straight forward bioinformatics and more complete data sets

The Genome Sequencer FLX System (GS FLX), powered by 454 Sequencing, is a next-generation DNA sequencing technology featuring a unique mix of long reads, exceptional accuracy, and ultra-high throughput. It has been proven to be the most versatile of all currently available next-generation sequencing technologies, supporting many high-profile studies in over seven applications categories. GS FLX users have pursued innovative research in de novo sequencing, re-sequencing of whole genomes and target DNA regions, metagenomics, and RNA analysis. 454 Sequencing is a powerful tool for human genetics research, having recently re-sequenced the genome of an individual human, currently re-sequencing the complete human exome and targeted genomic regions using the NimbleGen sequence capture process, and detected low-frequency somatic mutations linked to cancer.     

Tools and collaborative environments for bioinformatics research

Advanced research requires intensive interaction among a multitude of actors, often possessing different expertise and usually working at a distance from each other. The field of collaborative research aims to establish suitable models and technologies to properly support these interactions. In this article, we first present the reasons for an interest of Bioinformatics in this context by also suggesting some research domains that could benefit from collaborative research. We then review the principles and some of the most relevant applications of social networking, with a special attention to networks supporting scientific collaboration, by also highlighting some critical issues, such as identification of users and standardization of formats. We then introduce some systems for collaborative document creation, including wiki systems and tools for ontology development, and review some of the most interesting biological wikis. We also review the principles of Collaborative Development Environments for software and show some examples in Bioinformatics. Finally, we present the principles and some examples of Learning Management Systems. In conclusion, we try to devise some of the goals to be achieved in the short term for the exploitation of these technologies.     

XML, bioinformatics and data integration

Motivation: The eXtensible Markup Language (XML) is an emerging standard for structuring documents, notably for the World Wide Web. In this paper, the authors present XML and examine its use as a data language for bioinformatics. In particular, XML is compared to other languages, and some of the potential uses of XML in bioinformatics applications are presented. The authors propose to adopt XML for data interchange between databases and other sources of data. Finally the discussion is illustrated by a test case of a pedigree data model in XML. Contact: Emmanuel.Barillot@infobiogen.fr     

Databases as instruments for analysis of large-scale data sets of interactions between molecular biological objects

We have surveyed the most well-known and accessible information resources, consolidating data on interactions of molecular biological objects. Three main criteria are discussed: quality (coverage and reliability) of the information present, ability to analyze experimental data, and ease of results visualization. Several algorithms of data analysis, based on various types of resources, and statistics for its evaluation are named.     

Alternatives to animal experimentation for hormonal compounds research

Alternatives to animal testing and the identification of reliable methods that may decrease the need for animals are currently the subject of intense investigation worldwide. Alternative testing procedures are particularly important for synthetic and natural chemicals that exert their biological actions through binding nuclear receptors, called nuclear receptors-interacting compounds (NR-ICs), for which research is increasingly emphasizing the limits of several models in the accurate estimation of the physiological consequences of exposure to these compounds. In particular, estrogen receptor interacting compounds (ER-ICs) have a great impact on human health from the therapeutic, nutritional, and toxicological point of view due to the highly permissive nature of the estrogen receptors towards a large number of natural and synthetic compounds. Similar to in vitro systems, recently generated animal models (e.g., animal models generated for the study of estrogen receptor ligands) may fulfill the 3R principles: refine, reduce, and replace. If used correctly, NR-regulated models, such as reporter mice, xenopus, or zebrafish, and models obtained by somatic gene transfer in reporter systems, combined with imaging technologies, may contribute to strongly decreasing the overall number of animals required for NR-IC testing and research. With these models, flexible and highly standardized parameters and reporter marker quantification can be obtained. Here, we highlight the need for the substitution of currently used testing models with more appropriate ones that can reproduce the features and reactivity of specific mammalian target tissue/organs. We consider the promotion of this advancement a research priority bearing scientific, economic, social, and ethical relevance.     

Organismic sets and biological epimorphism

It is shown that the principle of biological epimorphism (Rashevsky,Mathematical Principles in Biology and Their Applications, Springfield, Ill.: Charles Thomas, 1960) is contained in the theory of organismic sets (Bull. Math. Biophysics,29, 139–152, 1967) if an additional postulate not directly connected to mappings is made.     

数据挖掘在生物信息学中的应用

借助各种应用数学和计算机技术 ,将大量积累并急需处理的生物信息数据利用起来 ,探索生物信息中的规律 ,是当前国内国际生物信息学研究的热点和重点。其中数据挖掘技术在生物信息研究中发挥着巨大的作用。     

The Gaggle: An open-source software system for integrating bioinformatics software and data sources

Background  

Systems biologists work with many kinds of data, from many different sources, using a variety of software tools. Each of these tools typically excels at one type of analysis, such as of microarrays, of metabolic networks and of predicted protein structure. A crucial challenge is to combine the capabilities of these (and other forthcoming) data resources and tools to create a data exploration and analysis environment that does justice to the variety and complexity of systems biology data sets. A solution to this problem should recognize that data types, formats and software in this high throughput age of biology are constantly changing.     

An international showcase of bioinformatics research

A report on the 11th International Conference on Intelligent Systems for Molecular Biology, Brisbane, Queensland, Australia, 29 June - 3 July 2003.     

Discriminatory power of different arthropod data sets for the biological monitoring of anthropogenic disturbance in tropical forests

Arthropods were monitored by local parataxonomists at 12 sites of increasing anthropogenic disturbance (old and young secondary forests, savanna and cultivated gardens) at Gamba, Gabon. We report on the discriminatory power of different data sets with regard to the classification of sites along the disturbance gradient, using preliminary data accounting for 13 surveys and 142425 arthropods collected by Malaise, pitfall and yellow-pan traps. We compared the performance of different data sets. These were based upon ordinal, familial and guild composition, or upon 22 target taxa sorted to morphospecies and either considered in toto or grouped within different functional guilds. Finally we evaluated predictor sets made up of a few families or other target taxa, selected on the basis of their indicator value index. Although the discriminatory power of data sets based on ordinal categories and guilds was low, that of target taxa belonging to chewers, parasitoids and predators was much higher. The data sets that best discriminated among sites of differing degrees of disturbance were the restricted sets of indicator families and target taxa. This validates the concept of predictor sets for species-rich tropical systems. Including or excluding rare taxa in the analyses did not alter these conclusions. We conclude that calibration studies similar to ours are needed elsewhere in the tropics and that this strategy will allow to devise a representative and efficient biotic index for the biological monitoring of terrestrial arthropod assemblages in the tropics.