一种通用的、基于Agent的生物资源整合架构

众所周知,随着基因组测序工作的蓬勃发展和后基因组时代的到来,生物信息学数据呈指数级增长.生物界在享受着资源共享所带来便利的同时,也随着数据总量和复杂性的不断增加而变得异构化和分布化.目前,各种生物计算软件和数据库资源通常标准不一而且很难兼容.因此,如何在这些异构资源之间实现数据集成与软件共享是有效利用生物信息资源的关键.为解决以上问题,本文提出了一种新型的数据整合架构,该架构通过将web服务与并行计算相结合的方法,轻松地实现了对异地资源数据的访问、提取、转化以及整合.实验证明,本系统在处理异构、海量数据方面有着巨大的计算潜力.     

From information to understanding: the role of model organism databases in comparative and functional genomics

Data integration is key to functional and comparative genomics because integration allows diverse data types to be evaluated in new contexts. To achieve data integration in a scalable and sensible way, semantic standards are needed, both for naming things (standardized nomenclatures, use of key words) and also for knowledge representation. The Mouse Genome Informatics database and other model organism databases help to close the gap between information and understanding of biological processes because these resources enforce well-defined nomenclature and knowledge representation standards. Model organism databases have a critical role to play in ensuring that diverse kinds of data, especially genome-scale data sets and information, remain useful to the biological community in the long-term. The efforts of model organism database groups ensure not only that organism-specific data are integrated, curated and accessible but also that the information is structured in such a way that comparison of biological knowledge across model organisms is facilitated.     

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.     

新一代植物志：iFlora

进入21世纪,随着分子生物学及计算机信息等技术的快速发展,人们认知自然的手段和方式发生了根本性的变化。在现有电子植物志（eFlora）的基础上,融入新一代测序技术、DNA条形码数据、地理信息数据和计算机信息技术等新元素的新一代植物志（iFlora）应运而生。iFlora是通过系列关键技术的集成和攻关,构建便捷、准确识别植物和掌握相关数字化信息的新一代植物志（或智能装备）,它将极大地促进植物分类学和系统发育、演化生物学、生态学、生物地理学和保护生物学等相关学科的发展,有效地服务于生物多样性保护和生物资源可持续利用、国家生态安全和社会公共教育等,并进一步提升公众对生物多样性的认识。iFlora的实施,将为培育和拓展物种识别圈（taxasphere）和生物文化圈（bioliterate world）做出应有的贡献,并可能成为引领国际植物学发展新的生长点。     

The Next-Generation Flora iFlora

The 21st century has witnessed a rapid development in technologies of molecular biology and computer informatics. Fundamental changes have taken place in means and methods in which humans take cognition of the world. Based on the currently available eFlora and combining this with elements of next generation sequencing techniques, DNA sequence data, geographical information system data and computer information technology, the next-generation Flora (iFlora) is bursting. Through a series of key technological innovations and integrations, the main objective of iFlora is to construct the next-generation Flora, which will fulfill the function of accurately and rapidly identifying species and acquiring species related digital information. iFlora will greatly advance the development of plant taxonomy, phylogenetics, evolutionary biology, ecology, biogeography, conservation biology and other related disciplines. Furthermore, iFlora will be a valuable tool for biodiversity conservation and sustainable utilization of biological resources, ecological security, public education and services, and will profoundly promote public understanding of biodiversity. The application of iFlora will tremendously nurture and boost the taxasphere and bioliterate world, and will be a new focal point that may reshape modern botany at the global and regional levels.     

Glycoproteomic and glycomic databases

Protein glycosylation serves critical roles in the cellular and biological processes of many organisms. Aberrant glycosylation has been associated with many illnesses such as hereditary and chronic diseases like cancer, cardiovascular diseases, neurological disorders, and immunological disorders. Emerging mass spectrometry (MS) technologies that enable the high-throughput identification of glycoproteins and glycans have accelerated the analysis and made possible the creation of dynamic and expanding databases. Although glycosylation-related databases have been established by many laboratories and institutions, they are not yet widely known in the community. Our study reviews 15 different publicly available databases and identifies their key elements so that users can identify the most applicable platform for their analytical needs. These databases include biological information on the experimentally identified glycans and glycopeptides from various cells and organisms such as human, rat, mouse, fly and zebrafish. The features of these databases - 7 for glycoproteomic data, 6 for glycomic data, and 2 for glycan binding proteins are summarized including the enrichment techniques that are used for glycoproteome and glycan identification. Furthermore databases such as Unipep, GlycoFly, GlycoFish recently established by our group are introduced. The unique features of each database, such as the analytical methods used and bioinformatical tools available are summarized. This information will be a valuable resource for the glycobiology community as it presents the analytical methods and glycosylation related databases together in one compendium. It will also represent a step towards the desired long term goal of integrating the different databases of glycosylation in order to characterize and categorize glycoproteins and glycans better for biomedical research.     

Human Proteinpedia as a resource for clinical proteomics

Clinical proteomics is an emerging field that deals with the use of proteomic technologies for medical applications. With a major objective of identifying proteins involved in pathological processes and as potential biomarkers, this field is already gaining momentum. Consequently, clinical proteomics data are being generated at a rapid pace, although mechanisms of sharing such data with the biomedical community lag far behind. Most of these data are either provided as supplementary information through journal web sites or directly made available by the authors through their own web resources. Integration of these data within a single resource that displays information in the context of individual proteins is likely to enhance the use of proteomic data in biomedical research. Human Proteinpedia is one such portal that unifies human proteomic data under a single banner. The goal of this resource is to ultimately capture and integrate all proteomic data obtained from individual studies on normal and diseased tissues. We anticipate that harnessing of these data will help prioritize experiments related to protein targets and also permit meta-analysis to uncover molecular signatures of disease. Finally, we encourage all biomedical investigators to maximize dissemination of their valuable proteomic data to rest of the community by active participation in existing repositories such as Human Proteinpedia.     

Surveying the manifold divergence of an entire protein class for statistical clues to underlying biochemical mechanisms

Certain residues have no known function yet are co-conserved across distantly related protein families and diverse organisms, suggesting that they perform critical roles associated with as-yet-unidentified molecular properties and mechanisms. This raises the question of how to obtain additional clues regarding these mysterious biochemical phenomena with a view to formulating experimentally testable hypotheses. One approach is to access the implicit biochemical information encoded within the vast amount of genomic sequence data now becoming available. Here, a new Gibbs sampling strategy is formulated and implemented that can partition hundreds of thousands of sequences within a major protein class into multiple, functionally-divergent categories based on those pattern residues that best discriminate between categories. The sampler precisely defines the partition and pattern for each category by explicitly modeling unrelated, non-functional and related-yet-divergent proteins that would otherwise obscure the analysis. To aid biological interpretation, auxiliary routines can characterize pattern residues within available crystal structures and identify those structures most likely to shed light on the roles of pattern residues. This approach can be used to define and annotate automatically subgroup-specific conserved domain profiles based on statistically-rigorous empirical criteria rather than on the subjective and labor-intensive process of manual curation. Incorporating such profiles into domain database search sites (such as the NCBI BLAST site) will provide biologists with previously inaccessible molecular information useful for hypothesis generation and experimental design. Analyses of P-loop GTPases and of AAA+ ATPases illustrate the sampler's ability to obtain such information.     

用于寡核苷酸二级结构预测的热力学数据库研究进展

基于核酸分子杂交的生物技术（如PCR）在病原微生物检测、临床诊断等诸多领域中应用广泛,此类技术的可靠性在于寡核苷酸分子与其靶点结合的高稳定性与特异性,而精确预测寡核苷酸与靶分子结合的二级结构是分析其稳定性与特异性的关键。其中,基于热力学的最近邻模型是寡核苷酸二级结构预测最为可靠的计算方法,但其精确性强烈依赖于精确的热力学参数。由于寡核苷酸分子二级结构的复杂性,除了完美匹配外,还需要错配、内环、膨胀环、末端摇摆、CNG重复、GU摆动等特殊结构的热力学数据。本文综述了近年来用于寡核苷酸二级结构预测的有效热力学数据库及相关计算方法,并指出当前热力学数据库的局限及未来发展方向。     

Protein–protein interactions and genetic diseases: The interactome

Protein–protein interactions mediate essentially all biological processes. Despite the quality of these data being widely questioned a decade ago, the reproducibility of large-scale protein interaction data is now much improved and there is little question that the latest screens are of high quality. Moreover, common data standards and coordinated curation practices between the databases that collect the interactions have made these valuable data available to a wide group of researchers. Here, I will review how protein–protein interactions are measured, collected and quality controlled. I discuss how the architecture of molecular protein networks has informed disease biology, and how these data are now being computationally integrated with the newest genomic technologies, in particular genome-wide association studies and exome-sequencing projects, to improve our understanding of molecular processes perturbed by genetics in human diseases. This article is part of a Special Issue entitled: From Genome to Function.     

Ontologies for data and knowledge sharing in biology: plant ROS signaling as a case study

Modern technologies have rapidly transformed biology into a data-intensive discipline. In addition to the enormous amounts of existing experimental data in the literature, every new study can produce a large amount of new data, resulting in novel ideas and more publications. In order to understand a biological process as completely as possible, scientists should be able to combine and analyze all such information. Not only molecular biology and bioinformatics, but all the other domains of biology including plant biology, require tools and technologies that enable experts to capture knowledge within distributed and heterogeneous sources of information. Ontologies have proven to be one of the most-useful means of constructing and formalizing expert knowledge. The key feature of an ontology is that it represents a computer-interpretable model of a particular subject area. This article outlines the importance of ontologies for systems biology, data integration and information analyses, as illustrated through the example of reactive oxygen species (ROS) signaling networks in plants.     

Bioimage informatics: a new area of engineering biology

In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called 'bioimage informatics'. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources.     

Automation of in-silico data analysis processes through workflow management systems

Data integration is needed in order to cope with the huge amounts of biological information now available and to perform data mining effectively. Current data integration systems have strict limitations, mainly due to the number of resources, their size and frequency of updates, their heterogeneity and distribution on the Internet. Integration must therefore be achieved by accessing network services through flexible and extensible data integration and analysis network tools. EXtensible Markup Language (XML), Web Services and Workflow Management Systems (WMS) can support the creation and deployment of such systems. Many XML languages and Web Services for bioinformatics have already been designed and implemented and some WMS have been proposed. In this article, we review a methodology for data integration in biomedical research that is based on these technologies. We also briefly describe some of the available WMS and discuss the current limitations of this methodology and the ways in which they can be overcome.     

Natural genetic variation in Arabidopsis: tools, traits and prospects for evolutionary ecology

BACKGROUND: The model plant Arabidopsis thaliana (Arabidopsis) shows a wide range of genetic and trait variation among wild accessions. Because of its unparalleled biological and genomic resources, the potential of Arabidopsis for molecular genetic analysis of this natural variation has increased dramatically in recent years. SCOPE: Advanced genomics has accelerated molecular phylogenetic analysis and gene identification by quantitative trait loci (QTL) mapping and/or association mapping in Arabidopsis. In particular, QTL mapping utilizing natural accessions is now becoming a major strategy of gene isolation, offering an alternative to artificial mutant lines. Furthermore, the genomic information is used by researchers to uncover the signature of natural selection acting on the genes that contribute to phenotypic variation. The evolutionary significance of such genes has been evaluated in traits such as disease resistance and flowering time. However, although molecular hallmarks of selection have been found for the genes in question, a corresponding ecological scenario of adaptive evolution has been difficult to prove. Ecological strategies, including reciprocal transplant experiments and competition experiments, and utilizing near-isogenic lines of alleles of interest will be a powerful tool to measure the relative fitness of phenotypic and/or allelic variants. CONCLUSIONS: As the plant model organism, Arabidopsis provides a wealth of molecular background information for evolutionary genetics. Because genetic diversity between and within Arabidopsis populations is much higher than anticipated, combining this background information with ecological approaches might well establish Arabidopsis as a model organism for plant evolutionary ecology.     

Books for free? How can this be?— A PubMed resource you may be overlooking

The NCBI (National Center for Biotechnology Information) at the National Institutes of Health collects a wide range of molecular biological data, and develops tools and databases to analyse and disseminate this information. Many life scientists are familiar with the website maintained by the NCBI (http://www.ncbi.nlm.nih.gov), because they use it to search GenBank for homologues of their genes of interest or to search the PubMed database for scientific literature of interest. There is also a database called the Bookshelf that includes searchable popular life science textbooks, medical and research reference books and NCBI reference materials. The Bookshelf can be useful for researchers and educators to find basic biological information. This article includes a representative list of the resources currently available on the Bookshelf, as well as instructions on how to access the information in these resources.     

Functional screening revisited in the postgenomic era

Functional screening can reveal a hidden function of a gene. cDNA library-based functional screening has flourished in various fields of biology so far, such as cancer biology, developmental biology and neuroscience. In the postgenomic era, however, various sequence database and public full-length cDNA resources are available, which now allow us to perform more straightforward, gene-oriented screening. Furthermore, the advent of RNA interference techniques has made it possible to perform effective loss-of-function screening. Gene-based functional screening is able to bridge the gap between genes and biological phenomena and raise important biological questions which should be tackled by integration of 'omic' datasets. These possible roles of functional screening will become more and more important in modern molecular biology moving toward the system level understanding of living organisms.