PROPHET--a national computing resource for life science research

PROPHET is a national computing resource tailored to meet the data management and analysis needs of life scientists working in a wide variety of disciplines, ranging from pharmacology to molecular biology. The PROPHET system offers a fully integrated graphics-oriented environment designed for the manipulation and analysis of tabular data, graphs, molecular structures, biological simulation models, and protein and nucleic acid sequences, and it includes access to molecular structure and sequence databases.     

Of truth and pathways: chasing bits of information through myriads of articles

Knowledge on interactions between molecules in living cells is indispensable for theoretical analysis and practical applications in modern genomics and molecular biology. Building such networks relies on the assumption that the correct molecular interactions are known or can be identified by reading a few research articles. However, this assumption does not necessarily hold, as truth is rather an emerging property based on many potentially conflicting facts. This paper explores the processes of knowledge generation and publishing in the molecular biology literature using modelling and analysis of real molecular interaction data. The data analysed in this article were automatically extracted from 50000 research articles in molecular biology using a computer system called GeneWays containing a natural language processing module. The paper indicates that truthfulness of statements is associated in the minds of scientists with the relative importance (connectedness) of substances under study, revealing a potential selection bias in the reporting of research results. Aiming at understanding the statistical properties of the life cycle of biological facts reported in research articles, we formulate a stochastic model describing generation and propagation of knowledge about molecular interactions through scientific publications. We hope that in the future such a model can be useful for automatically producing consensus views of molecular interaction data.     

Genetic identification of biological species in theSaccharomyces sensu stricto complex

Studies on taxonomic and evolutionary genetics of theSaccharomyces sensu stricto complex are considered in light of the biological species concept. Genetic variability of some physiological properties traditionally used in yeast taxonomy is discussed. Genetic hybridization analysis and molecular karyotyping revealed six biological species in theSaccharomyces sensu stricto complex. DNA-DNA reassociation data are concordant with the data obtained by genetic analysis. A new system for naming the cultivatedSaccharomyces yeast (groups of cultivars) is proposed.This paper is dedicated to Danish scientists Ö Winge and V Jensen in recognition of their contributions to zymology.     

Scanning molecular sieve chromatography of interacting protein systems: nonlinear least-squares analysis of small zone experiments

A simple routine for nonlinear least-squares analysis is applied to small zone scanning data, where calibration of the gel column requires the use of fully characterized markers of known molecular size. Application of nonlinear least-squares analysis eliminates the difficulty encountered because of scarcity of calibrating markers for gels whose porosities span certain size ranges by providing a sensitive measure of changes in molecular size of the interacting protein system, in order to determine the interaction parameters, size heterogeneity, and the centroid position for time difference chromatographic experiments.     

A practical guide to methods of parentage analysis

The use of molecular techniques for parentage analysis has been a booming science for over a decade. The most important technological breakthrough was the introduction of microsatellite markers to molecular ecology, an advance that was accompanied by a proliferation and refinement of statistical techniques for the analysis of parentage data. Over the last several years, we have seen steady progress in a number of areas related to parentage analysis, and the prospects for successful studies continue to improve. Here, we provide an updated guide for scientists interested in embarking on parentage analysis in natural or artificial populations of organisms, with a particular focus on computer software packages that implement various methods of analysis. Our survey of the literature shows that there are a few established methods that perform extremely well in the analysis of most types of parentage studies. However, particular experimental designs or study systems can benefit from some of the less well-known computer packages available. Overall, we find that parentage analysis is feasible and satisfying in most systems, and we try to provide a simple roadmap to help other scientists navigate the confusing topography of statistical techniques.     

Studying the functional genomics of stress responses in loblolly pine with the Expresso microarray experiment management system

Conception, design, and implementation of cDNA microarray experiments present a variety of bioinformatics challenges for biologists and computational scientists. The multiple stages of data acquisition and analysis have motivated the design of Expresso, a system for microarray experiment management. Salient aspects of Expresso include support for clone replication and randomized placement; automatic gridding, extraction of expression data from each spot, and quality monitoring; flexible methods of combining data from individual spots into information about clones and functional categories; and the use of inductive logic programming for higher-level data analysis and mining. The development of Expresso is occurring in parallel with several generations of microarray experiments aimed at elucidating genomic responses to drought stress in loblolly pine seedlings. The current experimental design incorporates 384 pine cDNAs replicated and randomly placed in two specific microarray layouts. We describe the design of Expresso as well as results of analysis with Expresso that suggest the importance of molecular chaperones and membrane transport proteins in mechanisms conferring successful adaptation to long-term drought stress.     

基因网络研究进展

分子生物学的深入发展揭示了复杂的生命现象是大量基因相互作用的结果,传统的以描述为主的生物学和分解分析的研究方法受到挑战.随着DNA芯片和分子阵列技术的应用,快速检测生物基因组的表达已成为可能.在生命科学领域,基因网络作为一种系统的、定量的研究方法正在受到重视,该方法建立在分子生物学、非线性数学和信息学等多学科交叉的基础上.基因网络是动力系统模型,具有稳定性、层次性等一系列非线性系统的特性.通过基因表达的大量数据,结合一定的分析和计算方法可以构建合适的基因网络拓扑结构模拟系统的行为.反过来,利用已建立的基因网络可以指导进一步的实验.计算机工具和Internet资源是基因网络研究的重要手段.基因网络研究将在后基因组研究中发挥重要的作用.     

The Next Decade of Big Data in Ecosystem Science

Ecosystem scientists will increasingly be called on to inform forecasts and define uncertainty about how changing planet conditions affect human well-being. We should be prepared to leverage the best tools available, including big data. Use of the term ‘big data’ implies an approach that includes capacity to aggregate, search, cross-reference, and mine large volumes of data to generate new understanding that can inform decision-making about emergent properties of complex systems. Although big-data approaches are not a panacea, there are large-scale environmental questions for which big data are well suited, even necessary. Ecosystems are complex biophysical systems that are not easily defined by any one data type, location, or time. Understanding complex ecosystem properties is data intensive along axes of volume (size of data), velocity (frequency of data), and variety (diversity of data types). Ecosystem scientists have employed impressive technology for generating high-frequency, large-volume data streams. Yet important challenges remain in both theoretical and infrastructural development to support visualization and analysis of large and diverse data. The way forward includes greater support for network science approaches, and for development of big-data infrastructure that includes capacity for visualization and analysis of integrated data products. Likewise, a new paradigm of cross-disciplinary training and professional evaluation is needed to increase the human capital to fully exploit big-data analytics in a way that is sustainable and adaptable to emerging disciplinary needs.     

SFMN GeoSearch: An interactive approach to the visualization and exchange of point-based ecological data

Recent advances in computer networks and information technologies have created exciting new possibilities for sharing and analyzing scientific research data. Although individual datasets can be studied efficiently, many scientists are still largely limited to considering data collected by themselves, their students, or closely affiliated research groups. Increasingly widespread high-speed network connections and the existence of large, coordinated research programs suggest the potential for scientists to access and learn from data from outside their immediate research circle. We are developing a web-based application that facilitates the sharing of scientific data within a research network using the now-common “virtual globe” in combination with advanced visualization methods designed for geographically distributed scientific data. Two major components of the system enable the rapid assessment of geographically distributed scientific data: a database built from information submitted by network members, and a module featuring novel and sophisticated geographic data visualization techniques. By enabling scientists to share results with each other and view their shared data through a common virtual-globe interface, the system provides a new platform for important meta-analyses and the analysis of broad-scale patterns. Here we present the design and capabilities of the SFMN GeoSearch platform for the Sustainable Forest Management Network, a pan-Canadian network of forest researchers who have accumulated data for more than a decade. Through the development and dissemination of this new tool, we hope to help scientists, students, and the general public to understand the depth and breadth of scientific data across potentially large areas.     

Molecular modelling and simulations in cancer research

The complexity of cancer and the vast amount of experimental data available have made computer-aided approaches necessary. Biomolecular modelling techniques are becoming increasingly easier to use, whereas hardware and software are becoming better and cheaper. Cross-talk between theoretical and experimental scientists dealing with cancer-research from a molecular approach, however, is still uncommon. This is in contrast to other fields, such as amyloid-related diseases, where molecular modelling studies are widely acknowledged. The aim of this review paper is therefore to expose some of the more common approaches in molecular modelling to cancer scientists in simple terms, illustrating success stories while also revealing the limitations of computational studies at the molecular level.     

Collecting and harvesting biological data: the GPCRDB and NucleaRDB information systems

The amount of genomic and proteomic data that is entered each day into databases and the experimental literature is outstripping the ability of experimental scientists to keep pace. While generic databases derived from automated curation efforts are useful, most biological scientists tend to focus on a class or family of molecules and their biological impact. Consequently, there is a need for molecular class-specific or other specialized databases. Such databases collect and organize data around a single topic or class of molecules. If curated well, such systems are extremely useful as they allow experimental scientists to obtain a large portion of the available data most relevant to their needs from a single source. We are involved in the development of two such databases with substantial pharmacological relevance. These are the GPCRDB and NucleaRDB information systems, which collect and disseminate data related to G protein-coupled receptors and intra-nuclear hormone receptors, respectively. The GPCRDB was a pilot project aimed at building a generic molecular class-specific database capable of dealing with highly heterogeneous data. A first version of the GPCRDB project has been completed and it is routinely used by thousands of scientists. The NucleaRDB was started recently as an application of the concept for the generalization of this technology. The GPCRDB is available via the WWW at http://www.gpcr.org/7tm/ and the NucleaRDB at http://www.receptors.org/NR/.     

Applications of genetic programming in cancer research

The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in the last century, computer scientists began adapting its principles, in particular natural selection, to complex computational challenges, leading to the emergence of evolutionary algorithms. The conceptual model of selective pressure and recombination in evolutionary algorithms allow scientists to efficiently search high dimensional space for solutions to complex problems. In the last decade, genetic programming has been developed and extensively applied for analysis of molecular data to classify cancer subtypes and characterize the mechanisms of cancer pathogenesis and development. This article reviews current successes using genetic programming and discusses its potential impact in cancer research and treatment in the near future.     

基于单核苷酸多态性的基因互作分析方法学进展

基于单核苷酸多态性的关联分析已成为当前解析人类常见复杂疾病遗传机制的重要手段之一, 然而, 目前普遍使用的单位点分析策略仅能发现部分单独效应显著的易感SNP位点, 因此遗漏了重要的遗传力组分——基因上位效应或联合效应。识别全基因组多基因间复杂的互作关系已成为全面解析复杂疾病致病分子机制必不可少的一项任务。已有很多方法被应用于全基因组交互作用分析, 加深了人类对复杂疾病遗传机制的进一步认识。基于各类方法的理论基础及算法的异同, 文章对目前应用较为广泛的基于遗传互作模型的方法、不基于互作模型的方法和数据挖掘类算法3类方法进行了系统地评述, 着重介绍了这些方法的主要思想、实现过程及应用中的注意事项等, 并指出开展大规模全基因组范围互作检测面临的问题, 以期能为相关领域的研究者提供方法学参考。     

Training in molecular cytopathology testing

Training in molecular cytopathology testing is essential in developing and maintaining skills in modern molecular technologies as they are introduced to a universal health care system such as extant in the UK and elsewhere. We review the system in place in Northern Ireland (NI) for molecular testing of solid tumours, as an example to train staff of all grades, including pathologists, clinical scientists, biomedical scientists and equivalent technical grades. We describe training of pathologists as part of the NI Deanery medical curriculum, the NI training programme for scientists and laboratory rotation for Biomedical Scientists. Collectively, the aims of our training are two‐fold: to provide a means by which individuals may extend their experience and skills; and to provide and maintain a skilled workforce for service delivery. Through training and competency, we introduce new technologies and tests in response to personalised medicine therapies with a competent workforce. We advocate modifying programmes to suit individual needs for skill development, with formalised courses in pre‐analytical, analytical and postanalytical demands of modern molecular pathology. This is of particular relevance for cytopathology in small samples such those from formalin‐fixed paraffin‐embedded cell blocks. We finally introduce how university courses can augment training and develop a skilled workforce to benefit the delivery of services to our patients.     

The Biological Connection Markup Language: a SBGN-compliant format for visualization, filtering and analysis of biological pathways

MOTIVATION: Many models and analysis of signaling pathways have been proposed. However, neither of them takes into account that a biological pathway is not a fixed system, but instead it depends on the organism, tissue and cell type as well as on physiological, pathological and experimental conditions. RESULTS: The Biological Connection Markup Language (BCML) is a format to describe, annotate and visualize pathways. BCML is able to store multiple information, permitting a selective view of the pathway as it exists and/or behave in specific organisms, tissues and cells. Furthermore, BCML can be automatically converted into data formats suitable for analysis and into a fully SBGN-compliant graphical representation, making it an important tool that can be used by both computational biologists and 'wet lab' scientists. Availability and implementation: The XML schema and the BCML software suite are freely available under the LGPL for download at http://bcml.dc-atlas.net. They are implemented in Java and supported on MS Windows, Linux and OS X.     

Domain-enhanced analysis of microarray data using GO annotations

MOTIVATION: New biological systems technologies give scientists the ability to measure thousands of bio-molecules including genes, proteins, lipids and metabolites. We use domain knowledge, e.g. the Gene Ontology, to guide analysis of such data. By focusing on domain-aggregated results at, say the molecular function level, increased interpretability is available to biological scientists beyond what is possible if results are presented at the gene level. RESULTS: We use a 'top-down' approach to perform domain aggregation by first combining gene expressions before testing for differentially expressed patterns. This is in contrast to the more standard 'bottom-up' approach, where genes are first tested individually then aggregated by domain knowledge. The benefits are greater sensitivity for detecting signals. Our method, domain-enhanced analysis (DEA) is assessed and compared to other methods using simulation studies and analysis of two publicly available leukemia data sets. AVAILABILITY: Our DEA method uses functions available in R (http://www.r-project.org/) and SAS (http://www.sas.com/). The two experimental data sets used in our analysis are available in R as Bioconductor packages, 'ALL' and 'golubEsets' (http://www.bioconductor.org/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Mapping microbial biodiversity

We report the development of a prototype database that "maps" microbial diversity in the context of the geochemical and geological environment and geographic location. When it is fully implemented, scientists will be able to conduct database searches, construct maps containing the information of interest, download files, and enter data over the Internet.     

The bacteriorhodopsin model membrane system as a prototype molecular computing element

The quest for more sophisticated integrated circuits to overcome the limitation of currently available silicon integrated circuits has led to the proposal of using biological molecules as computational elements by computer scientists and engineers. While the theoretical aspect of this possibility has been pursued by computer scientists, the research and development of experimental prototypes have not been pursued with an equal intensity. In this survey, we make an attempt to examine model membrane systems that incorporate the protein pigment bacteriorhodopsin which is found in Halobacterium halobium. This system was chosen for several reasons. The pigment/membrane system is sufficiently simple and stable for rigorous quantitative study, yet at the same time sufficiently complex in molecular structure to permit alteration of this structure in an attempt to manipulate the photosignal. Several methods of forming the pigment/membrane assembly are described and the potential application to biochip design is discussed. Experimental data using these membranes and measured by a tunable voltage clamp method are presented along with a theoretical analysis based on the Gouy-Chapman diffuse double layer theory to illustrate the usefulness of this approach. It is shown that detailed layouts of the pigment/membrane assembly as well as external loading conditions can modify the time course of the photosignal in a predictable manner. Some problems that may arise in the actual implementation and manufacturing, as well as the use of existing technology in protein chemistry, immunology, and recombinant DNA technology are discussed.     

L.S. Vygotsky: In Memoriam

On the night of 11 June, death took from the ranks of our country's outstanding scientists Professor Lev Semenovich Vygotsky. In him Soviet psychology has lost not only a great scientist and brilliant educator, not only a person with remarkable personal qualities, but also one of those people whose appearance in our science has been of tremendous importance for its development, whose life and death are as much a part of the history of psychological knowledge as of his personal biography. Indeed, the system of theoretical psychological ideas Vygotsky created requires an understanding of the biography of its creator to be fully grasped. Yet the opposite is also true: only analysis of this system can provide the real key to understanding the personality of the deceased.