国际生物信息学研究的可视化分析

利用当前主流的信息可视化分析软件Cite Space对2005~2014年间SCI收录的生物信息学的5种高影响力外文期刊所刊载论文的题录数据进行统计和可视化分析,绘制该领域的关键词共现、膨胀词共现、经典文献共现、高被引文献共现和关键节点文献共现的网络可视化图谱,试图揭示生物信息学领域的研究热点、研究前沿以及知识基础,以期帮助研究人员了解该领域在国际范围内的研究态势。     

Visualizing the knowledge structure and evolution of bioinformatics

Background

Bioinformatics has gained much attention as a fast growing interdisciplinary field. Several attempts have been conducted to explore the field of bioinformatics by bibliometric analysis, however, such works did not elucidate the role of visualization in analysis, nor focus on the relationship between sub-topics of bioinformatics.

Results

First, the hotspot of bioinformatics has moderately shifted from traditional molecular biology to omics research, and the computational method has also shifted from mathematical model to data mining and machine learning. Second, DNA-related topics are bridge topics in bioinformatics research. These topics gradually connect various sub-topics that are relatively independent at first. Third, only a small part of topics we have obtained involves a number of computational methods, and the other topics focus more on biological aspects. Fourth, the proportion of computing-related topics hit a trough in the 1980s. During this period, the use of traditional calculation methods such as mathematical model declined in a large proportion while the new calculation methods such as machine learning have not been applied in a large scale. This proportion began to increase gradually after the 1990s. Fifth, although the proportion of computing-related topics is only slightly higher than the original, the connection between other topics and computing-related topics has become closer, which means the support of computational methods is becoming increasingly important for the research of bioinformatics.

Conclusions

The results of our analysis imply that research on bioinformatics is becoming more diversified and the ranking of computational methods in bioinformatics research is also gradually improving.

     

植物microRNA的生物信息学预测与分析

microRNAs(miRNAs)是一类广泛存在于真核生物中调控基因转录后表达的非编码小分子RNA。大量研究表明,miRNA在调节多种生物途径中起着重要的作用,采用生物信息学方法预测与分析miRNA是当前发现和鉴定植物miRNA的重要策略之一。研究内容总结了生物信息学预测植物miRNA及其靶基因的方法策略,阐述了生物信息学在植物miRNA研究中的重要作用,为今后的研究奠定了基础。     

Promoting synergistic research and education in genomics and bioinformatics

Bioinformatics and Genomics are closely related disciplines that hold great promises for the advancement of research and development in complex biomedical systems, as well as public health, drug design, comparative genomics, personalized medicine and so on. Research and development in these two important areas are impacting the science and technology.High throughput sequencing and molecular imaging technologies marked the beginning of a new era for modern translational medicine and personalized healthcare. The impact of having the human sequence and personalized digital images in hand has also created tremendous demands of developing powerful supercomputing, statistical learning and artificial intelligence approaches to handle the massive bioinformatics and personalized healthcare data, which will obviously have a profound effect on how biomedical research will be conducted toward the improvement of human health and prolonging of human life in the future. The International Society of Intelligent Biological Medicine (http://www.isibm.org) and its official journals, the International Journal of Functional Informatics and Personalized Medicine (http://www.inderscience.com/ijfipm) and the International Journal of Computational Biology and Drug Design (http://www.inderscience.com/ijcbdd) in collaboration with International Conference on Bioinformatics and Computational Biology (Biocomp), touch tomorrow's bioinformatics and personalized medicine throughout today's efforts in promoting the research, education and awareness of the upcoming integrated inter/multidisciplinary field. The 2007 international conference on Bioinformatics and Computational Biology (BIOCOMP07) was held in Las Vegas, the United States of American on June 25-28, 2007. The conference attracted over 400 papers, covering broad research areas in the genomics, biomedicine and bioinformatics. The Biocomp 2007 provides a common platform for the cross fertilization of ideas, and to help shape knowledge and scientific achievements by bridging these two very important disciplines into an interactive and attractive forum. Keeping this objective in mind, Biocomp 2007 aims to promote interdisciplinary and multidisciplinary education and research. 25 high quality peer-reviewed papers were selected from 400+ submissions for this supplementary issue of BMC Genomics. Those papers contributed to a wide-range of important research fields including gene expression data analysis and applications, high-throughput genome mapping, sequence analysis, gene regulation, protein structure prediction, disease prediction by machine learning techniques, systems biology, database and biological software development. We always encourage participants submitting proposals for genomics sessions, special interest research sessions, workshops and tutorials to Professor Hamid R. Arabnia (hra@cs.uga.edu) in order to ensure that Biocomp continuously plays the leadership role in promoting inter/multidisciplinary research and education in the fields. Biocomp received top conference ranking with a high score of 0.95/1.00. Biocomp is academically co-sponsored by the International Society of Intelligent Biological Medicine and the Research Laboratories and Centers of Harvard University--Massachusetts Institute of Technology, Indiana University--Purdue University, Georgia Tech--Emory University, UIUC, UCLA, Columbia University, University of Texas at Austin and University of Iowa etc. Biocomp--Worldcomp brings leading scientists together across the nation and all over the world and aims to promote synergistic components such as keynote lectures, special interest sessions, workshops and tutorials in response to the advances of cutting-edge research.     

生物信息学预测与实验验证相结合策略筛选鉴定新的人分泌蛋白基因

使用生物信息学预测结合实验验证的策略筛选鉴定人新的分泌蛋白基因。用SignalP、SOSUI、PSORT和BLAST等程序对UniProt蛋白数据库进行生物信息学分析 ,筛选出用于实验验证的 1 4个功能未知基因。采用RT PCR方法 ,克隆得到 1 4个基因的全长编码序列 ,并构建到真核表达载体pcDNA3.1 ( - ) Myc His质粒。采用蛋白质印迹与免疫荧光分析 ,检测到其中 7个基因的表达。除其中一个在细胞核表达外 ,其余 6个只在细胞质中表达 ;其中的 4个基因的表达产物在细胞培养液中可被检测到 ,鉴定为 4个新的分泌蛋白基因。     

Predicting sRNAs and Their Targets in Bacteria

Bacterial small RNAs (sRNAs) are an emerging class of regulatory RNAs of about 40-500 nucleotides in length and, by binding to their target mRNAs or proteins, get involved in many biological processes such as sensing environmental changes and regulating gene expression. Thus, identification of bacterial sRNAs and their targets has become an important part of sRNA biology. Current strategies for discovery of sRNAs and their targets usually involve bioinformatics prediction followed by experimental validation, emphasizing a key role for bioinformatics prediction. Here, therefore, we provided an overview on prediction methods, focusing on the merits and limitations of each class of models. Finally, we will present our thinking on developing related bioinformatics models in future.     

Artificial intelligence techniques for bioinformatics

This review provides an overview of the ways in which techniques from artificial intelligence (AI) can be usefully employed in bioinformatics, both for modelling biological data and for making new discoveries. The paper covers three techniques: symbolic machine learning approaches (nearest neighbour and identification tree techniques), artificial neural networks and genetic algorithms. Each technique is introduced and supported with examples taken from the bioinformatics literature. These examples include folding prediction, viral protease cleavage prediction, classification, multiple sequence alignment and microarray gene expression analysis.     

共词分析国内外生物信息学领域研究态势

生物信息学作为自然科学领域中多学科交叉的新兴学科,其发展研究得到了众多学者的关注。为了解生物信息学在国内外的研究态势,以CNKI中文数据库和Web of science外文数据库中生物信息学领域期刊论文为研究对象,利用R语言编程工具,文献计量和共词分析归纳了国内外生物信息学领域的研究现状、热点及趋势。结果表明:国内外生物信息学研究均处于高速发展期,文献量呈逐年增长趋势,研究领域也在不断拓宽;国内外研究热点均聚焦在基因挖掘、蛋白质结构与功能预测、miRNA分析等,但国内偏向于理论研究,而国际更注重其在疾病治疗、药物设计等方面的应用研究。     

Discovering functions and revealing mechanisms at molecular level from biological networks

With the increasingly accumulated data from high-throughput technologies, study on biomolecular networks has become one of key focuses in systems biology and bioinformatics. In particular, various types of molecular networks (e.g., protein-protein interaction (PPI) network; gene regulatory network (GRN); metabolic network (MN); gene coexpression network (GCEN)) have been extensively investigated, and those studies demonstrate great potentials to discover basic functions and to reveal essential mechanisms for various biological phenomena, by understanding biological systems not at individual component level but at a system-wide level. Recent studies on networks have created very prolific researches on many aspects of living organisms. In this paper, we aim to review the recent developments on topics related to molecular networks in a comprehensive manner, with the special emphasis on the computational aspect. The contents of the survey cover global topological properties and local structural characteristics, network motifs, network comparison and query, detection of functional modules and network motifs, function prediction from network analysis, inferring molecular networks from biological data as well as representative databases and software tools.     

Evolving research trends in bioinformatics

The cross-disciplinary nature of bioinformatics entails co-evolution with other biomedical disciplines, whereby some bioinformatics applications become popular in certain disciplines and, in turn, these disciplines influence the focus of future bioinformatics development efforts. We observe here that the growth of computational approaches within various biomedical disciplines is not merely a reflection of a general extended usage of computers and the Internet, but due to the production of useful bioinformatics databases and methods for the rest of the biomedical scientific community. We have used the abstracts stored both in the MEDLINE database of biomedical literature and in NIH-funded project grants, to quantify two effects. First, we examine the biomedical literature as a whole and find that the use of computational methods has become increasingly prevalent across biomedical disciplines over the past three decades, while use of databases and the Internet have been rapidly increasing over the past decade. Second, we study the recent trends in the use of bioinformatics topics. We observe that molecular sequence databases are a widely adopted contribution in biomedicine from the field of bioinformatics, and that microarray analysis is one of the major new topics engaged by the bioinformatics community. Via this analysis, we were able to identify areas of rapid growth in the use of informatics to aid in curriculum planning, development of computational infrastructure and strategies for workforce education and funding.     

Gene structure identification with MyGV using cDNA evidence and protein homologs to improve ab initio predictions

MyGV is an application to visualize (potentially genome-scale) gene structure annotation and prediction. The output of any external gene prediction program can be easily converted to a generalized format for input into MyGV. The application displays all input simultaneously in graphical representation, with a toggle option for a text-based view. Zooming capabilities allow detailed comparisons for specific genome locations. The tool is particularly helpful for refinement of ab initio predicted gene structures by spliced alignment with cDNA or protein homologs. AVAILABILITY: The program was written in Java and is freely available to non-commercial users by electronic download from http://bioinformatics.iastate.edu/bioinformatics2go/MyGV.     

Impact of RNA structure on the prediction of donor and acceptor splice sites

Background  

gene identification in genomic DNA sequences by computational methods has become an important task in bioinformatics and computational gene prediction tools are now essential components of every genome sequencing project. Prediction of splice sites is a key step of all gene structural prediction algorithms.     

数据挖掘在生物信息学中的应用——文献计量学视角

基因组和蛋白质结构与功能方面已积累了海量数据。如何从海量数据中获取有效信息成为生物信息学迫切要解决的问题。本文以相关主题词检索文献,分析了该领域历年文章数量、发文最多的机构和作者、被引用频次居前论文、期刊载文量,并对关键词和被引用频次居前论文的作者进行共现分析。我们发现,生物信息学中运用数据挖掘方法的文献逐年增多,该领域30.1%的文献发表在十个期刊上,分类、聚类、特征选择和支持向量机等数据挖掘方法使用较多。本研究描绘了生物信息学与数据挖掘这一交叉领域的研究概况,为后续数据挖掘方法与生物信息学研究相结合提供帮助。     

EMD: an ensemble algorithm for discovering regulatory motifs in DNA sequences

Background  

Understanding gene regulatory networks has become one of the central research problems in bioinformatics. More than thirty algorithms have been proposed to identify DNA regulatory sites during the past thirty years. However, the prediction accuracy of these algorithms is still quite low. Ensemble algorithms have emerged as an effective strategy in bioinformatics for improving the prediction accuracy by exploiting the synergetic prediction capability of multiple algorithms.     

麻风树LEC1基因的生物信息学分析

LEC1是调控植物种子发育过程和油脂产量的重要基因。本文选择功能已知的拟南芥LEC1基因为参考序列,对麻风树LEC1基因开展了蛋白组分和理化性质分析,蛋白结构和功能预测,系统进化分析等生物信息学研究,为进一步研究麻风树LEC1基因的生物学功能奠定了基础。     

高粱LEA基因家族的鉴定及表达分析

有研究表明,干旱、低温和盐等环境胁迫能够诱导LEA基因的表达。为了探索LEA基因家族在高粱响应外界刺激过程中起到的作用,本研究通过生物信息学的方法对LEA基因家族在高粱全基因组水平进行鉴定和分析,于高粱全基因组中共鉴定出35个基因家族成员,不均匀地分布于高粱8条染色体上,结合系统进化树和保守结构域分析结果,将高粱LEA基因家族成员分为7组。亲水性分析和结构无序性预测表明高粱LEA蛋白绝大多数为亲水性且结构无序。基因结构分析显示了各分组基因结构上的保守性。高粱LEA基因的启动子分析发现了一些与激素和非生物胁迫响应相关的顺式作用元件。对激素和干旱胁迫下高粱LEA基因的表达分析发现外界胁迫能够诱导部分高粱LEA基因的表达。     

Computational studies of membrane proteins: Models and predictions for biological understanding

We discuss recent progresses in computational studies of membrane proteins based on physical models with parameters derived from bioinformatics analysis. We describe computational identification of membrane proteins and prediction of their topology from sequence, discovery of sequence and spatial motifs, and implications of these discoveries. The detection of evolutionary signal for understanding the substitution pattern of residues in the TM segments and for sequence alignment is also discussed. We further discuss empirical potential functions for energetics of inserting residues in the TM domain, for interactions between TM helices or strands, and their applications in predicting lipid-facing surfaces of the TM domain. Recent progresses in structure predictions of membrane proteins are also reviewed, with further discussions on calculation of ensemble properties such as melting temperature based on simplified state space model. Additional topics include prediction of oligomerization state of membrane proteins, identification of the interfaces for protein-protein interactions, and design of membrane proteins. This article is part of a Special Issue entitled: Protein Folding in Membranes.     

Function and structure of inherently disordered proteins

The application of bioinformatics methodologies to proteins inherently lacking 3D structure has brought increased attention to these macromolecules. Here topics concerning these proteins are discussed, including their prediction from amino acid sequence, their enrichment in eukaryotes compared to prokaryotes, their more rapid evolution compared to structured proteins, their organization into specific groups, their structural preferences, their half-lives in cells, their contributions to signaling diversity (via high contents of multiple-partner binding sites, post-translational modifications, and alternative splicing), their distinct functional repertoire compared to that of structured proteins, and their involvement in diseases.