利用C++ Builder实现肿瘤遗传信息数据库的查询和利用

利用C＋＋ Builder,Microsoft SQL Server 2000数据库和ADO控件构建了肿瘤遗传信息的查询系统。先在数据库中建立肿瘤的生物信息表,通过C＋＋ Builder编写代码,添加ADO控件及其属性设置实现数据库的访问和信息查询。     

四川省生物多样性基础数据库信息系统

生物多样性本底信息是开展生物多样性评价的基础,现有的生物多样性数据分布比较分散,不同的数据掌握在不同的部门,没有得到很好整合。同时,由于缺乏物种分布的空间信息,生物多样性数据很难满足环境影响评价中生物多样性评价的需求,生物多样性评价一般只限于简单的定性分析。为了促使生物多样性评价工作能更深入的开展,切实有效地保护生物多样性资源,以四川省(重点以甘孜州)为研究案例,整合了现有的生物多样性数据资源,主要包括四川省自然保护区生物多样性数据、甘孜州水生和陆生生物多样性数据、四川省环境敏感区数据以及生物多样性现场调查数据等,建立了四川省生物多样性基础数据库,同时通过数据库开发和网络开发,建立了数据库信息系统,实现了生物多样性空间数据库和属性数据库的查询检索、数据显示等功能。     

基因转录调控相关数据库集成系统及其应用

通过互联网访问的有关基因转录调控的数据库集成系统及其应用 ,包括调控区 (3’和 5’调控区、内显子和外显子调控区等 )、调控单元 (启动子 ,增强子 ,沉默子等 )和转录因子结合位点相关数据库及其数据库系统的性质、组成和功能。也介绍了这些数据库和系统的查询和搜索方法以及相关开发的程序工具。这些生物信息学资源对于从事生物信息学、分子生物学、遗传工程、基因功能、生物技术、代谢工程、药物设计、病理学和药理学研究的机构及人员在教学研究方面具一定的参考价值和帮助。     

园艺植物分子育种相关生物信息资源及其应用

园艺植物分子育种中, 生物信息技术是一项新技术。GenBank、EMBL、DDBJ、Swiss-Prot等数据库及其序列查询系统、序列比对软件和序列提交软件是园艺植物分子育种中的重要生物信息资源。本文综述了这些生物信息资源, 以及它们在克隆新基因、预测新序列功能、鉴定种质资源和进行系谱分析等方面的应用。     

园艺植物分子育种相关生物信息资源及其应用

园艺植物分子育种中,生物信息技术是一项新技术.GenBank、EMBL、DDBJ、Swiss-Prot等数据库及其序列查询系统、序列比对软件和序列提交软件是园艺植物分子育种中的重要生物信息资源.本文综述了这些生物信息资源,以及它们在克隆新基因、预测新序列功能、鉴定种质资源和进行系谱分析等方面的应用.     

北京市高等植物种质资源信息查询系统的构建与特点

生物多样性信息学是生物多样性研究的重要内容,信息平台是生物多样性保护、生态教育和普及有效模式.目前,北京开展了大量的生物多样性资源的调查,但还缺少针对该区域系统而全面的物种资源信息化平台.针对北京地区生物多样性保护和生态教育的需要,基于2007～2009年开展的北京市高等植物种质资源全面系统的调查以及以往的研究数据,以物种多样性编目的数据库为框架,以计算机网络技术为支撑,构建北京市种质资源信息查询网络平台.该平台实现了高等植物种质资源信息的共享,用户可以准确查询到149科,61属,256种及变种的详细文字信息和图片信息,包括植物物种的分类、生态学特征、分布特点、濒危与保护状况、利用与人为干扰等.该平台的研建不仅为北京市植物物种资源的保护规划、外来物种的管理、资源利用等提供了重要信息支持,对科学研究和生态教育等也具有重要意义.     

我国建立的乙型肝炎病毒生物数据库供使用（http：//www.scbit.org/hbv）

根据研究乙型肝炎病毒（HBV）研究需要,建立了HBV二级数据库,希望方便针对病毒的生物信息学研究,并促进对HBV生物学特性的研究。为此,教育部医学分子病毒学开放实验室与上海生物信息技术中心联合构建了Bio-HBV生物数据库。     

中华民族基因组多态性数据库的构建

为配合总体的实验研究构建了中华民族基因组多态性(Genomic Polymorphism of Chinese Ethnic Groups,简称GPCEG)数据库,现已初步建成包括民族名称、基本情况介绍、体态特征、基因多态性数据、永生细胞株系、参考文献、国际相关数据库连接等内容的数据库,并完成了其可视化浏览及查询系统的建立,为建成具有中国特色的国家自有数据库奠定了基础,也可为从事相关研究的科学工作者提供信息服务。     

生物多样性信息学研究进展

生物多样性信息学是一门蓬勃发展的新学科。它将现代的信息技术带入生物多样性及其相关学科的研究领域。它在生物多样性基础数据的数字化、模型工具和各种工具软件的开发、数据整合, 以及全球、地区和国家尺度生物多样性信息网络等多个方面的发展, 向我们展示了未来在全球范围内自由、免费共享生物多样性数据和信息, 以及人们行动起来共同关注、调查与监测野外生物多样性的前景。目前, 已有大量数字化的物种编目、标本馆标本、多媒体影像、研究文献等生物多样性基础信息可以通过互联网检索和利用。其中, 最值得关注的是一些成功的国际性研究项目, 如物种2000、全球生物多样性信息网络、生命条形码以及网络生命大百科全书。这些项目的成功不仅体现在对大量基础信息和数据的发布, 而且它们通过与生物多样性信息标准TDWG(Biodiversity Information Standards: TDWG)的合作, 推动了达尔文核心标准(Darwin Core)等一些重要的生物多样性信息标准的应用, 以及地区和国家性生物多样性信息节点的建立, 这些都为将来全球范围生物多样性信息的共享和数据交换奠定了重要基础。在数字化信息的基础上, 研究人员也开发了一些在特定研究领域应用的数据挖掘和模型工具, 例如基于数字化标本的地理分布预测工具MAXENT, 分类学专家知识管理的LifeDesk。公民科学理念的发展则向我们展示了公众和科学爱好者广泛参与以互联网为基础的生物多样性信息学研究活动。因此, 生物多样性信息学的发展前景广阔, 它将为我们实现全球保护战略目标, 应对生物多样性危机, 解决全球气候变化条件下生物多样性资源管理和利用建立坚实的信息基础。     

基因表达谱微阵列网络数据库在肿瘤研究中的应用

基因表达谱微阵列数据库是一类可提供存储、查询、下载分析的在线网络数据库,在肿瘤相关领域的研究中提供了大量的数据来源。由于微阵列分析对于无生物/医学信息学专业背景的研究人员仍然有较多困难,致使该数据库的使用尚未普及。本文从数据查询、下载分析和使用方法等方面对常用基因表达谱微阵列数据库进行概述,并对现阶段基因表达微阵列数据库的应用策略进行总结,旨在帮助该领域研究的初学工作者了解数据库的基本知识并推动其在科研工作中的应用。     

Do Triplets Have Enough Information to Construct the Multi-Labeled Phylogenetic Tree?

The evolutionary history of certain species such as polyploids are modeled by a generalization of phylogenetic trees called multi-labeled phylogenetic trees, or MUL trees for short. One problem that relates to inferring a MUL tree is how to construct the smallest possible MUL tree that is consistent with a given set of rooted triplets, or SMRT problem for short. This problem is NP-hard. There is one algorithm for the SMRT problem which is exact and runs in time, where is the number of taxa. In this paper, we show that the SMRT does not seem to be an appropriate solution from the biological point of view. Indeed, we present a heuristic algorithm named MTRT for this problem and execute it on some real and simulated datasets. The results of MTRT show that triplets alone cannot provide enough information to infer the true MUL tree. So, it is inappropriate to infer a MUL tree using triplet information alone and considering the minimum number of duplications. Finally, we introduce some new problems which are more suitable from the biological point of view.     

Visualizations for taxonomic and phylogenetic trees

MOTIVATION: Despite substantial efforts to develop and populate the back-ends of biological databases, front-ends to these systems often rely on taxonomic expertise. This research applies techniques from human-computer interaction research to the biodiversity domain. RESULTS: We developed an interactive node-link tool, TaxonTree, illustrating the value of a carefully designed interaction model, animation, and integrated searching and browsing towards retrieval of biological names and other information. Users tested the tool using a new, large integrated dataset of animal names with phylogenetic-based and classification-based tree structures. These techniques also translated well for a tool, DoubleTree, to allow comparison of trees using coupled interaction. Our approaches will be useful not only for biological data but as general portal interfaces.     

Towards reconstructing a metabolic tree of life

Using information from several metabolic databases, we have built our own metabolic database containing 434 pathways and 1157 different enzymes. We have used this information to construct a dendrogram that demonstrates the metabolic similarities between 282 species. The resulting species distribution and the clusters defined in the tree show a certain taxonomic congruence, especially in recent relationships between species. This dendrogram is another representation of the tree of life, based on metabolism that may complement the trees constructed by other methods. For example, the metabolic dissimilarity we demonstrate between Symbiobacterium thermophilum (previously defined as Actinobacteria) and the other Actinobacteria species, and the metabolic similarity between S. thermophilum and Clostridia, combined with other evidence, suggest that S. thermophilum may be re-classified as Firmicutes, Clostridia.     

phyloXML: XML for evolutionary biology and comparative genomics

Background  

Evolutionary trees are central to a wide range of biological studies. In many of these studies, tree nodes and branches need to be associated (or annotated) with various attributes. For example, in studies concerned with organismal relationships, tree nodes are associated with taxonomic names, whereas tree branches have lengths and oftentimes support values. Gene trees used in comparative genomics or phylogenomics are usually annotated with taxonomic information, genome-related data, such as gene names and functional annotations, as well as events such as gene duplications, speciations, or exon shufflings, combined with information related to the evolutionary tree itself. The data standards currently used for evolutionary trees have limited capacities to incorporate such annotations of different data types.     

Analyzing and Synthesizing Phylogenies Using Tree Alignment Graphs

Phylogenetic trees are used to analyze and visualize evolution. However, trees can be imperfect datatypes when summarizing multiple trees. This is especially problematic when accommodating for biological phenomena such as horizontal gene transfer, incomplete lineage sorting, and hybridization, as well as topological conflict between datasets. Additionally, researchers may want to combine information from sets of trees that have partially overlapping taxon sets. To address the problem of analyzing sets of trees with conflicting relationships and partially overlapping taxon sets, we introduce methods for aligning, synthesizing and analyzing rooted phylogenetic trees within a graph, called a tree alignment graph (TAG). The TAG can be queried and analyzed to explore uncertainty and conflict. It can also be synthesized to construct trees, presenting an alternative to supertrees approaches. We demonstrate these methods with two empirical datasets. In order to explore uncertainty, we constructed a TAG of the bootstrap trees from the Angiosperm Tree of Life project. Analysis of the resulting graph demonstrates that areas of the dataset that are unresolved in majority-rule consensus tree analyses can be understood in more detail within the context of a graph structure, using measures incorporating node degree and adjacency support. As an exercise in synthesis (i.e., summarization of a TAG constructed from the alignment trees), we also construct a TAG consisting of the taxonomy and source trees from a recent comprehensive bird study. We synthesized this graph into a tree that can be reconstructed in a repeatable fashion and where the underlying source information can be updated. The methods presented here are tractable for large scale analyses and serve as a basis for an alternative to consensus tree and supertree methods. Furthermore, the exploration of these graphs can expose structures and patterns within the dataset that are otherwise difficult to observe.     

Clique-based data mining for related genes in a biomedical database

Background  

Progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, including diseases. There is thus a strong need for a way to automatically and comprehensively search from biomedical databases for related genes, such as genes in the same families and genes encoding components of the same pathways. Here we address the extraction of related genes by searching for densely-connected subgraphs, which are modeled as cliques, in a biomedical relational graph.     

Accurate Phylogenetic Tree Reconstruction from Quartets: A Heuristic Approach

Supertree methods construct trees on a set of taxa (species) combining many smaller trees on the overlapping subsets of the entire set of taxa. A ‘quartet’ is an unrooted tree over taxa, hence the quartet-based supertree methods combine many -taxon unrooted trees into a single and coherent tree over the complete set of taxa. Quartet-based phylogeny reconstruction methods have been receiving considerable attentions in the recent years. An accurate and efficient quartet-based method might be competitive with the current best phylogenetic tree reconstruction methods (such as maximum likelihood or Bayesian MCMC analyses), without being as computationally intensive. In this paper, we present a novel and highly accurate quartet-based phylogenetic tree reconstruction method. We performed an extensive experimental study to evaluate the accuracy and scalability of our approach on both simulated and biological datasets.     

Deep Phylogeny��How a Tree Can Help Characterize Early Life on Earth

The Darwinian concept of biological evolution assumes that life on Earth shares a common ancestor. The diversification of this common ancestor through speciation events and vertical transmission of genetic material implies that the classification of life can be illustrated in a tree-like manner, commonly referred to as the Tree of Life. This article describes features of the Tree of Life, such as how the tree has been both pruned and become bushier throughout the past century as our knowledge of biology has expanded. We present current views that the classification of life may be best illustrated as a ring or even a coral with tree-like characteristics. This article also discusses how the organization of the Tree of Life offers clues about ancient life on Earth. In particular, we focus on the environmental conditions and temperature history of Precambrian life and show how chemical, biological, and geological data can converge to better understand this history.

“You know, a tree is a tree.  How many more do you need to look at?”–Ronald Reagan (Governor of California), quoted in the Sacramento Bee, opposing expansion of Redwood National Park, March 3, 1966

The following article addresses a period in life most removed from life’s origins compared with other articles in this collection. The article discusses an advanced form of life that seems to have lived on the order of 3.5–4.0 billion years ago, around the time when life as we know it began to diversify in a Darwinian sense. The life from this geological period is located deep within an illustrated taxonomic tree of life. The hope is that by understanding how early life evolved, we can better understand how life originated. In this sense, the article attempts to travel backwards in time, starting from modern organisms, to understand life’s origin.The Darwinian concept of evolution suggests that all modern life shares a single common ancestor, often referred to as the last universal common ancestor (LUCA). Throughout evolutionary history, this ancestor has for the most part generated descendants as successive bifurcations in a tree-like manner. This so called Tree of Life, and phylogenetics in general provides much of the framework for the field of molecular evolution. Taxonomic trees allow us to better understand relationships and commonalities shared by life. For instance, a tree may tell us whether a trait or phenotype shared between two organisms is the result of shared-common ancestry (termed homologous traits) or whether the trait has evolved multiple times independent of ancestry (analogous traits such as wings).Taxonomic trees can be built using diverse sources of information. These can include morphological and phenotypic data at the macro-level down to DNA and protein sequence data at the micro-level. Ideally, trees built from multiple sources of input have identical taxonomic relationships and branching patterns, and such trees are said to be congruent. In practice, however, trees built from morphological data (say, presence or absence of wings) are often different than a tree built from molecular data (DNA or protein sequences). This requires the biologist to determine which of the two data sets is misleading and/or which taxonomic tree-building algorithm is most appropriate to use for a particular data set. Such an artform is common in the field of molecular evolution because rarely are trees congruent when built from two sources of input data.In light of this fact, we have provided the quote at the beginning of this article as a reflection about the field of molecular evolution and its interpretations of taxonomic trees. Although Reagan was not speaking about taxonomic trees in his quote, the same sort of disconnect exists between evolutionary biologists and molecular biologists (Woese and Goldenfeld 2009), as it did between conservationists and Ronald Reagan. A molecular biologist may be inclined to say that once you have seen one phylogenetic tree, you have seen them all. And in fairness, there is some validity to such a notion because historically a phylogenetic tree could not help a molecular biologist to better describe their system. An evolutionary biologist, however, will argue that individual trees have nuances that can dramatically alter our interpretation of evolutionary processes.We intend to show in this article that not all (taxonomic) trees look similar and describe identical evolutionary scenarios. We will discuss how our concept of the Tree of Life has changed over the past couple of decades, how trees can be interpreted, and what a tree can tell us about early life. In particular, the article will focus on the temperature conditions of early life because this topic has received much attention over the past few years as a direct result of improved DNA sequencing technology and a better understanding of molecular evolutionary processes. We will also describe how trees can be used to guide laboratory experiments in our attempt to understand ancient life. Lastly, we will discuss how phylogenetic trees will serve as the foundation for an “evolutionary synthetic biology” that should allow us to better understand the evolution of cellular pathways, macromolecular machines such as the ribosome, and other emergent properties of early life.     

Self-organizing tree-growing network for the classification of protein sequences.

The self-organizing tree algorithm (SOTA) was recently introduced to construct phylogenetic trees from biological sequences, based on the principles of Kohonen''s self-organizing maps and on Fritzke''s growing cell structures. SOTA is designed in such a way that the generation of new nodes can be stopped when the sequences assigned to a node are already above a certain similarity threshold. In this way a phylogenetic tree resolved at a high taxonomic level can be obtained. This capability is especially useful to classify sets of diversified sequences. SOTA was originally designed to analyze pre-aligned sequences. It is now adapted to be able to analyze patterns associated to the frequency of residues along a sequence, such as protein dipeptide composition and other n-gram compositions. In this work we show that the algorithm applied to these data is able to not only successfully construct phylogenetic trees of protein families, such as cytochrome c, triosephophate isomerase, and hemoglobin alpha chains, but also classify very diversified sequence data sets, such as a mixture of interleukins and their receptors.