学习科学相关基因数据库的构建

利用生物信息学方法和技术，收集、整理、集成分散在多个数据库和文献中与学习科学相关的基因信息，建立与学习科学相关的基因数据库。数据库提供基因数据管理和查询功能。数据库的管理，采用在线管理方式，可向数据库添加、修改、删除记录，及时、有效地管理数据库。在数据库查询方面，提供关键词查询方式、染色体浏览查询方式、基因列表浏览查询方式和行为特征分类浏览查询方式。     

遗传印迹与疾病

印迹基因的调节失衡在很多疾病中都观察到,如Beckwith-Wiedemann综合征,Prader-Willi/Angelman综合征和肿瘤, 印迹疾病以复杂突变型式为特征,相关的表型有影响出生前和出生后生长的表型以及影响神经功能的表型, 印迹基因表达调节是由基因座位特异性DNA和染色质的表观修饰介导,这些修饰优先影响中央调节元件,这些元件顺式控制长距离附近几个基因座位特异性表达, 印迹基因疾病的研究对生物医学研究有重要影响,也为表观遗传基因控制的功能和机理研究提供了一个有趣的模型.     

胎儿骨骼发育异常的遗传因素研究进展

胎儿骨骼发育异常(skeletal dysplasia,SD)是胎儿期最常见的出生缺陷之一,绝大多数与遗传性疾病相关,骨骼系统病种较多,具有遗传异质性和表型异质性。导致骨骼发育异常的遗传因素可以分为:染色体异常、染色体微缺失微重复综合征和致病基因相关的遗传性骨病。基因测序技术的快速发展为胎儿骨骼发育异常提供了新的研究策略,随着各种遗传性骨病的相应基因型-超声影像及临床表型信息的逐步完善,提高了骨骼发育异常的胎儿产前诊断的效率和准确率。让建立更加高效的、标准化的诊断流程成为可能,本文就胎儿骨骼发育异常的遗传性因素研究进展进行综述。     

TAP1基因和FcεRIβ基因多态性与哮喘及其表型的相关性研究

目的：为研究TAP1基因和FcεRIβ基因多态性与哮喘及其表型的关系。方法：选择TAP1基因中微卫星DNA标志-TAP1及FcεRIβ基因中RFLP位点-RsaI,采用Amp-FLP和RFLP方法在散哮喘病人中进行分析。并检测其血清IgE水平,过敏原发皮试及气道高反应性,应用相关分析观察TAP1及RsaI等位基因与哮喘及其表型的关系。结果：RsaI位点等位基因片段与哮喘及气道高反应性相关,未见TAP1与哮喘或其表型相关。结论：FcεRIβ基因可能是哮喘发病的一个候选基因,TAP1基因可能与哮喘的发病无关。     

新闻点击

美国宣布开放全基因组研究数据库据美国BIOCOMPARE科技新闻网(2006／12／19)报道，美国国家医学图书馆于近日宣布开放一个全基因组范围(genome wideassociation，GWA)內的全新数据库“基因型与表型数据库(the database of Genotype and Phenotype，dbGaP)，能够于第一时间提供给研究者所有基因之间相关与否的研究资料。这项由美国国家卫生研究院主导的GWA研究计划，主要探讨特定基因型与表型之间的关联，例如血压与体重之间与是否导致某些疾病等关系；这对于了解基因，以及发展治疗法与策略十分重要。隶属于国家生物信息中心的dbGa…     

肿瘤转移过程中相关基因的研究进展

肿瘤细胞转移相关基因的激活和/或转移抑制相关基因的失活均可诱发肿瘤细胞转移表型而导致转移的发生。肿瘤细胞成瘤性和转移性分别受“转移相关基因”和“转移抑制相关基因”的调控。本文就肿瘤转移的细胞学基础、肿瘤转移相关基因的研究及肿瘤转移抑制相关基因的研究进行了综述。     

肺癌相关基因克隆策略与进展

肺癌和其它肿瘤一样是遗传和环境等诸多因素的相互作用所致 ,涉及到大量相关基因结构和表达调控的改变。寻找肺癌致病相关基因是阐明肺癌分子发病机制的关键。基因识别的基础在于其表型 ,但对于肺癌来说 ,其表型是遗传因素与环境因素相互作用的复杂表现 ,无法运用功能克隆法来寻找其相关基因。目前大多采用的方法和策略有定位克隆法、定位候选克隆法和差异筛选法     

调控基因KLF1变异在遗传性血液病中的研究进展

KLF1是调节红细胞生成以及成人β-珠蛋白基因的表达的关键调控基因。参与激活、调节多个血型抗原表达、珠蛋白基因表达和转换、细胞周期、酶代谢、结构蛋白、血红素合成等相关基因及转录因子,并与这些转录因子在红系调控网络中协同工作,共同参与红系的基因表达。在高通量测序广泛应用以前,KLF1变异一直被认为是及其罕见的。事实上在许多独立的红细胞疾病中都发现了KLF1的突变。这些患者表型因突变类型的不同而各有差异。同时KLF1突变的流行区域也与血红蛋白疾病有一定的重叠。本文将从KLF1结构、功能、突变类型、表型、流行趋势等多个方面探讨其在红细胞疾病中的作用。为关键细胞主要调控因子的变异在一些迄今为止无法解释的遗传疾病中的作用提供一个范例。     

表型克隆：复杂性状相关基因的分离策略

介绍了表型克隆概念和复杂性状相关基因克隆的主要策略．表型克隆直接依据表型与基因组序列或ｍＲＮＡ表达序列的联系来克隆基因而不必事先分析其生化功能或连锁、定位．开辟了一条分离复杂性状相关基因的快捷可行的途径，大大增强了分离致病基因的能力和速度。它可分为两大类；从基因组序列特征入手的基因组筛选策略，主要包括错配筛选、代表性差异分析等技术，它们分别着眼于两基因组间与表型相关的全同等序列和差异序列；从ｍＲＮＡ表达特征入手的ｍＲＮＡ差异显示技术等等．还介绍ＲＤＡ的几种有效的替代方案．如ＲＦＬＰ消减法、凝胶原位竞争复性技术等。讨论了表型克隆在分离与致病基因连锁不平衡的基因片段中的应用、存在的问题，并展望了发展趋势。     

在线孟德尔人类遗传数据库数据挖掘的研究进展

在线孟德尔人类遗传数据库(OMIM)是描述人类遗传病及其相关基因的知识库,其词条包括疾病的临床特征、基因连锁分析、染色体定位以及动物模型等,是研究疾病与基因关系的重要依据。疾病表型的相似性可能提示分子之间的相互作用。进行表型比对将有助于预测疾病候选基因以及分析分子之间的关系。OMIM数据库采用文本描述疾病表型,并不适用于计算机分析。对OMIM数据进行标准化对于大规模比对和分析疾病的表型数据、建立表型与基因的对应关系具有重要的意义。研究者近期通过引入标准的医学语言系统,采用文本挖掘中的词频-逆文档频率技术以及用于文档分类的余弦定理方法,结合基因本体论及其比对方法,推动了OMIM数据挖掘的快速发展。本文总结了近年来OMIM数据标准化、表型相似性度量及数据挖掘研究的主要成果,并对其发展趋势进行了预测。     

Semantic SenseLab: Implementing the vision of the Semantic Web in neuroscience

ObjectiveIntegrative neuroscience research needs a scalable informatics framework that enables semantic integration of diverse types of neuroscience data. This paper describes the use of the Web Ontology Language (OWL) and other Semantic Web technologies for the representation and integration of molecular-level data provided by several of SenseLab suite of neuroscience databases.MethodsBased on the original database structure, we semi-automatically translated the databases into OWL ontologies with manual addition of semantic enrichment. The SenseLab ontologies are extensively linked to other biomedical Semantic Web resources, including the Subcellular Anatomy Ontology, Brain Architecture Management System, the Gene Ontology, BIRNLex and UniProt. The SenseLab ontologies have also been mapped to the Basic Formal Ontology and Relation Ontology, which helps ease interoperability with many other existing and future biomedical ontologies for the Semantic Web. In addition, approaches to representing contradictory research statements are described. The SenseLab ontologies are designed for use on the Semantic Web that enables their integration into a growing collection of biomedical information resources.ConclusionWe demonstrate that our approach can yield significant potential benefits and that the Semantic Web is rapidly becoming mature enough to realize its anticipated promises. The ontologies are available online at http://neuroweb.med.yale.edu/senselab/.     

A genetic similarity algorithm for searching the Gene Ontology terms and annotating anonymous protein sequences

A genetic similarity algorithm is introduced in this study to find a group of semantically similar Gene Ontology terms. The genetic similarity algorithm combines semantic similarity measure algorithm with parallel genetic algorithm. The semantic similarity measure algorithm is used to compute the similitude strength between the Gene Ontology terms. Then, the parallel genetic algorithm is employed to perform batch retrieval and to accelerate the search in large search space of the Gene Ontology graph. The genetic similarity algorithm is implemented in the Gene Ontology browser named basic UTMGO to overcome the weaknesses of the existing Gene Ontology browsers which use a conventional approach based on keyword matching. To show the applicability of the basic UTMGO, we extend its structure to develop a Gene Ontology -based protein sequence annotation tool named extended UTMGO. The objective of developing the extended UTMGO is to provide a simple and practical tool that is capable of producing better results and requires a reasonable amount of running time with low computing cost specifically for offline usage. The computational results and comparison with other related tools are presented to show the effectiveness of the proposed algorithm and tools.     

Developing the Quantitative Histopathology Image Ontology (QHIO): A case study using the hot spot detection problem

Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology – QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts.     

TRAK ontology: Defining standard care for the rehabilitation of knee conditions

In this paper we discuss the design and development of TRAK (Taxonomy for RehAbilitation of Knee conditions), an ontology that formally models information relevant for the rehabilitation of knee conditions. TRAK provides the framework that can be used to collect coded data in sufficient detail to support epidemiologic studies so that the most effective treatment components can be identified, new interventions developed and the quality of future randomized control trials improved to incorporate a control intervention that is well defined and reflects clinical practice. TRAK follows design principles recommended by the Open Biomedical Ontologies (OBO) Foundry. TRAK uses the Basic Formal Ontology (BFO) as the upper-level ontology and refers to other relevant ontologies such as Information Artifact Ontology (IAO), Ontology for General Medical Science (OGMS) and Phenotype And Trait Ontology (PATO). TRAK is orthogonal to other bio-ontologies and represents domain-specific knowledge about treatments and modalities used in rehabilitation of knee conditions. Definitions of typical exercises used as treatment modalities are supported with appropriate illustrations, which can be viewed in the OBO-Edit ontology editor. The vast majority of other classes in TRAK are cross-referenced to the Unified Medical Language System (UMLS) to facilitate future integration with other terminological sources. TRAK is implemented in OBO, a format widely used by the OBO community. TRAK is available for download from http://www.cs.cf.ac.uk/trak. In addition, its public release can be accessed through BioPortal, where it can be browsed, searched and visualized.     

Knowledge guided analysis of microarray data

To microarray expression data analysis, it is well accepted that biological knowledge-guided clustering techniques show more advantages than pure mathematical techniques. In this paper, Gene Ontology is introduced to guide the clustering process, and thus a new algorithm capturing both expression pattern similarities and biological function similarities is developed. Our algorithm was validated on two well-known public data sets and the results were compared with some previous works. It is shown that our method has advantages in both the quality of clusters and the precision of biological annotations. Furthermore, the clustering results can be adjusted according to different stringency requirements. It is expected that our algorithm can be extended to other biological knowledge, for example, metabolic networks.     

Annotation of functional impact of voltage‐gated sodium channel mutations

Voltage‐gated sodium channels are pore‐forming transmembrane proteins that selectively allow sodium ions to flow across the plasma membrane according to the electro‐chemical gradient thus mediating the rising phase of action potentials in excitable cells and playing key roles in physiological processes such as neurotransmission, skeletal muscle contraction, heart rhythm, and pain sensation. Genetic variations in the nine human genes encoding these channels are known to cause a large range of diseases affecting the nervous and cardiac systems. Understanding the molecular effect of genetic variations is critical for elucidating the pathologic mechanisms of known variations and in predicting the effect of newly discovered ones. To this end, we have created a Web‐based tool, the Ion Channels Variants Portal, which compiles all variants characterized functionally in the human sodium channel genes. This portal describes 672 variants each associated with at least one molecular or clinical phenotypic impact, for a total of 4,658 observations extracted from 264 different research articles. These data were captured as structured annotations using standardized vocabularies and ontologies, such as the Gene Ontology and the Ion Channel ElectroPhysiology Ontology. All these data are available to the scientific community via neXtProt at https://www.nextprot.org/portals/navmut .     

The Gene Ontology Annotation (GOA) project: implementation of GO in SWISS-PROT,TrEMBL, and InterPro

Gene Ontology Annotation (GOA) is a project run by the European Bioinformatics Institute (EBI) that aims to provide assignments of terms from the Gene Ontology (GO) resource to gene products in a number of its databases (http://www.ebi.ac.uk/GOA). In the first stage of this project, GO assignments have been applied to a data set representing the complete human proteome by a combination of electronic mappings and manual curation. This vocabulary has also been applied to the nonredundant proteome sets for all other completely sequenced organisms as well as to proteins from a wide range of organisms where the proteome is not yet complete.     

Formal ontology for natural language processing and the integration of biomedical databases

The central hypothesis underlying this communication is that the methodology and conceptual rigor of a philosophically inspired formal ontology can bring significant benefits in the development and maintenance of application ontologies [A. Flett, M. Dos Santos, W. Ceusters, Some Ontology Engineering Procedures and their Supporting Technologies, EKAW2002, 2003]. This hypothesis has been tested in the collaboration between Language and Computing (L&C), a company specializing in software for supporting natural language processing especially in the medical field, and the Institute for Formal Ontology and Medical Information Science (IFOMIS), an academic research institution concerned with the theoretical foundations of ontology. In the course of this collaboration L&C's ontology, LinKBase, which is designed to integrate and support reasoning across a plurality of external databases, has been subjected to a thorough auditing on the basis of the principles underlying IFOMIS's Basic Formal Ontology (BFO) [B. Smith, Basic Formal Ontology, 2002. http://ontology.buffalo.edu/bfo]. The goal is to transform a large terminology-based ontology into one with the ability to support reasoning applications. Our general procedure has been the implementation of a meta-ontological definition space in which the definitions of all the concepts and relations in LinKBase are standardized in the framework of first-order logic. In this paper we describe how this principles-based standardization has led to a greater degree of internal coherence of the LinKBase structure, and how it has facilitated the construction of mappings between external databases using LinKBase as translation hub. We argue that the collaboration here described represents a new phase in the quest to solve the so-called "Tower of Babel" problem of ontology integration [F. Montayne, J. Flanagan, Formal Ontology: The Foundation for Natural Language Processing, 2003. http://www.landcglobal.com/].     

Ontological integration of data models for cell signaling pathways by defining a factor of causality called 'signal'

Databases have collected masses of information concerning cell signaling pathways that includes information on pathways, molecular interactions as well as molecular complexes. However we have no general data model to represent comprehensive properties of cell signaling pathways, so that this type of information has been represented by two different data models that we call 'binary relation' and 'state transition'. The disagreement between the existing models derives from lack of consensus about a factor of causality in reactions in cell signaling pathways, which is often called 'signal'. We developed an ontology named CSNO (Cell Signaling Networks Ontology) based on device ontology. As device ontology is a research product of knowledge engineering, CSNO is the first application of it to biological knowledge. CSNO defines the factor of causality called 'signal', offers an integrative viewpoint for the two different data models, explicates intrinsic distinctions between signaling and metabolic pathways, and eliminates ambiguity from representation of complex molecules.