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。     

生物信息学及其在蛋白质组学中的应用

随着基因组学和蛋白质组学的发展,生物信息学在数据处理中的应用已经越来越广泛。作为数据处理中越来越重要的分析手段,蛋白质组学数据库是蛋白质组学的主要内容之一。本文分别从生物信息学的蛋白质双向电泳数据库和基于蛋白质质谱结果的数据库两个方面,概述了发展中的蛋白质数据库的最新动态和有关信息,同时对主要的热门蛋白质组学数据库站点和资源进行了评价和分析。     

生物信息学在蛋白质组学研究中的应用进展

生物信息学是运用数学和信息学方法阐明和解释海量生物学数据所蕴含的生物学意义的重要手段和工具.随着蛋白质组学研究的不断发展和深入,大量的蛋白序列、结构、功能以及互作数据不断产生.面对海量蛋白质组数据的获取、处理、存储以及蛋白质组数据信息的挖掘,生物信息学已成为蛋白组学研究中不可或缺的组成部分.本文结合蛋白质组学的发展历程...     

基于知识发现的生物信息学

生物信息学的研究热点及方面的基本工具是知识发现,而知识发现是计算机科学近期研究的热点,具有一定的理论基础,基于知识发现的生物信息学是理论,应用相结合的必由之路。     

生物信息学在食用菌研究中的应用

食用菌因其富含多种氨基酸及微量元素等物质,具有较高的营养价值和药用价值,越来越受到人们的关注和喜爱。我国作为最重要的食用菌生产国,食用菌生产规模不断扩大,产量也在逐年提高。为了更好地发展食用菌产业,迫切需要在传统的食用菌产业链,如优良品种选育及栽培生产中融入新技术。生物信息学作为一门研究分析生物生命结构的技术门类,通过运用数学、计算机科学等工具揭示了数据所蕴含的生物学意义,极大地促进了生命科学研究的发展,也为食用菌更深入的研究与应用提供了技术保障。本文从食用菌育种及种质资源调查、病虫害防治、基因组学、食用菌安全等几方面阐述了生物信息学在食用菌领域的具体应用,对生物信息学在食用菌及农业领域的发展进行了展望,以期为促进食用菌研究和生产发展提供参考。     

医学生物信息学在心血管研究领域的应用--数据与知识

随着生物技术的进步,特别是以基因组、蛋白质组为标志的导致高通量实验数据产生的工作的开展,大量的实验数据在各个领域堆积拥塞,与领域内的知识的累计出现了极为不平衡的发展,因此,对这些数据的处理成为了学科发展的迫切需求。为了避免这些数据成为垃圾,数据库、统计学、信号处理、数据挖掘、知识管理、人工智能等多种技术被运用到医学生物学领域,使得医学生物信息学不再是医学、生物学和信息学、计算机科学的单纯交叉,而独立成为一门专业的学科,重点也由原来单纯的研究计算机信息技术在医学生物信息学中的延展和运用,转变到研究、发现、开发、创新适合医学生物学自身特点的新思想和新方法上来。本文对近年来心血管领域内医学生物信息发展和运用的情况进行了回顾和分析,并对该领域可能的发展方向做出判断。     

生物信息学在生物学研究领域的应用

综合叙述了生物信息学对生物学科研工作的影响,介绍了生物信息学在生物技术、生物医学、农业、食品、环境、能源等研究领域的应用概况,阐述了生物信息学为生物科研工作服务的意义。     

生物信息学对计算机科学发展的机遇与挑战

生物信息学是一个发展很快的新兴学科,是计算机应用的最重要的领域之一,同时生物信息学的发展又给计算机学科提出了许多新的课题,从而促进计算机学科自身的发展。从数据库技术、海量存储技术、数据挖掘、计算几何、DNA计算、网格计算、机器学习、人工心智、web service等方面,就生物信息学对计算机科学发展的促进作用进行了论述。     

生物信息学及其在菌物学研究上的应用

主要介绍生物信息学的基本概念、发展历程、特点、研究领域、面临的挑战以及生物信息学在菌物研究上的应用成果。分析了生物信息学在菌物研究方面的应用前景,并提出了研究和开发的建议。     

生物信息学在基因芯片中的应用

生物信息学和基因芯片是生命科学研究领域中的两种新方法和新技术,生物信息学与基因芯片密切相关,生物信息学促进了基因芯片的研究与应用,而基因芯片则丰富了生物信息学的研究内容。本论文探讨生物信息学在基因芯片中的应用,将生物信息学方法运用到高密度基因芯片设计和芯片实验数据管理及分析。从信息学的角度提出基因芯片设计准则,提出寡核苷酸探针的优化设计方法,将该方法运用于再测序型芯片和基因表达型芯片的设计,在此基础上研制出高密度基因芯片设计软件系统和实验结果分析系统。     

Helicobacter pylori proteomics by 2‐DE/MS, 1‐DE‐LC/MS and functional data mining

With its predicted proteome of 1550 proteins (data set Etalon) Helicobacter pylori 26695 represents a perfect model system of medium complexity for investigating basic questions in proteomics. We analyzed urea‐solubilized proteins by 2‐DE/MS (data set 2‐DE) and by 1‐DE‐LC/MS (Supprot); proteins insoluble in 9 M urea but solubilized by SDS (Pellet); proteins precipitating in the Sephadex layer at the application side of IEF (Sephadex) by 1‐DE‐LC/MS; and proteins precipitating close to the application side within the IEF gel by LC/MS (Startline). The experimental proteomics data of H. pylori comprising 567 proteins (protein coverage: 36.6%) were stored in the Proteome Database System for Microbial Research ( http://www.mpiib‐berlin.mpg.de/2D‐PAGE/ ), which gives access to raw mass spectra (MALDI‐TOF/TOF) in T2D format, as well as to text files of peak lists. For data mining the protein mapping and comparison tool PROMPT ( http://webclu.bio.wzw.tum.de/prompt/ ) was used. The percentage of proteins with transmembrane regions, relative to all proteins detected, was 0, 0.2, 0, 0.5, 3.8 and 6.3% for 2‐DE, Supprot, Startline, Sephadex, Pellet, and Etalon, respectively. 2‐DE does not separate membrane proteins because they are insoluble in 9 M urea/70 mM DTT and 2% CHAPS. SDS solubilizes a considerable portion of the urea‐insoluble proteins and makes them accessible for separation by SDS‐PAGE and LC. The 2‐DE/MS analysis with urea‐solubilized proteins and the 1‐DE‐LC/MS analysis with the urea‐insoluble protein fraction (Pellet) are complementary procedures in the pursuit of a complete proteome analysis. Access to the PROMPT‐generated diagrams in the Proteome Database allows the mining of experimental data with respect to other functional aspects.     

ProteoConnections: a bioinformatics platform to facilitate proteome and phosphoproteome analyses

Novel and improved computational tools are required to transform large-scale proteomics data into valuable information of biological relevance. To this end, we developed ProteoConnections, a bioinformatics platform tailored to address the pressing needs of proteomics analyses. The primary focus of this platform is to organize peptide and protein identifications, evaluate the quality of the acquired data set, profile abundance changes, and accelerate data interpretation. Peptide and protein identifications are stored into a relational database to facilitate data mining and to evaluate the quality of data sets using graphical reports. We integrated databases of known PTMs and other bioinformatics tools to facilitate the analysis of phosphoproteomics data sets and to provide insights for subsequent biological validation experiments. Phosphorylation sites are also annotated according to kinase consensus motifs, contextual environment, protein domains, binding motifs, and evolutionary conservation across different species. The practical application of ProteoConnections is further demonstrated for the analysis of the phosphoproteomics data sets from rat intestinal IEC-6 cells where we identified 9615 phosphorylation sites on 2108 phosphoproteins. Combined proteomics and bioinformatics analyses revealed valuable biological insights on the regulation of phosphoprotein functions via the introduction of new binding sites on scaffold proteins or the modulation of protein-protein, protein-DNA, or protein-RNA interactions. Quantitative proteomics data can be integrated into ProteoConnections to determine the changes in protein phosphorylation under different cell stimulation conditions or kinase inhibitors, as demonstrated here for the MEK inhibitor PD184352.     

A collection of open source applications for mass spectrometry data mining

We present several bioinformatics applications for the identification and quantification of phosphoproteome components by MS. These applications include a front‐end graphical user interface that combines several Thermo RAW formats to MASCOT? Generic Format extractors (EasierMgf), two graphical user interfaces for search engines OMSSA and SEQUEST (OmssaGui and SequestGui), and three applications, one for the management of databases in FASTA format (FastaTools), another for the integration of search results from up to three search engines (Integrator), and another one for the visualization of mass spectra and their corresponding database search results (JsonVisor). These applications were developed to solve some of the common problems found in proteomic and phosphoproteomic data analysis and were integrated in the workflow for data processing and feeding on our LymPHOS database. Applications were designed modularly and can be used standalone. These tools are written in Perl and Python programming languages and are supported on Windows platforms. They are all released under an Open Source Software license and can be freely downloaded from our software repository hosted at GoogleCode.     

农林院校生物信息学专业数据库技术课程教学改革刍议

生物信息学是一门交叉学科,对于现代生物学研究具有重要的意义。数据库技术是生物信息学的基础之一。本文对农林院校的生物信息学专业的数据库技术课程的教学现状做了一些介绍,对目前生物信息专业数据库教学存在的问题进行了分析。结合教学的实践,有针对性的提出了一些教学改革的具体措施。     

Data mining the protein data bank: automatic detection and assignment of carbohydrate structures

Knowledge of the 3D structure of glycans is a prerequisite for a complete understanding of the biological processes glycoproteins are involved in. However, due to a lack of standardised nomenclature, carbohydrate compounds are difficult to locate within the Protein Data Bank (PDB). Using an algorithm that detects carbohydrate structures only requiring element types and atom coordinates, we were able to detect 1663 entries containing a total of 5647 carbohydrate chains. The majority of chains are found to be N-glycosidically bound. Noncovalently bound ligands are also frequent, while O-glycans form a minority. About 30% of all carbohydrate containing PDB entries comprise one or several errors. The automatic assignment of carbohydrate structures in PDB entries will improve the cross-linking of glycobiology resources with genomic and proteomic data collections, which will be an important issue of the upcoming glycomics projects. By aiding in detection of erroneous annotations and structures, the algorithm might also help to increase database quality.     

卵巢癌侵袭转移机制的生物信息学研究进展

卵巢癌因其侵袭转移特性,致死率极高,居所有妇科恶性肿瘤之首。近年来随着高通量测序技术及生物信息学方法的快速发展,越来越多调控卵巢癌侵袭转移机制的相关生物大分子被发现。本文对卵巢癌侵袭转移机制的研究背景及现状进行了综述,归纳总结了侵袭转移机制相关调控因素,并对蛋白质组学和单细胞组学的生物信息学分析工具及数据库进行了汇总和介绍,以期为卵巢肿瘤细胞侵袭转移机制的深入研究提供理论依据和科研线索。     

Association rule mining based study for identification of clinical parameters akin to occurrence of brain tumor

Healthcare sector is generating a large amount of information corresponding to diagnosis, disease identification and treatment of an individual. Mining knowledge and providing scientific decision-making for the diagnosis & treatment of disease from the clinical dataset is therefore increasingly becoming necessary. Aim of this study was to assess the applicability of knowledge discovery in brain tumor data warehouse, applying data mining techniques for investigation of clinical parameters that can be associated with occurrence of brain tumor. In this study, a brain tumor warehouse was developed comprising of clinical data for 550 patients. Apriori association rule algorithm was applied to discover associative rules among the clinical parameters. The rules discovered in the study suggests - high values of Creatinine, Blood Urea Nitrogen (BUN), SGOT & SGPT to be directly associated with tumor occurrence for patients in the primary stage with atleast 85% confidence and more than 50% support. A normalized regression model is proposed based on these parameters along with Haemoglobin content, Alkaline Phosphatase and Serum Bilirubin for prediction of occurrence of STATE (brain tumor) as 0 (absent) or 1 (present). The results indicate that the methodology followed will be of good value for the diagnostic procedure of brain tumor, especially when large data volumes are involved and screening based on discovered parameters would allow clinicians to detect tumors at an early stage of development.