生物信息学在昆虫学研究中的应用

随着深度测序和基因芯片技术的不断发展,基因组、转录组、表达谱数据大量积累。目前,至少有10多个昆虫的基因组已被测序,30多个昆虫的转录组数据被报道。显然,传统的生物统计学方法无法处理如此海量的生物数据。量变引发质变,生物数据的大量积累催生了一门新兴学科,生物信息学。生物信息学融合了统计学、信息科学和生物学等各学科的理论和研究内容,在医学、基础生物学、农业科学以及昆虫学等方面获得了广泛的应用。生物信息学的目标是存储数据、管理数据和数据挖掘。因此,建立维护生物学数据库、设计开发基于模式识别、机器学习、数据挖掘等方法的生物软件,以及运用上述工具进行深度的数据挖掘,是生物信息学的重要研究内容。本文首先简要介绍了生物信息学的历史、研究现状及其在昆虫学科中的应用,然后综述了昆虫基因组学和转录组学的研究进展,最后对生物信息学在昆虫学研究中的应用前景进行了展望。     

后基因组时代的生物信息学

随着人类基因组计划的完成,不断积累的巨量的生物学数据和快速发展的信息学技术,给后基因组时代的生物信息学研究带来了新的挑战。该文对后基因组时代的生物信息学研究内容进行了比较全面的描述,分别就其研究对象和研究方向作了区别讨论,分析了生物信息学研究的现状和趋势,比较了国内外的研究发展情况和差距。针对我国在研究中所存在的主要问题,提出了建议并做了展望。     

一种通用的、基于Agent的生物资源整合架构

众所周知,随着基因组测序工作的蓬勃发展和后基因组时代的到来,生物信息学数据呈指数级增长.生物界在享受着资源共享所带来便利的同时,也随着数据总量和复杂性的不断增加而变得异构化和分布化.目前,各种生物计算软件和数据库资源通常标准不一而且很难兼容.因此,如何在这些异构资源之间实现数据集成与软件共享是有效利用生物信息资源的关键.为解决以上问题,本文提出了一种新型的数据整合架构,该架构通过将web服务与并行计算相结合的方法,轻松地实现了对异地资源数据的访问、提取、转化以及整合.实验证明,本系统在处理异构、海量数据方面有着巨大的计算潜力.     

神经网络自编码器算法在癌症信息学研究中的应用

癌症已经被广泛认为是高度异质性的疾病,癌症的早期诊断、分型和预后已成为癌症研究的关注重点。在大数据时代,对海量癌症生物医学数据进行高效的数据挖掘是生物信息学面临的重要挑战。自编码器(Autoencoder)作为神经网络的一种典型模型,能够通过无监督的方式高效地学习输入数据的特征,进而对生物数据进行整合与挖掘。文中首先介绍了自编码器模型结构并阐述其工作流程,之后结合多种类型的生物医学数据总结自编码器在癌症信息学研究领域的进展,并展望其发展趋势及应用方向。     

生物信息学中的数据库技术

生物信息学是近年来发展最快的学科之一,各类生物信息数据库不断的涌现。随着生物分子数据量的迅速膨胀,数据结构日趋复杂,生物信息学对数据库技术提出了更高的要求。本文讨论了目前生物信息学中数据库技术的发展现状、面临的问题和未来趋势,主要包括数据库管理、数据库分析、数据库集成等。     

关于高通量测序背景下生物信息学教学的一些建议

高通量数据背景下,生物信息学已成为解决生物问题一门必备知识和工具,本文从生命科学的现实背景出发,探讨了高通量测序时代关于生物信息学课程的教学内容、教学方式和教学目的方面的一些建议。期望通过现代生物信息学课程的设计和讲授,使学生及时掌握利用生物信息学处理前沿问题的能力,强化生物信息学实践教学中技能的培养,培养在获取、处理、开发和利用等方面有较强的研究能力和实践能力的生物信息技术人才,提高生命科学相关专业学生的社会就业竞争力。     

文本分析技术在蛋白质生物信息学中应用的案例综述

海量数据时代考察文本分析技术在生物信息学领域的应用具有重要的理论和现实价值。本文讨论了文本分析在蛋白质计算分析中的几个应用实例以及核心技术内容。文本分析技术应用于生物信息学领域可发挥引领和导向作用,在生物信息学中的应用又进一步促进了文本分析技术的发展。文本分析技术虽然广泛在生物信息学中应用,但是其发展仍然有需要尚待解决的几个问题,本文对此也进行了讨论。     

生命与健康大数据中心资源

生命与健康多组学数据是生命科学研究和生物医学技术发展的重要基础。然而,我国缺乏生物数据管理和共享平台,不但无法满足国内日益增长的生物医学及相关学科领域的研究发展需求,而且严重制约我国生物大数据整合共享与转化利用。鉴于此,中国科学院北京基因组研究所于2016年初成立生命与健康大数据中心(BIG Data Center, BIGD),围绕国家人口健康和重要战略生物资源,建立生物大数据管理平台和多组学数据资源体系。本文重点介绍BIGD的生命与健康大数据资源系统,主要包括组学原始数据归档库、基因组数据库、基因组变异数据库、基因表达数据库、甲基化数据库、生物信息工具库和生命科学维基知识库,提供生物大数据汇交、整合与共享服务,为促进我国生命科学数据管理、推动国家生物信息中心建设奠定重要基础。     

生物芯片、生物传感器和生物信息学

近年来,在生物技术和医学研究领域涌现出了许多新技术平台,其中就包括生物芯片技术和生物传感器技术。生物芯片和生物传感器的构建都必须以生物信息学为基础,而两种技术平台应用所得出的数据和结果又反过来大大丰富和充实了生物信息学本身。本分析概述了生物芯片和生物传感器两种技术平台以及生物信息学,对三之间的相互关系进行了讨论。     

生命科学信息工程设施以及在中国的实现

以组学数据为代表的生命科学数据呈指数增长.与高能物理、气象、地质、地理和环境科学等其他数据密集型学科一样,现代生命科学已经进入了高度信息化的时代——第四范式时代.国家跨组学信息工程大设施(China Information Engineering Infrastructure for Pan-Omics Studies,CIEIPOS)已经成为推动中国生命科学进一步发展、并使海量数据转化成知识与应用的必不可少的国家生命科学基础设施.本文介绍国内外生物数据收集、管理与利用的现状,提出建设CIEIPOS生物信息"集散地"的重要性与迫切性,阐述实现数据整合、搜索与可视化的挑战与可能方案.CIEIPOS的另外一个重要功能是支持对组学数据的管理、分析、挖掘与利用,这使得CIEIPOS不同于传统的国际生物信息中心,如美国国家生物信息技术中心(National Center for Biotechnology Information)与欧洲生物信息学研究所(European Bioinformatics Institute).本文以质谱平台产出的高通量蛋白质组数据为例,说明组学数据分析的复杂性.通过对跨组学数据在不同时空的模拟分析,进一步说明CIEIPOS的实际应用对计算机硬件与网络的要求.     

Bioinformatics data distribution and integration via Web Services and XML

It is widely recognized that exchange, distribution, and integration of biological data are the keys to improve bioinformatics and genome biology in post-genomic era. However, the problem of exchanging and integrating biological data is not solved satisfactorily. The extensible Markup Language (XML) is rapidly spreading     

Yale school of public health symposium on lifetime exposures and human health: the exposome; summary and future reflections

The exposome is defined as “the totality of environmental exposures encountered from birth to death” and was developed to address the need for comprehensive environmental exposure assessment to better understand disease etiology. Due to the complexity of the exposome, significant efforts have been made to develop technologies for longitudinal, internal and external exposure monitoring, and bioinformatics to integrate and analyze datasets generated. Our objectives were to bring together leaders in the field of exposomics, at a recent Symposium on “Lifetime Exposures and Human Health: The Exposome,” held at Yale School of Public Health. Our aim was to highlight the most recent technological advancements for measurement of the exposome, bioinformatics development, current limitations, and future needs in environmental health. In the discussions, an emphasis was placed on moving away from a one-chemical one-health outcome model toward a new paradigm of monitoring the totality of exposures that individuals may experience over their lifetime. This is critical to better understand the underlying biological impact on human health, particularly during windows of susceptibility. Recent advancements in metabolomics and bioinformatics are driving the field forward in biomonitoring and understanding the biological impact, and the technological and logistical challenges involved in the analyses were highlighted. In conclusion, further developments and support are needed for large-scale biomonitoring and management of big data, standardization for exposure and data analyses, bioinformatics tools for co-exposure or mixture analyses, and methods for data sharing.     

Applications of integrative OMICs approaches to gene regulation studies

Background: Functional genomics employs dozens of OMICs technologies to explore the functions of DNA, RNA and protein regulators in gene regulation processes. Despite each of these technologies being powerful tools on their own, like the parable of blind men and an elephant, any one single technology has a limited ability to depict the complex regulatory system. Integrative OMICS approaches have emerged and become an important area in biology and medicine. It provides a precise and effective way to study gene regulations.Results: This article reviews current popular OMICs technologies, OMICs data integration strategies, and bioinformatics tools used for multi-dimensional data integration. We highlight the advantages of these methods, particularly in elucidating molecular basis of biological regulatory mechanisms. Conclusions: To better understand the complexity of biological processes, we need powerful bioinformatics tools to integrate these OMICs data. Integrating multi-dimensional OMICs data will generate novel insights into system-level gene regulations and serves as a foundation for further hypothesis-driven research.     

Bioinformatic-driven search for metabolic biomarkers in disease

The search and validation of novel disease biomarkers requires the complementary power of professional study planning and execution, modern profiling technologies and related bioinformatics tools for data analysis and interpretation. Biomarkers have considerable impact on the care of patients and are urgently needed for advancing diagnostics, prognostics and treatment of disease. This survey article highlights emerging bioinformatics methods for biomarker discovery in clinical metabolomics, focusing on the problem of data preprocessing and consolidation, the data-driven search, verification, prioritization and biological interpretation of putative metabolic candidate biomarkers in disease. In particular, data mining tools suitable for the application to omic data gathered from most frequently-used type of experimental designs, such as case-control or longitudinal biomarker cohort studies, are reviewed and case examples of selected discovery steps are delineated in more detail. This review demonstrates that clinical bioinformatics has evolved into an essential element of biomarker discovery, translating new innovations and successes in profiling technologies and bioinformatics to clinical application.     

Predicting biological networks from genomic data

Continuing improvements in DNA sequencing technologies are providing us with vast amounts of genomic data from an ever-widening range of organisms. The resulting challenge for bioinformatics is to interpret this deluge of data and place it back into its biological context. Biological networks provide a conceptual framework with which we can describe part of this context, namely the different interactions that occur between the molecular components of a cell. Here, we review the computational methods available to predict biological networks from genomic sequence data and discuss how they relate to high-throughput experimental methods.     

From biological databases to platforms for biomedical discovery

The use of high-throughput DNA sequencing and proteomic methods has led to an unprecedented increase in the amount of genomic and proteomic data. Application of computing technologies and development of computational tools to analyze and present these data has not kept pace with the accumulation of information. Here, we discuss the use of different database systems to store biological information and mention some of the key emerging computing technologies that are likely to have a key role in the future of bioinformatics.     

Statistics and bioinformatics in nutritional sciences: analysis of complex data in the era of systems biology

Over the past 2 decades, there have been revolutionary developments in life science technologies characterized by high throughput, high efficiency, and rapid computation. Nutritionists now have the advanced methodologies for the analysis of DNA, RNA, protein, low-molecular-weight metabolites, as well as access to bioinformatics databases. Statistics, which can be defined as the process of making scientific inferences from data that contain variability, has historically played an integral role in advancing nutritional sciences. Currently, in the era of systems biology, statistics has become an increasingly important tool to quantitatively analyze information about biological macromolecules. This article describes general terms used in statistical analysis of large, complex experimental data. These terms include experimental design, power analysis, sample size calculation, and experimental errors (Type I and II errors) for nutritional studies at population, tissue, cellular, and molecular levels. In addition, we highlighted various sources of experimental variations in studies involving microarray gene expression, real-time polymerase chain reaction, proteomics, and other bioinformatics technologies. Moreover, we provided guidelines for nutritionists and other biomedical scientists to plan and conduct studies and to analyze the complex data. Appropriate statistical analyses are expected to make an important contribution to solving major nutrition-associated problems in humans and animals (including obesity, diabetes, cardiovascular disease, cancer, ageing, and intrauterine growth retardation).     

Issues in bioinformatics benchmarking: the case study of multiple sequence alignment

The post-genomic era presents many new challenges for the field of bioinformatics. Novel computational approaches are now being developed to handle the large, complex and noisy datasets produced by high throughput technologies. Objective evaluation of these methods is essential (i) to assure high quality, (ii) to identify strong and weak points of the algorithms, (iii) to measure the improvements introduced by new methods and (iv) to enable non-specialists to choose an appropriate tool. Here, we discuss the development of formal benchmarks, designed to represent the current problems encountered in the bioinformatics field. We consider several criteria for building good benchmarks and the advantages to be gained when they are used intelligently. To illustrate these principles, we present a more detailed discussion of benchmarks for multiple alignments of protein sequences. As in many other domains, significant progress has been achieved in the multiple alignment field and the datasets have become progressively more challenging as the existing algorithms have evolved. Finally, we propose directions for future developments that will ensure that the bioinformatics benchmarks correspond to the challenges posed by the high throughput data.