MSc Applied Bioinformatics

应用生物信息学理学硕士

• 批准号: BB/H020659/1
• 负责人: Lee Larcombe
• 金额: $ 28.44万
• 依托单位: CRANFIELD UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Training Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FH020659%2F1
• 关键词: MSc; Applied; Bioinformatics

Bioinformatics is about solving biological problems through the application of information technologies. Recent advances in bioanalytical platforms have resulted in the ability to acquire vast amounts of biologically-important data. Since the completion of large-scale genome sequencing projects both the volume and complexity of such data have further grown and we now have a framework on which to base what are known as the post-genomic technologies: transcriptomics, proteomics and metabolomics. Cutting-edge biology is focused increasingly on the elucidation of underlying biological mechanisms for which bioinformatic techniques are needed. These include advanced data analysis for biomarker discovery and data integration strategies required for systems biology. Developed with industry input, this bespoke MSc aims to produce graduates who are able to talk a common language with laboratory-based practitioners and play an increasingly important role in modern interdisciplinary biology. As such students will be trained in state-of-the-art bioinformatics tools and the analytical techniques for understanding and handling complex data sets, and also learn to programme in the major languages used in bioinformatics; R, Perl and Java. The course comprises five main compulsory elements: 1. Introductory streams: two streams designed to develop life science or IT students who join the course into a coherent cohort of bioinformatics students, able to communicate with a common language, and progress through the core application modules with a solid foundation of fundamental skills and knowledge. 2. A series of five core modules: Each module consists of one or two intensive weeks of teaching, with an average 50/50 split between lectures and hands-on computer work. Each module is typically followed by a study week in which the students tackle a significant piece of coursework related to the modules. These coursework assignments are all designed to typify real biological problems, which need to be solved using bioinformatics - examples include writing a software tool, extracting biologically relevant information from data, integrating data or modelling interactions or systems. 3. A software development group project: the group project is one of two integrative assessment points during the course designed to align the learning development with M-level experience and develop on key programming skills and application-specific informatics approaches form the core modules. Following initial lectures covering skills specific to the project, teams are formed to tackle challenging group projects. Each team is encouraged to work out their roles and management structure with guidance from the course tutors. 4. Integrating examination: undertaken by all students following the group project and immediately prior to beginning their individual research projects. This exam assesses the breadth of technical and conceptual knowledge and the student's ability to discuss this in the context of bioinformatics and wider associated fields. 5. An 18-Week Thesis Project Selected by the Student : In keeping with the aims of the MSc course, all projects must involve the application of bioinformatics approaches to solve a biological problem. Typically projects cover different applications (e.g. microarrays, proteomics, text mining, systems biology) and requiring different informatic skills (e.g .programming, statistics, databases). Projects are a major avenue for the involvement of our industrial partners, and an excellent opportunity for students to gain real-world experience. The course is specified to run with a maximum of 25 students and a minimum of 10, although it has run with fewer under exceptional circumstances. The optimum student number with current staff is 15. Please see the Case for Support and associated programme specifications for more details.

生物信息学是通过应用信息技术来解决生物问题的学科。生物分析平台的最新进展使人们能够获取大量具有生物学意义的数据。自大规模基因组测序项目完成以来，此类数据的数量和复杂性都进一步增长，我们现在有了一个框架，可以作为我们所知的后基因组技术的基础：转录组学、蛋白质组学和代谢组学。前沿生物学越来越注重阐明需要生物信息学技术的潜在生物学机制。其中包括用于发现生物标记物的高级数据分析和系统生物学所需的数据集成策略。在行业投入下开发的这一定制硕士课程旨在培养能够与实验室从业人员交谈的毕业生，并在现代跨学科生物学中发挥越来越重要的作用。这样，学生将接受最先进的生物信息学工具以及理解和处理复杂数据集的分析技术的培训，并学习用生物信息学中使用的主要语言R、Perl和Java编程。这门课程包括五个主要必修课：1.入门课程：两个课程旨在将参加课程的生命科学或信息技术学生培养成一群连贯的生物信息学学生，能够使用共同的语言进行交流，并在具有坚实基础的基本技能和知识的基础上逐步完成核心应用模块。2.由五个核心模块组成的系列：每个模块包括一到两周的密集教学，讲课和动手操作的计算机工作平均各占一半。每个模块之后通常会有一个学习周，在此期间，学生们要处理与模块相关的重要课程作业。这些课程作业都是为了代表真实的生物学问题，需要使用生物信息学来解决--例如编写软件工具，从数据中提取与生物相关的信息，整合数据或对相互作用或系统进行建模。3.软件开发小组项目：该小组项目是课程期间的两个综合评估点之一，旨在将学习发展与M-Level经验相结合，并发展关键编程技能和特定于应用程序的信息学方法，形成核心模块。在最初讲授了项目特定技能之后，将组成团队来处理具有挑战性的团体项目。鼓励每个团队在课程导师的指导下确定自己的角色和管理结构。4.综合考试：由所有学生在小组项目之后、紧接着开始个人研究项目之前进行。这项考试评估技术和概念知识的广度，以及学生在生物信息学和更广泛的相关领域中讨论这一问题的能力。5.由学生选择的为期18周的论文项目：为了与硕士课程的目标保持一致，所有项目都必须涉及生物信息学方法的应用，以解决生物学问题。通常，项目涵盖不同的应用(例如，微阵列、蛋白质组学、文本挖掘、系统生物学)，并需要不同的信息学技能(例如，编程、统计、数据库)。项目是我们行业合作伙伴参与的主要途径，也是学生获得真实世界经验的绝佳机会。该课程规定最多招收25名学生，最少招收10名学生，尽管在特殊情况下招生人数较少。现有教职员工的最佳学生人数为15人。有关更多细节，请参阅支持案例和相关的课程规格。