伯明翰大学生物信息学

专业：Bioinformatics MSc/Diploma/Certificate  生物信息学

链接：

https://www.birmingham.ac.uk/postgraduate/courses/taught/med/bioinformatics.aspx

课程设置：

The modules on the programme are as follows (please find more details below):

Essentials of Biology, Mathematics and Statistics (20 credits) 生物学，数学和统计学的基本要素

Genomics & Next Generation Sequencing (20 credits)  基因组学和下一代测序

Data Analytics & Statistical Machine Learning (20 credits)  数据分析和统计机器学习

Metabolomics and advanced (omics) technologies (20 credits) 代谢组学和高级（组学）技术

Computational Biology for Complex Systems (20 credits) 复杂系统的计算生物学

Interdisciplinary Bioinformatics Group Project (20 credits) 跨学科生物信息学小组项目

Individual Project (60 credits) 个人项目

Essentials of Biology, Mathematics and Statistics (20 credits)

This module will provide an introduction (or refresher) to essential biological and quantitative theory that underpins modern bioinformatics. Concepts will be introduced via a series of core problems whose details will be explored in greater depth in later modules.

本单元将提供现代生物信息学的基本生物学和定量理论的介绍（或复习）。概念将通过一系列核心问题来介绍，这些问题的细节将在以后的模块中更深入地探讨。

Quantitative topics will include:

量化主题包括

Linear Algebra: basic matrix-vector operations, least-squares

Probability Theory: Rules of Probability, Conditional Probability, Bayes’ Rule, distributions

Descriptive Statistics: summary statistics, visualisation

Hypothesis Testing: Fisher exact, chi-square, t-test

Correlation and Causation: Parametric and non-parametric measures

Introduction to Statistical Modelling in the R programming language: linear models, estimation

Furthermore, this module will go through the very essential of biology, biochemistry and biotechnology including cells, proteins, DNA and genes in to reach a level where you are on par to understand the mandatory modules.

线性代数：基本矩阵-向量运算，最小二乘概率理论：概率规则，条件概率，贝叶斯规则，分布描述性统计：汇总统计，可视化假设检验：Fisher精确，卡方，t-测试相关和因果关系：在R编程语言中对统计建模的参数和非参数度量：线性模型，进一步估计，这个模块将会经历生物学，生物化学和生物技术的基本原理，包括细胞，蛋白质，DNA和基因，达到一个水平，你可以达到标准，去理解强制性的模块。

The module contains a variety of integrated learning environments, including interactive lectures as well as tutorials to explain and give feedback on aspects of assessment.

这个模块包含了各种各样的集成学习环境，包括交互式讲座和教程，以解释和给出评估方面的反馈。

By the end of the module you will be able to:

Understand essential mathematical and statistical concepts and apply the correct techniques to solve elementary data analysis problems

Correctly apply techniques for the graphical representation and visualisation of data

Perform essential statistical data analysis in a computer programming language, specifically R

Understand essential concepts in cell biology and genetics such as the role of DNA, RNA and Proteins and their relation to specific bioinformatics problems.

Solve quantitative problems inspired by real world bioinformatics that require an understanding of the underlying biology and the application of the correct mathematical and statistical techniques

Demonstrate the qualities and transferable skills necessary for employment requiring the exercise of initiative and personal responsibility, decision making in complex and unpredictable situations, and the independent learning ability required for continuing professional development

在这个模块的最后，你将能够：了解基本的数学和统计的概念和应用正确的技术来解决基础数据分析问题正确应用技术的图形表示和可视化数据执行必要的统计数据分析在计算机编程语言中,特别是R等细胞生物学和遗传学的理解基本概念的作用DNA,RNA和蛋白质和他们的关系到特定的生物信息学问题。灵感来自现实世界生物信息学解决定量问题,需要了解的基础生物学和应用正确的数学和统计技术演示所需的品质和可转移技能就业需要的锻炼计划和个人责任,决策在复杂和不可预测的情况下,和持续的职业发展所需的独立学习能力

Genomics & Next Generation Sequencing (20 credits)

基因组学和下一代测序

This module will introduce the you to various sides of Omics:

Genomics

Transcriptomics

Methylation

Transcription factors analysis

RNA binding protein analysis

Chromatin accessibility analysis (e.g. DNase-seq, ATAC-seq)

Chromatin structure analysis (e.g. HiC, ChIA-PET)

The module will include a coverage of the technological progress:

History: Sanger sequencing through array technologies

Next generation Sequencing

Advanced library construction procedures for specialized assays, including ChIP, DNase, ATAC, HiC, eCLIP, and others

This module will also address specific fields of Classical Genetics, Population Genetics and Cancer Genomics. It will involve a biological, technological and analytical dimension to help you design the best experiment with the appropriate data type and enable its analysis with the latest state of the art approaches.

基因组学转录因子分析RNA结合蛋白分析（如DNase-seq，ATAC-seq）染色质结构分析（如HiC，ChIA-PET），该模块将包括对技术进步的报道：历史：Sanger通过阵列技术对下一代进行测序，包括芯片、dna酶、ATAC、HiC、eCLIP等专业化验方法，该模块还将处理经典遗传学、人口遗传学和癌症基因组学等特定领域。它将涉及到一个生物学、技术和分析方面的维度，帮助您设计出最佳的具有适当数据类型的实验，并使其能够以最新的艺术方法进行分析。

By the end of the module you should be able to:

Understand the biological interpretation of the various *omics fields, especially DNA, RNA and Methylation based.

Understand the various technologies available to measure the various type of information from Sanger sequencing, micro-array, Mass-Spectrometry to Next Generation sequencing

Analyse the various types of data generated in the field both with command line and web interface such as Galaxy

Integrate the various type of data to understand the biological implication of the results

Deal with the complexity of information available to enable the integration of diverse data types

在这个模块的最后，你应该能够：理解各种组学领域的生物学解释，特别是DNA、RNA和甲基化。了解各种技术可用来测量的各种类型的信息从桑格测序,质谱仪微阵列,下一代测序分析各种类型的数据中生成领域都与星系等命令行和web界面集成的各种类型的数据的生物学含义理解结果处理信息的复杂性,使集成不同的数据类型

Data Analytics & Statistical Machine Learning (20 credits) 数据分析,统计机器学习

By the end of the module you will be able to:

Demonstrate a good understanding of complexity of omics and clinical data and their management including their semantic representation

Demonstrate an in-depth understanding and ability to perform Data integration, mining and analysis

Demonstrate conceptual understanding of Computing, Algorithmic and Programming that enables the student to evaluate methodologies and develop critiques of them and, where appropriate, propose new methods

Deal with the complexity of information available to enable the integration of diverse data types

Demonstrate self direction and originality in tackling and solving problems to perform the appropriate Modelling and Optimization

证明很好地理解复杂的组学和临床数据和他们的管理,包括演示深入语义表示理解和执行数据集成能力,挖掘和分析证明概念的理解计算、算法和编程,使学生评价方法和开发他们的批评,在适当的地方,提出新的方法来处理可获得的信息的复杂性，以支持不同类型的数据类型的集成，演示了在处理和解决问题时的自导向和原创性，以执行适当的建模和优化。

Metabolomics and advanced (omics) technologies (20 credits)

代谢组学和高级（组学）技术

By the end of the module you will be able to:

Demonstrate a conceptual understanding of metabolomics, biological imaging and other advanced bioscience technologies.

Demonstrate a conceptual understanding of the major challenges facing metabolomics, biological imaging and other advanced bioscience technologies.

Demonstrate a conceptual understanding of a typical bioinformatics workflow to process and analyse metabolomics datasets.

Perform basic bioinformatics data analysis and extract biological insight from large metabolomics data sets.

对代谢组学、生物成像和其他先进的生物科学技术有一个概念上的理解。对代谢组学、生物成像和其他先进的生物科学技术所面临的主要挑战进行概念性的理解。演示对典型的生物信息学工作流的概念理解，以处理和分析代谢组学数据集。执行基本的生物信息学数据分析，并从大型代谢组学数据集中提取生物信息。

入学要求：

2:1 or equivalent in Biology, Mathematics, Computer Science or other relevant subjects

English to IELTS 6.5 (with no less than 6.0 in any band).