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/postgrauate/courses/taught/me/bioinformatics.aspx

课程设置：

The moules on the programme are as follows (please fin more etails below):

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

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

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

Metabolomics an avance (omics) technologies (20 creits) 代谢组学和高级（组学）技术

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

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

Iniviual Project (60 creits) 个人项目

Essentials of Biology, Mathematics an Statistics (20 creits)

This moule will provie an introuction (or refresher) to essential biological an quantitative theory that unerpins moern bioinformatics. Concepts will be introuce via a series of core problems whose etails will be explore in greater epth in later moules.

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 inclue:

量化主题包括

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

Probability Theory: Rules of Probability, Conitional Probability, Bayes ’ Rule, istributions

Descriptive Statistics: summary statistics, visualisation

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

Correlation an Causation: Parametric an non-parametric measures

Introuction to Statistical Moelling in the R programming language: linear moels, estimation

Furthermore, this moule will go through the very essential of biology, biochemistry an biotechnology incluing cells, proteins, DNA an genes in to reach a level where you are on par to unerstan the manatory moules.

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 moule contains a variety of integrate learning environments, incluing interactive lectures as well as tutorials to explain an give feeback 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 en of the moule you will be able to:

Unerstan essential mathematical an statistical concepts an apply the correct techniques to solve elementary ata analysis problems

Correctly apply techniques for the graphical representation an visualisation of ata

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

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

Solve quantitative problems inspire by real worl bioinformatics that require an unerstaning of the unerlying biology an the application of the correct mathematical an statistical techniques

Demonstrate the qualities an transferable skills necessary for employment requiring the exercise of initiative an personal responsibility, ecision making in complex an unpreictable situations, an the inepenent learning ability require for continuing professional evelopment

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 creits)

基因组学和下一代测序

This moule will introuce the you to various sies of Omics:

Genomics

Transcriptomics

Methylation

Transcription factors analysis

RNA bining protein analysis

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

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

The moule will inclue a coverage of the technological progress:

History: Sanger sequencing through array technologies

Next generation Sequencing

Avance library construction proceures for specialize assays, incluing ChIP, DNase, ATAC, HiC, eCLIP, an others

This moule will also aress specific fiels of Classical Genetics, Population Genetics an Cancer Genomics. It will involve a biological, technological an analytical imension to help you esign the best experiment with the appropriate ata type an 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、 na 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 en of the moule you shoul be able to:

Unerstan the biological interpretation of the various *omics fiels, especially DNA, RNA an Methylation base.

Unerstan 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 ata generate in the fiel both with comman line an web interface such as Galaxy

Integrate the various type of ata to unerstan the biological implication of the results

Deal with the complexity of information available to enable the integration of iverse ata 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 creits) 数据分析 , 统计机器学习

By the en of the moule you will be able to:

Demonstrate a goo unerstaning of complexity of omics an clinical ata an their management incluing their semantic representation

Demonstrate an in-epth unerstaning an ability to perform Data integration, mining an analysis

Demonstrate conceptual unerstaning of Computing, Algorithmic an Programming that enables the stuent to evaluate methoologies an evelop critiques of them an, where appropriate, propose new methos

Deal with the complexity of information available to enable the integration of iverse ata types

Demonstrate self irection an originality in tackling an solving problems to perform the appropriate Moelling an 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 an avance (omics) technologies (20 creits)

代谢组学和高级（组学）技术

By the en of the moule you will be able to:

Demonstrate a conceptual unerstaning of metabolomics, biological imaging an other avance bioscience technologies.

Demonstrate a conceptual unerstaning of the major challenges facing metabolomics, biological imaging an other avance bioscience technologies.

Demonstrate a conceptual unerstaning of a typical bioinformatics workflow to process an analyse metabolomics atasets.

Perform basic bioinformatics ata analysis an extract biological insight from large metabolomics ata 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 ban).