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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/bioinformatics-pku
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《生物信息学：导论与方法》课程由北京大学提供，旨在帮助学员熟悉生物信息学的基本概念和计算方法，以及其在生物学中的应用。本课程将为未来的学习和研究提供必要的知识和技能。 课程内容包括： 1. **生物信息学的导论与历史**：学习生物信息学的基本概念及其历史背景，了解该领域的快速发展。 2. **序列比对**：掌握基于动态规划的序列比对算法，理解全局比对和局部比对的不同方法，及其关键的时间复杂度和缺口惩罚原则。 3. **序列数据库搜索**：熟悉常用的序列数据库及BLAST算法，掌握根据研究项目调整BLAST参数的技巧。 4. **马尔可夫模型**：了解状态转移、马尔可夫链及隐藏马尔可夫模型，实践如何进行生物问题的预测。 5. **下一代测序（NGS）**：掌握NGS的特征，了解读取映射和变异调用的方法，以及NGS数据分析的Pipeline。 6. **遗传变异的功能预测**：学习变异预测的原理和工具，如SIFT、Polyphen等，并将其应用于具体的科研项目中。 7. **中期考试**：评估之前学习的内容。 8. **下一代测序：转录组分析和RNA-Seq**：掌握转录组数据的生成与分析算法，为后续模块奠定基础。 9. **非编码RNA的预测与分析**：分析转录组数据中的非编码RNA，识别长非编码RNA及其功能。 10. **本体论和分子通路的识别**：了解基因本体及KEGG通路数据库，学习如何识别通路并应用于药物研究。 11. **生物信息学数据库与软件资源**：熟悉重要的生物信息学资源，包括集中资源和个别资源。 12. **新基因的起源**：通过案例学习应用生物信息学方法研究物种特异性新基因的起源和演化，构建系统发育树。 13. **DNA甲基转移酶的进化功能分析**：利用生物信息学方法研究DNA甲基化酶的功能与进化，分享科研经验。 14. **期末考试**：评估学习成果。 课程提供的材料受CC BY-NC-SA许可证保护。学员将通过本课程为今后的学术和科研道路打下坚实的基础。
Name:Introduction and History of Bioinformatics
Description:Welcome to “Bioinformatics: Introduction and Methods! Upon completion of this module you will be able to: become familiar with the essential concepts of bioinformatics; explore the history of this young area; experience how rapidly bioinformatics is growing. Our supplementary materials will give you a better understanding of the course lectures through they are not required in quizzes or exams
Name:Sequence Alignment
Description:Upon completion of this module, you will be able to: describe dynamic programming based sequence alignment algorithms; differentiate between the Needleman-Wunsch algorithm for global alignment and the Smith-Waterman algorithm for local alignment; examine the principles behind gap penalty and time complexity calculation which is crucial for you to apply current bioinformatic tools in your research; experience the discovery of Smith-Waterman algorithm with Dr. Michael Waterman himself.
Name:Sequence Database Search
Description:Upon completion of this module, you will be able to: become familiar with sequence databse search and most common databases; explore the algoritm behind BLAST and the evaluation of BLAST results; ajdust BLAST parameters base on your own research project.
Name:Markov Model
Description:Upon completion of this module, you will be able to: recognize state transitions, Markov chain and Markov models; create a hidden Markov model by yourself; make predictuions in a real biological problem with hidden Markov model.
Name:Next Generation Sequencing (NGS): Mapping of Reads From Resequencing and Calling of Genetic Variants
Description:Upon completion of this module, you will be able to: describe the features of NGS; associate NGS results you get with the methods for reads mapping and models for variant calling; examine pipelines in NGS data analysis; experience how real NGS data were analyzed using bioinformatic tools. This module is required before entering Module 8.
Name:Functional Prediction of Genetic Variants
Description:Upon completion of this module you will able to: describe what is variant prediction and how to carry out variant predictions; associate variant databases with your own research projects after you get a list of variants; recognize different principles behind prediction tools and know how to use tools such as SIFT, Polyphen and SAPRED according to your won scientific problem.
Name:Mid-term Exam
Description:The description goes here
Name:Next Generation Sequencing: Transcriptome Analysis, and RNA-Seq
Description:Upon completion of this module, you will be able to: describe how transcriptome data were generated; master the algorithm used in transcriptome analysis; explore how the RNA-seq data were analyzed. This module is required before entering Module 9.
Name:Prediction and Analysis of Noncoding RNA
Description:Upon completion of this module, you will be able to: Analyze non-coding RNAs from transcriptome data; identify long noncoding RNA (lncRNA) from NGS data and predict their functions.
Name:Ontology and Identification of Molecular Pathways
Description:Upon completion of this module, you will be able to: define ontology and gene ontology, explore KEGG pathway databses; examine annotations in Gene Ontology; identify pathways with KOBAS and apply the pipeline to drug addition study.
Name:Bioinformatics Database and Software Resources
Description:Upon completion of this module, you will be able to describe the most important bioinformatic resources including databases and software tools; explore both centralized resources such as NCBI, EBI, UCSC genome browser and lots of individual resources; associate all your bioinformatic problems with certain resources to refer to.
Name:Origination of New Genes
Description:Upon completion of this case study module, you will be able to: experience how to apply bioinformatic data, methods and analyses to study an important problem in evolutionary biology; examine how to detect and study the origination, evolution and function of species-specific new genes; create phylogenetic trees with your own data (not required) with Dr. Manyuan Long, a world-renowned pioneer and expert on new genes from University of Chicago.
Name:Evolution function analysis of DNA methyltransferase
Description:Upon completion of this case study module, you will be able to: experience how to use bioinformatic methods to study the function and evolution of DNA methylases; share with Dr. Gang Pei, president of Tongji University and member of the Chinese Academy of Science, the experiences in scientific research and thought about MOOC.
Name:Final Exam
Description:The description goes here
A big welcome to “Bioinformatics: Introduction and Methods” from Peking University! In this MOOC you will become familiar with the concepts and computational methods in the exciting interdisciplinary field of bioinformatics and their applications in biology, the knowledge and skills in bioinformatics you acquired will help you in your future study and research. Course materials are available under the CC BY-NC-SA License.
