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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/bioinformatics-methods-1
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课程名称：生物信息学方法 I 本课程综述：大型生物学项目（例如人类基因组测序和基因表达调查）产生了丰富的生物数据，生物学家面临的挑战是如何分析和访问这些数据，以提取与研究系统相关的有用信息。本课程专注于利用现有的生物信息学资源（主要是基于网络的程序和数据库），以此来访问这些海量数据，从而回答生物学家的相关问题，强调实际操作。 课程内容包括多个序列比对、系统发育学、基因表达数据分析和蛋白质相互作用网络，分为两部分： 第一部分生物信息学方法 I（本课程）涵盖数据库、BLAST、多个序列比对、系统发育学、选择分析和宏基因组学。 第二部分生物信息学方法 II 涉及基序搜索、蛋白质-蛋白质相互作用、结构生物信息学、基因表达数据分析和顺式元件预测。 这对课程适合考虑生物科学研究生学位的学生以及考虑分子医学的学生，提供了多种现有的生物信息学工具概述。 本课程基于多伦多大学为具有基本分子生物学理解的高年级本科生教授的课程。如果您不熟悉相关内容，类似于 https://learn.saylor.org/course/bio101 的课程可能会有所帮助。本课程不要求编程基础。 生物信息学方法 I 定期更新，并于2022年1月进行了全面更新。 课程大纲： 1. **NCBI/BLAST I**：探索美国国家生物技术信息中心的资源，并进行BLAST搜索以查找相似序列。 2. **BLAST II/比较基因组学**：继续探索NCBI的资源，进行多种类型的BLAST搜索，并比较不同物种的基因组部分。 3. **多个序列比对**：使用Clustal、MUSCLE和MAFFT进行多个序列比对，以识别保守和变异区域。 4. **系统发育学**：利用生成的多个序列比对进行邻接法和最大似然法的系统发育分析。 5. **选择分析**：分析细菌的正、负或中性选择位置，以帮助理解蛋白质编码序列的生物学。 6. **下一代测序分析（RNA-Seq）/宏基因组学**：探索由于测序成本迅速下降而生成的数据，分析RNA-Seq数据集和宏基因组数据集。 7. **复习与期末作业**：对系统发育学、选择分析及下一代测序分析进行复习，并进行期末任务。 本课程将为学习者提供实用的生物信息学技能和知识，支持他们在生物科学领域的进一步研究和应用。
Name:NCBI/Blast I
Description:In this module we'll be exploring the amazing resources available at NCBI, the National Centre for Biotechnology Information, run by the National Library of Medicine in the USA. We'll also be doing a Blast search to find similar sequences in the enormous NR sequence database. We can use similar sequences to infer homology, which is the primary predictor of gene or protein function.
Name:Blast II/Comparative Genomics
Description:In this module we'll continue exploring the incredible resources available at NCBI, the National Centre for Biotechnology Information. We will be performing several different kinds of Blast searches: BlastP, PSI-Blast, and Translated Blast. We can use similar sequences identified by such methods to infer homology, which is the primary predictor of gene or protein function. We'll also be comparing parts of the genomes of a couple of different species, to see how similar they are.
Name:Multiple Sequence Alignments
Description:In this module we'll be doing multiple sequence alignments with Clustal and MUSCLE (as implemented in MEGA), and MAFFT. Multiple sequences alignments can tell you where in a sequence the conserved and variable regions are, which is important for understanding the biology of the sequences under investigation. It also has practical applications, such as being able to design PCR primers that will amplify sequences from a number of different species, for example.
Name:Review: NCBI/Blast I, Blast II/Comparative Genetics, and Multiple Sequence Alignments
Description:
Name:Phylogenetics
Description:In this module we'll be using the multiple sequence alignments we generated last lab to do some phylogenetic analyses with both neighbour-joining and maximum likelihood methods. The tree-like structure generated by such analyses tells us how closely sequences are related one to another, and suggests when in evolutionary time a speciation or gene duplication event occurred.
Name:Selection Analysis
Description:In this module we'll take a set of orthologous sequences from bacteria and use DataMonkey to analyze them for the presence of certain sites under positive, negative or neutral selection. Such an analysis can help understand the biology of a set of protein coding sequences by identifying residues that might be important for biological function (those residues under negative selection) or those that might be involved in response to external influences, such as drugs, pathogens or other factors (residues under positive selection).
Name:'Next Gen' Sequence Analysis (RNA-Seq) / Metagenomics
Description:In this module we'll explore some of the data that have been generated as a result of the rapid decrease in the cost of sequencing DNA. We'll be exploring a couple of RNA-Seq data sets that can tell us where any given gene is expressed, and also how that gene might be alternatively spliced. We'll also be looking at a couple of metagenome data sets that can tell us about the kinds of species (especially microbial species that might otherwise be hard to culture) that are in a given environmental niche.
Name:Review: Phylogenetics, Selection Analysis, and 'Next Gen' Sequence Analysis (RNA-seq)/Metagenomics + Final Assignment
Description:
Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on. Topics covered include multiple sequence alignments, phylogenetics, gene expression data analysis, and protein interaction networks, in two separate parts. The first part, Bioinformatic Methods I (this one), deals with databases, Blast, multiple sequence alignments, phylogenetics, selection analysis and metagenomics. The second part, Bioinformatic Methods II, covers motif searching, protein-protein interactions, structural bioinformatics, gene expression data analysis, and cis-element predictions. This pair of courses is useful to any student considering graduate school in the biological sciences, as well as students considering molecular medicine. Both provide an overview of the many different bioinformatic tools that are out there. These courses are based on one taught at the University of Toronto to upper-level undergraduates who have some understanding of basic molecular biology. If you're not familiar with this, something like https://learn.saylor.org/course/bio101 might be helpful. No programming is required for this course. Bioinformatic Methods I is regularly updated, and was completely updated for January 2022.
