欢迎选修北京大学MOOC课程“生物信息学：导论与方法”。 近三十年来，生命科学与计算科学飞速发展。生物信息学是一门生命科学与计算科学的前沿交叉学科。生物信息学产生和迅猛发展的主要推动力来自于新一代测序等高通量技术在生命科学领域越来越广泛的应用。目前，人和很多动植物的基因组已经被测序出来。已知DNA序列的数量已超过20兆亿碱基，每五个月翻一番。这些前所未有的大数据中蕴藏着很多尚不为人知的新发现、新知识，给生命科学研究带来新的历史机遇。但是同时，这些大数据不仅数量巨大、持续呈现指数增长的趋势，而且噪音高，异构程度高。准确、快速地分析这些数据需要先进的计算方法。 生物信息学是一门强大的新技术，是用来分析、存储、搜索海量生物医学数据的信息技术和计算技术。另一方面，生物信息学是一种研究生命科学问题的新方法、新思路，是一种从全基因组出发、从系统水平出发、基于数据整合，提出新假说、发现新规律的研究方法。在这门“生物信息学：导论与方法”网上课程中，我们将向您系统地讲授生物信息学主要概念及方法。课程内容从基础的序列比对开始，循序渐进，围绕深度测序数据分析、计算基因组学、分子通路鉴定等当前研究的前沿热点内容进行介绍与讨论。进一步地，我们会以我们实验室自己开发的算法、软件及数据库为例，告诉您如何开发新的生物信息学技术。我们将用具体实例展示给您，如何用生物信息学的方法及研究思路来解决生命科学里的实际问题。我们也邀请到几位生命科学领域资深教授介绍相关的生物信息学重要工作及生物信息学在生命科学中的作用。
KOBAS演示
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来自 北京大学 的课程
生物信息学: 导论与方法
138 评分
从本节课中
本体论、分子通路鉴定
完成本模块的课程后你将可以：了解本体论和基因本体轮等重要定义；了解KEGG通路数据库；了解GO的注释消息；在药物成瘾研究中学会使用KOBAS进行通路分析。
与讲师见面
Ge Gao 高歌, Ph.D.Assistant Professor, Principle Investigator
Center for Bioinformatics, School of Life Science
Liping Wei 魏丽萍, Ph.D.Professor, Director
Center for Bioinformatics, School of Life Sciences
Welcome back to Peking University MOOC Bioinformatics: Introduction and Methods.
This video is supplementary learning materials 2: KOBAS demo.
Iâm Chen Xie from National Institute of Biological Sciences, Beijing.
Go to KOBAS web page by visiting kobas.cbi.pku.edu.cn. KOBAS has two main programs, Annotate and Identify.
Click Annotate to go to the program page. Annotate annotates input genes to pathways, diseases, and GO terms.
Let us try the Gene ID example.
Input file can be a file on KOBAS web server,
content in the following clipboard,and a file from local disk.
It is content in the clipboard in this example.
Input file can be a file on KOBAS web server, content in the following clipboard, and a file from local disk.
Besides that, you need to choose the species that you want to annotate with, and it is human in this example.
After filling the location and name of the output file, click Run.
In the result, the first column represents input IDs, and the second and third columns represent mapped KEGG gene IDs and names.
The detailed information about mapped pathways, diseases, and GO terms will be shown after clicking details;
and a web page about other information of a gene on KEGG website will be shown by clicking Gene ID.
The result can be downloaded to local disk.
Then we click Use this file as Identifyâs Input to perform the next analysis on the program page.
Identify performs enrichment analysis for pathways, diseases, and GO terms.
After clicking Show available databases for this sample file,
we can choose databases. Here, due to running time limitation, we only choose four databases.
The background for statistical test is the annotation result of Annotate for background gene set containing the input genes;
and could also be default background, which is the annotation of genes from whole genome.It is default background in this example.
You can choose statistical method and FDR correction method, and set small term cutoff in Options for statistics.
After filling the location and name of the output file, click Run.
In the result, the first three columns are the name, database, and ID of a term;
The fourth column is the input number of that term, and the fifth column is the background number;
the sixth and seventh columns are P-Value and Corrected P-Value separately.
The information of the term in the original database can be accessed by clicking ID;
and genes mapped to that term will appear after clicking input number.
Registered users can save data and analysis history on KOBAS web server.
Users can also download KOBAS standalone command line version, and install and run locally.
The following demo for running command line programs gets identical results as above ones.
