課程資訊
課程名稱
生物資訊學
Bioinformatics 
開課學期
110-2 
授課對象
生命科學院  基因體與系統生物學學位學程  
授課教師
林友瑜 
課號
GenSys5020 
課程識別碼
B48 U0380 
班次
 
學分
3.0 
全/半年
半年 
必/選修
選修 
上課時間
星期二2,3,4(9:10~12:10) 
上課地點
生科4A 
備註
本課程以英語授課。
總人數上限:30人
外系人數限制:15人 
 
課程簡介影片
 
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課程概述

This course will put equal emphasis towards lecture and hands-on experience. Most weeks will include classroom lecture followed by computer lab time. There will be several homework projects assigned throughout the course. Office hours and additional lab time will be available for students who have questions regarding the lecture or lab portion of the course. Server accounts will be provided at the beginning of the course and rescinded upon completion of the course. 

課程目標
This course is designed to introduce bioinformatics to students of biology background. This course will cover an introduction, signature developments and breakthroughs, current and future emphasis of the field of bioinformatics. Working concept, underlying algorithms, and practical usage of mainstream bioinformatics tools will also be covered. Computer programming (Linux, R, Python, Excel) at a beginners level will also introduced. Upon completion of the course, students should gain a more comprehensive understanding of the basic concepts, analytic methods, and applicable tools of bioinformatics. Students should also become more comfortable handling vast amounts of biological
and genomic data with bioinformatics tools that will be essential to their work. 
課程要求
Grades will be based on attendance (5%), project assignments (45%), midterm exam (20%), and final exam (30%).
Some background in statistics and genetics is preferred but not required. No computer science prerequisites for this course. 
預期每週課後學習時數
 
Office Hours
 
參考書目
Main textbook:
Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins.
Baxevanis et al. 4th edition.
Supplementary textbook:
1. Exploring Bioinformatics: A Project-Based Approach: A Project-Based Approach. Clair et al. 2nd edition.
2. Bioinformatics with Python Cookbook: Learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology. Antao. 2nd edition.
3. Learning R: A Step-by-Step Function Guide to Data Analysis. Cotton. 1st edition.
4. Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools. Buffalo. 1st edition.
5. Molecular and Genome Evolution. Graur. 1st edition. 
指定閱讀
 
評量方式
(僅供參考)
   
課程進度
週次
日期
單元主題
無資料