Difference between revisions of "UNIST Bioinformatics course syllabus"
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Revision as of 20:38, 19 April 2015
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Syllabus
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Course Information Bioinformatics
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Instructor Information Dugwoo Nam & Jong Bhak |
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Course Code |
BIO43101
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Instructor
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Dugwoo Nam,
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Course Title |
Bioinformatics
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Office
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Room 701-8, EB1 (DN) Room 215, Bldg 103 (JB) |
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Year/Semester
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2015/1st
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Telephone
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010 4644 6754 (JB)
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School
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SLS
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E-mail
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dougnam@unist.ac.kr (DN)
jongbhak@gmail.com (JB)
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Course Classification |
TE-BIO/TE-BME/TE-BMS
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Office Hours |
TBA
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Classroom/Class Time |
FRI 13:00-15:30 (NSB S205)
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Grading Type |
Letter grade
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Course Objectives & Description
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Bioinformatics course contains introductory information in how to process biological data using computers. The first part of the course taught by Dougu Nam covers the basic computational methods used for biological data analysis. During the class, students will learn the concepts by short lectures and hands-on experiences. So, please bring a laptop computer to the class from the second week. The second part of the course taught by Jong Bhak will focus on: 1) What Bioinformatics is 2) How to independently read research papers 3) How to critically analyze research papers and topics 4) How to choose a research topic in bioinformatics and design an experiment and perform research 5) How to write research report(paper) to make the results known to the public. 6) The course will be done through active action-oriented learning method(AAO) 7) Students are required to motivate themselves and bring their research topics after reading research papers. |
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Grading
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Midterm( Two tests: 50 %) / Final Exam( Team project: 50%) |
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Text
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Category
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Book and PPT files, on-line links |
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Title
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Bioinformatics
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Author
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Jong Bhak
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ISBN
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Remarks
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Course Reserves Designation |
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References
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Category
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Book, on-line links
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Title
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Bioinformatics
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Author
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Jong Bhak
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ISBN
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Remarks
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Course Reserves Designation |
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Week
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Contents
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01
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Introduction to Bioinformatics, Introduction to Microarray, RNA-seq |
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02
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Microarray & gene data analysis I: Normalization, Identification of DEGs (R language), Gene list analysis with DAVID and STRING(web) |
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03
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Microarray & gene data analysis II: Clustering, (R, cluster, treeview), Dimension Reduction (PCA), survival analysis |
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04
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Test 1
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05
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Gene-set and pathway analysis with GSEA, Introduction to classification analysis (SVM) |
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06
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RNA-seq, ChIP-seq data analysis
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07
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Introduction to NCBI databases: data and literature search |
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08
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Test 2
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09
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Principles of Bioinformatics
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10
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Bioprogramming
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11
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Genomics
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12
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Transcriptomics
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13
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Proteomics
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14
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Epigenomics
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15
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Canceromics and Geromics
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