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2016　Computational Biology

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Academic unit or major
Graduate major in Life Science and Technology
Instructor(s)
Sakurai Minoru  Itoh Takehiko  Kurokawa Ken  Yamada Takuji  Kotera Masaaki 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
Mon1-2(J221,W831)  Thr1-2(J221,W831)  
Group
-
Course number
LST.A408
Credits
2
Academic year
2016
Offered quarter
3Q
Syllabus updated
2016/4/27
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

How deep knowledge or useful information can we retrieve from diverse and enormous data obtain from multi-omics analysis? This course forcuses on Bioinformatics. Topics includes molecular evolution, sequence analysis, comparative genomics, multi-omics analysis, algorithms for bioinformatics, molecular or metabolic network analysis, and data mining methods. By combining lectures and exercises, the course enables students to understand and acquire the fundamentals of bioinformatics widely applicable to biological research. Bioinformatic approaches taught in this course are not only useful in analyzing multi-omics data, but are applicable to various other types of biological problem.

Student learning outcomes

By the end of this course, students will be able to:
1) Understand principles and methods of sequence analysis based on molecular evolution
2) Understand the knowledge obtained by comparing the gene sequences and genomic sequences
3) Understand computer algorithms in bioinformatic analyses
4) Understand the fundamentals and applications of multi- omics analysis

Keywords

Bioinformatics

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Required learning should be completed outside of the classroom for preparation and review purposes.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction to computational biology Understand the outline of bioinformatics
Class 2 Introduction of molecular evolution Understand fundamentals of molecular evolution
Class 3 Fundamentals of molecular evolution for amino acids Understand fundamentals of amino acid sequence analysis method
Class 4 Fundamentals of molecular evolution for nucleic acids Understand fundamentals of nucleic acid sequence analysis method
Class 5 Fundamentals of sequence analysis and genome analysis Understand fundamentals of genome analysis
Class 6 Comparative genomics Understand fundamentals of comparative genomics
Class 7 Multi-omics analysis Understand fundamentalsl of multi-omics analysis
Class 8 Computational algorithms 1 (Greedy algorithm) Understand the greedy algorithm
Class 9 Computational algorithms 2 (Pattern matching alogrithm and the others) Understand the pattern matching algorithm
Class 10 Metabolic pathway analysis 1 (Databases) Understand the metabolic pathway database
Class 11 Metabolic pathway analysis 2 (Graph theory) Understand graph theory
Class 12 Metabolic pathway analysis 3 (Cross-omics) Understand cross-omics analysis
Class 13 Methods for data mining 1 (Multiple classification analysis) Understand the multiple classification analysis
Class 14 Methods for data mining 2 (HMM) Understand the HMM method
Class 15 Methods for data mining 3 (Bayesian statistics) Understand the Bayesian statistics

Textbook(s)

None

Reference books, course materials, etc.

Neil C. Jones and Pavel A. Pevzner. An Introduction to Bioinformatics Algorithms. ISBN-13: 978-0262101066
Masatoshi Nei and Sudhir Kumar. Molecular Evolution and Phylogenetics. ISBN-13: 978-0195135855

Assessment criteria and methods

By written reports for each class.

Related courses

  • None

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None

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