BIOINFORMATICS

AIM

The course provides an introduction to the field of bioinformatics including key concepts, algorithms, structures and databases, the development of the field historically, its applications and relevant developments in the field. The course covers the basics of bioinformatics sequence analysis and related tools and databases. Topics covered include pairwise alignment, score matrices, sequence database search, biological networks, network analyssi and machine learning techniques, and visualization. The course also an overview of basics of molecular biology, including the concepts of genomes and genes and includes an introduction to genome browsers and central biological databases and knowledge-bases.

CONTENT

This course contains; Introduction to the course material, what is bioinformatics, and why to study bioinformatics,Building the background: Basic concepts in bioinformatics,suffix trees and arrays,Sequence Alignment basics,pairwise sequence alignment,multiple sequence alignment,Databases and database search,Microarray data analysis,Presentations by students lecture/ articles / tools ,Presentations by students lecture/ articles / tools ,Phylogenetic Trees,Machine learning, Network model and graph analysis,Biological networks, visualization and analysis,Project Presentations.

1. 1

   Has a general understanding of central topics and concepts within the field of bioinformatics

   Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

2. 2

   Understands dynamic programming algorithms for alignment of biological sequences

   Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

3. 3

   Understands and be able to explain basics of molecular biology and evolution pertaining to sequence alignment and connect them with the various algorithms

   Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

4. 4

   Is able to compare technical aspects of pairwise local and global sequence alignment algorithm

   Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

5. 5

   Is able to use biological databases and knowledgebases, machine learning and network analysis

   Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

6. 6

   Understanding of basic approaches to biological networks and visualize

   Taught by: Cooperative Learning · Assessed by: Traditional Written Exam, Project Task, Quiz

WEEKLY PLAN

1. WEEK 1

   Introduction to the course material, what is bioinformatics, and why to study bioinformatics

2. WEEK 2

   Building the background: Basic concepts in bioinformatics

3. WEEK 3

   suffix trees and arrays

4. WEEK 4

   Sequence Alignment basics

5. WEEK 5

   pairwise sequence alignment

6. WEEK 6

   multiple sequence alignment

7. WEEK 7

   Databases and database search

8. WEEK 8

   Microarray data analysis

9. WEEK 9

   Presentations by students lecture/ articles / tools

10. WEEK 10

    Presentations by students lecture/ articles / tools

11. WEEK 11

    Phylogenetic Trees

12. WEEK 12

    Machine learning, Network model and graph analysis

13. WEEK 13

    Biological networks, visualization and analysis

14. WEEK 14

    Project Presentations

ASSESSMENT

• Rate of Midterm Exam to Success30%
• Rate of Final Exam to Success70%

WORKLOAD

READING

• "No specific text book, notes will be made available, including in class notes, (sometimes) slides, research papers, book chapters, etc. Recommendaed Reference: Understanding Bioinformatics Marketa Zvelebil & Jeremy O. Baum"

TEACHING STAFF

• Prof.Dr. Reda ALHAJJCOORDINATOR
• Prof.Dr. Reda ALHAJJ