APPLIED 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 analysis 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-2,Machine learning, Network model and graph analysis,Phylogenetic Trees,Biological networks, visualization and analysis,Project Presentation.

1. 1

   1. Recognize the central topics and concepts within the field of bioinformatics.

   Taught by: Discussion Method, Self Study Method, Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

2. 2

   2. Uses the dynamic programming algorithms for alignment of biological sequences.

   Taught by: Discussion Method, Self Study Method, Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

3. 3

   3. Compares the technical aspects of the pairwise local and global sequence alignment algorithm.

   Taught by: Discussion Method, Self Study Method, Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

4. 4

   4. Explain the fundamentals of molecular biology and evolution regarding sequence alignment.

   Taught by: Discussion Method, Self Study Method, Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

5. 5

   5. Compare technical aspects of pairwise local and global sequence alignment algorithm.

   Taught by: Discussion Method, Self Study Method, Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

6. 6

   5. Use biological databases and knowledgebases, machine learning and network analysis.

   Taught by: Discussion Method, Self Study Method, Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

7. 7

   7. Makes inferences about central topics and concepts in the field of bioinformatics

   Taught by: Discussion Method, Self Study Method, Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam, Homework, 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-2

11. WEEK 11

    Machine learning, Network model and graph analysis

12. WEEK 12

    Phylogenetic Trees

13. WEEK 13

    Biological networks, visualization and analysis

14. WEEK 14

    Project Presentation

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

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