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| Course Description |
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| Course Name |
: |
Introduction to Bioinformatics |
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| Course Code |
: |
ZO-615 |
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| Course Type |
: |
Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Fall (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
: |
Prof.Dr. ZEYNEL CEBECİ
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| Learning Outcomes of the Course |
: |
The students comprehend the basic issues in bioinformatics,
search for information on bioinformatics resources,
work with gene, genome and protein databases,
understand BLAST algorithms,
query for homologies in nucleotide and protein sequences,
produce phylogenetic charts and graphs,
search genomic, proteomic, taxonomic and bibilographical information for species, genes and/or proteins.
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| Mode of Delivery |
: |
Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
This course aims to teach the bioinformatic information resources; protein and gene databases; and BLAS algorithms for similarity analysis of gene and protein sequences. |
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| Course Contents |
: |
This course includes the bionformatics information resources; BLAST search and interperation for nulcleotide and protein sequences; searching and analysing data in gene, genome and protein databases; and homology search for a specific gene or protein. |
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| Language of Instruction |
: |
Turkish |
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| Work Place |
: |
Classroom |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Introduction to bioinformatics: Terms, concepts and definitions |
none |
none |
|
2 |
Organization of bioinformatics data, Information systems |
none |
none |
|
3 |
Nucleic acids, amino acids, and introduction to protein synthesis |
none |
none |
|
4 |
Genomic and proteomic information resources and systems |
none |
none |
|
5 |
Global, local and multiple aligment algorithms |
none |
none |
|
6 |
BLAST algorithms and Genebank |
none |
none |
|
7 |
Similarity search with BLAST for protein and nucleotid sequences |
none |
none |
|
8 |
Midterm exam |
none |
none |
|
9 |
Phylogenetic trees |
none |
none |
|
10 |
Project works 1 |
none |
none |
|
11 |
Project works 2 |
none |
none |
|
12 |
Project works 3 |
none |
none |
|
13 |
Project works 4 |
none |
none |
|
14 |
Project works 5 |
none |
none |
|
15 |
Preperation for the final exam |
none |
none |
|
16/17 |
Final exam |
none |
none |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Weissig, H. (2005). Computer Skills for Biotechnology. http://www.bioinformaticscourses.com/BIOL358/lectures.html
David Mount. Bioinformatics: Sequence and Genome Analysis, Second Edition
Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids.
Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins. Wiley-Interscience
D. Lopresti (2010). Introduction to Bioinformatics. http://www.lehigh.edu/~inbios21/PDF/Fall2010/Lopresti_10082010.pdf
M Gerstein. Bioinformatics Introduction . http://bioinfo.mbb.yale.edu/mbb452a/intro/
SB Nagl. Introduction to Bioinformatics. http://www.imtech.res.in/raghava/slides/dbase.ppt
N.C. Jones, P.l A. Pevzner (2004) An Introduction to Bioinformatics Algorithms.ISBN-10: 0262101068
T Can (2009). Introduction to Bioinformatics. http://ocw.metu.edu.tr/course/view.php?id=37
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| |
| Required Course Material(s) |
S. Taylor. Introduction to Bioinformatics. http://www.compbio.ox.ac.uk/presentations/CBRGPostGrad2008_ptI.pdf
NCBI - http://www.ncbi.nlm.nih.gov
Weissig H. (2005). Bioinformatics Applications in Target Identification and Validation. http://www.bioinformaticscourses.com/targetid/lecture2005.html
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Assessment Methods and Assessment Criteria |
|
Semester/Year Assessments |
Number |
Contribution Percentage |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
60 |
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Homeworks/Projects/Others |
4 |
40 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
|
Final Assessments
|
100 |
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Rate of Final Assessments to Success
|
60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
At the end of this programme, the students improve and deepen their knowledge in the field of Animal Science by building on the knowledge and competence acquired at the undergraduate level and can employ interdisciplinary interaction in their field of study. |
5 |
|
2 |
The students interpret and generate new information and theories in specific fields related to Animal Science using the theoretical and practical knowledge at masters level. Also, they can reveal the cause-effect relationship regarding the problems in their field of study and employ scientific research methods to generate possible solutions. |
5 |
|
3 |
The students independently identify potential problems and carry out research studies aiming at solutions in the field of Animal Science. Also, they investigate and develop strategic approaches for potential problems that may arise related to the particular studies. |
4 |
|
4 |
The students access and compile information about the latest developments and fundamental sources in the particular field and reach a new synthesis by evaluating and interpreting the existing research. They can make use of this acquired knowledge to practice the profession effectively and follow the improving implementations in the field. |
5 |
|
5 |
The students use information in the field of Animal Science, through compiling, interpreting and synthesising it, in order to make social contributions. They make evaluations by creating a plan and framework and taking specific total quality criteria into consideration. They use the skills and knowledge acquired in the field of Animal Science in joint projects with other disciplines. |
5 |
|
6 |
The students discuss and pass on the acquired knowledge based on their work in the field by making written and oral presentations. They have speaking and writing competence in at least one foreign language at a level that enables them to keep up with the requirements of the age. They express their ideas clearly using the tools of information and communication technologies. |
0 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
13 |
4 |
52 |
|
Out of Class Study (Preliminary Work, Practice) |
5 |
10 |
50 |
| Assesment Related Works |
|
Homeworks, Projects, Others |
4 |
10 |
40 |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
3 |
3 |
|
Final Exam |
1 |
3 |
3 |
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Total Workload: | 148 |
| Total Workload / 25 (h): | 5.92 |
| ECTS Credit: | 6 |
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