BIN781 - BIOLOGICAL DATABASES and DATA ANALYSIS TOOLS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| BIOLOGICAL DATABASES and DATA ANALYSIS TOOLS | BIN781 | Any Semester/Year | 3 | 0 | 3 | 9 |
| Prequisites | - | |||||
| Course language | English | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Discussion Question and Answer Team/Group Work Preparing and/or Presenting Reports Problem Solving Project Design/Management | |||||
| Instructor (s) | Assoc. Prof. Dr. Tunca DoÄŸan | |||||
| Course objective | The objectives of the course are to teach students about the: basic information on database concepts, relational algebra and entity-relationship diagrams. using SQL to construct a database from scratch and writing queries to retrieve data. the skill to find biological information from available databases, to process/analyse the data and to use available tools to visualize the data. the skill to integrate data from multiple sources. writing and presentation skills. | |||||
| Learning outcomes |
| |||||
| Course Content | An in-depth review of the publicly available software tools and biological databases. Different types of biological data will be introduced and techniques for organization of biological data will be discussed. Also, the course will cover extensive use of web- based bioinformatics environments for investigation and analysis of biological data. | |||||
| References | For Database Concepts, Relational Algebra and Entity Relationship Diagrams, Chapters 1-3, 6,7 in the textbook: Ramez Elmasri & Shamkant Navathe (2010). "Fundamentals of Database Systems" (Sixth edition), Pearson. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction to Biological Databases and Tools, Service/Database/Tool Centres (EMBL-EBI & NCBI) |
| Week 2 | Introduction to Database Concepts and Relational Algebra - 1 |
| Week 3 | Relational Algebra - 2 |
| Week 4 | Entity-Relationship Diagrams and SQL Tutorial |
| Week 5 | SQL Lab - 1 (hand-on exercises) |
| Week 6 | SQL Lab - 2 (hand-on exercises) |
| Week 7 | Genome Databases (Ensembl), Genome Browsers/Tools/Tables: Ensembl and BioMart |
| Week 8 | Protein Sequence Databases (UniProt), Alignment and Visualization Tools (BLAST & Clustal), Nucleotide Databases (GenBank, ENA & DDBJ), Phylogenomic Databases (OMA & TreeFam) |
| Week 9 | Midterm Exam |
| Week 10 | Protein Structure Databases (PDB), Domain & Motif Databases (InterPro & Pfam), Functional Annotation & Enrichment (Gene Ontology & DAVID) |
| Week 11 | Journal Club on Biological Databases and Tools (Student Presentations) |
| Week 12 | Databases for Interactomics and Pathways (KEGG & Reactome), PPI Databases (IntAct & STRING), Network Visualization and Analysis Tools (Cytoscape) |
| Week 13 | Databases for Gene Expression and Analysis (ArrayExpress & GEO), GEO Applications |
| Week 14 | Mutation, Variation and Disease Centric Databases (TCGA & OMIM), Bio-activity and Bio-interaction services (PubChem & ChEMBL) |
| Week 15 | Preparation to final exam |
| Week 16 | Final |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 3 | 30 |
| Presentation | 1 | 10 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 1 | 25 |
| Final exam | 1 | 35 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 5 | 65 |
| Percentage of final exam contributing grade succes | 1 | 35 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 3 | 42 |
| Presentation / Seminar Preparation | 1 | 16 | 16 |
| Project | 0 | 0 | 0 |
| Homework assignment | 3 | 10 | 30 |
| Midterms (Study duration) | 1 | 40 | 40 |
| Final Exam (Study duration) | 1 | 100 | 100 |
| Total Workload | 34 | 172 | 270 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. He/She should use electronical databases; such as Science Direct, PubMed, ISI, published books and periodical publications properly. | X | ||||
| 2. He/She will know basic bioinformatics analysis methods and use them properly in research. | X | ||||
| 3. Since bioinformatics is an interdisciplinary branch of science, he/she will be able to a part of group work, have good communication skills, and will understand others' problems. | X | ||||
| 4. He/She should have internet utilization skills enough to follow the innovations in the field and access desired information, accessing library sources should be advanced. | X | ||||
| 5. He/She will prepare projects for his/her technical-scientific development, provide consultancy service for seminars and genetic analyses, attend article discussions, congresses and workshops. | X | ||||
| 6. /She will be able to follow recent developments in other research fields also. | X | ||||
| 7. He/She will use programming languages, such as R, Phyton and Linux and uses Bioinformatics tools like Bioconductor, BLAST, PLINK, GATK etc. and will know the logic of basic programming. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest