Akademik Dersler - Tanıtım

Course Introduction Information


Code - Course Title	BİY5504 - Genomic Data Analysis in Molecular Biology
Course Type	Elective Courses
Language of Instruction	Türkçe
Laboratory + Practice	3+0
ECTS	7.5
Course Instructor(s)	ARAŞTIRMA GÖREVLİSİ DOKTOR BURAK BERBER
Mode of Delivery	Face to Face
Prerequisites	There are no prerequisites or co-requisites for this course.
Courses Recomended	There is no recommended optional programme component.
Required or Recommended Resources	Books1. Lesk AM. Introduction to Genomics.2. Pevsner J. Bioinformatics and Functional Genomics.3. Xiong J. Essential Bioinformatics.4. Brown TA. Genomes.5. Mount DW. Bioinformatics: Sequence and Genome Analysis.6. Campbell AM, Heyer LJ. Discovering Genomics, Proteomics, and Bioinformatics.7. Buffalo V. Bioinformatics Data Skills.
Recommended Reading List	Articles / Recommended Readings1. Recent review articles on next-generation sequencing technologies and biomedical applications2. Applied articles on genomic data quality control and preprocessing3. Resources explaining GATK-based variant calling workflows4. Foundational articles on RNA-seq differential gene expression analysis5. Review articles on pathway analysis and gene set enrichment analysis6. Guideline articles on clinical genomic data interpretation and reporting7. Recent publications on ethics, privacy, data sharing, and reproducibility in genomic research
Assessment methods and criteria	1 Midterm exam 1 Final exam
Work Placement	No compulsory work placement is required for this course. Practical components are integrated through genomic data analysis exercises, quality control report interpretation, variant annotation tasks, gene expression analysis examples, pathway analysis activities, data visualization assignments, and final project presentations.
Sustainability Development Goals

Content

Weeks	Topics
Week - 1	Introduction to Genomic Data Analysis Content: • Definition and scope of genomic data analysis • The role of genomic approaches in molecular biology • Concepts of genome, transcriptome, epigenome, and metagenome • Use of omics technologies in biomedical research • Basic concepts in genomic data analysis • Relationship between experimental data and bioinformatics analysis • Developing biological questions for genomic data analysis
Week - 2	Genomic Data Generation Technologies Content: • Introduction to next-generation sequencing technologies • Short-read and long-read sequencing approaches • Overview of Illumina, Ion Torrent, PacBio, and Oxford Nanopore technologies • DNA-seq, RNA-seq, ChIP-seq, ATAC-seq, metagenomics, and single-cell sequencing data types • Targeted panels, exome sequencing, and whole-genome sequencing • Advantages and limitations of sequencing platforms • Selection of technology according to the research question
Week - 3	Genomic Experimental Design and Sample Planning Content: • Developing research questions and hypotheses in genomic studies • Determination of sample groups • Selection of control groups • Biological and technical replicates • Randomization and batch effect • Sample size and statistical power • Effects of DNA/RNA quality criteria on experimental design • Planning clinical and biological metadata
Week - 4	Raw Data Formats and Data Organization Content: • FASTQ, FASTA, SAM, BAM, CRAM, BED, GTF/GFF, and VCF file formats • Concepts of reads, base quality, Phred score, and Q-score • Reference genome and genome annotation files • File naming and folder organization • Preparation of metadata files • Distinction between raw data, processed data, and analysis outputs • Data storage, backup, and traceability in genomic studies
Week - 5	Raw Data Quality Control and Preprocessing Content: • Principles of quality control in raw sequencing data • Interpretation of FastQC and MultiQC outputs • Per-base sequence quality • GC content • Adapter contamination • Duplication level • Overrepresented sequences • Read trimming and filtering • Overview of Cutadapt, Trimmomatic, and similar tools • Interpretation of quality control reports in the experimental context
Week - 6	Reference Genome Alignment and Alignment Evaluation Content: • Principles of alignment to the reference genome • Differences between DNA-seq and RNA-seq alignment • Overview of alignment tools such as BWA, Bowtie2, HISAT2, and STAR • Mapping rate, properly paired reads, insert size, and coverage • Structure of SAM/BAM files • Basic operations using SAMtools • Marking PCR duplicates • Alignment errors and the impact of low-quality alignment on results
Week - 7	Variant Calling Analyses Content: • Differences between germline and somatic variant calling • Detection of SNVs and indels • Concepts of coverage, allele depth, and variant allele frequency • Overview of the GATK workflow • Variant calling quality metrics • Causes of false-positive and false-negative variant calls • Introduction to CNV and structural variant analyses • Technical limitations in variant calling
Week - 8	Variant Annotation and Clinical/Biological Interpretation Content: • Interpretation of VCF files • Concept of variant annotation • Annotation at gene, transcript, and protein levels • Prediction of functional effects • Evaluation of population frequency • ClinVar, gnomAD, dbSNP, OMIM, and COSMIC databases • Overview of annotation tools such as VEP, ANNOVAR, and SnpEff • Variant prioritization strategies • Basic principles in germline and somatic variant interpretation
Week - 9	Introduction to RNA-seq Data and Gene Expression Analysis Content: • Basic principles of RNA-seq technology • Total RNA-seq, mRNA-seq, and small RNA-seq approaches • RNA quality control and RIN value • Concepts of read count, transcript abundance, and expression matrix • Gene-level and transcript-level analyses • Overview of STAR, HISAT2, featureCounts, Salmon, and kallisto approaches • Concept of normalization • Differences between TPM, FPKM, and raw counts
Week - 10	Differential Gene Expression Analysis Content: • Concept of differential gene expression • Expression comparison between experimental groups • Overview of DESeq2, edgeR, and limma-voom approaches • Log2 fold change, p-value, and adjusted p-value • Multiple testing correction • Interpretation of volcano plots, MA plots, and heatmaps • Biological interpretation of upregulated and downregulated genes • Batch effects and confounding factors in differential expression analysis
Week - 11	Functional Annotation, Pathway Analysis, and Systems Biology Functional Annotation, Pathway Analysis, and Systems Biology
Week - 12	Genomic Data Visualization and Reporting Content: • Principles of genomic data visualization • PCA plots, volcano plots, heatmaps, bar plots, and scatter plots • Visual evaluation of genomic variants using IGV • Coverage plots and genome browser usage • UCSC Genome Browser and Ensembl Genome Browser • Preparation of publication-quality figures • Preparation of analysis reports • Communicating bioinformatics analysis results to experimental biologists
Week - 13	Biomedical Applications of Genomic Data Analysis Content: • Genomic data analysis in inherited diseases • Cancer genomics and somatic variant analysis • Discovery of transcriptomic biomarkers • Pharmacogenomic data analysis • Infectious diseases and metagenomic analysis • Liquid biopsy and cfDNA analyses • Personalized medicine applications • Clinical genomic reporting approaches • Use of genomic data in translational research
Week - 14	Ethical Principles, Data Security, Reproducibility, and Student Presentations Content: • Ethical principles in genomic data analysis • Personal genetic data and privacy • Informed consent • Data sharing and open science • FAIR data principles • Reproducibility in genomic analyses • Pipeline documentation and version control • Artificial intelligence-supported genomic data analysis • Student project presentations • General course evaluation

Learning Activities and Teaching Methods

Teaching Methods
Lecture
Discussion
Question & Answer
Problem Solving
Brain Storming
Report Preparation and/or Presentation
Proje Design/Management
Competences
Productive
True to core values
Rational
Questoning
Entrepreneur
Creative
Follow ethical and moral rules
Abstract analysis and synthesis
Problem solving
Information Management
Organization and planning
To work in interdisciplinary projects
Project Design and Management

Assessment Methods

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
Toplam (%)		0