ENS210 - Fall 2026

Instructor

Name: Ogun Adebali

E-mail: oadebali@sabanciuniv.edu

Office: FENS-1055

Office hours: Fri 10.40-11:30 (by appointment only)

Teaching Assistants

TA E-mail Office Day Office Hours Office
Ceren Yildirim ceren.yildirim@sabanciuniv.edu Thursday 14:40pm-16:30pm FENS-L038
Yagmur Sozeri yagmur.sozeri@sabanciuniv.edu Wednesday 9:40am-11:30am FENS-L038
Mustafa Malkoc mustafa.malkoc@sabanciuniv.edu Thursday 9:40pm-11:30pm FENS-L038
Ebrar Dilbaz ebrar.dilbaz@sabanciuniv.edu Wednesday 12:40pm-14:30pm FENS-L038

Class hours

To be announced.

Content

Course Description

Have you ever thought about how the code in each of your cells shapes your physical features, disease risks, and even your behaviors? Why are you and a fruit fly like Drosophila so different — yet so genetically similar? Why does a diet work well for you but not for others? It’s all in the genome.

Identifying the genome is no longer the main challenge — understanding it is. In this course, we will explore the basics of computational genomics and bioinformatics. You will use publicly available tools and write custom Python scripts to answer real biological questions.

How to Succeed

  • Attend every lecture and lab — active participation is the single best predictor of success.
  • Take notes. Slides will be posted after class, but in-class discussion goes beyond the slides.
  • Ask questions freely. There is no such thing as a stupid question in this course.
  • Start lab work early within the session — late work is not accepted.
  • Use AI tools thoughtfully: understand everything you submit and be ready to explain it.

Learning objectives

By the end of this course, students will be able to:

  • Explain why bioinformatics is necessary in modern biology.
  • Use a UNIX environment to parse genome data files.
  • Write Python scripts to perform basic DNA and protein sequence analyses.
  • Identify hypothetical genes in a given DNA sequence.
  • Synthesize protein sequences from a given DNA sequence.
  • Use regular expressions to find protein motifs and visualize them on protein structures.
  • Explain homology and apply it to protein function identification.
  • Build and interpret multiple sequence alignments.
  • Build, visualize, and analyze phylogenetic trees.
  • Describe protein domains and predict them from a given sequence.
  • Identify a variety of next-generation sequencing (NGS) methods and their applications.
  • Build and execute NGS analysis pipelines.

Requirements and expectations

  • There is no required textbook. Slides will be posted after each class.
  • Bring a laptop to every lecture and lab session.
  • Lab work must be completed within lab hours. The assignment system enforces a firm deadline unless your instructor or TA explicitly grants an extension; any approved extension deadline will be set to midnight.
  • Late work will not be accepted without prior approval.
  • Plan your schedule accordingly.

Academic Integrity

Complete all work independently unless group work is explicitly stated.

Plagiarism will not be tolerated. You are welcome to use the internet and AI tools, but you may not copy and paste code or text without understanding it. Cite all references and sources of inspiration properly. Unattributed use of external material will be treated as plagiarism.

Sharing code with other students is not permitted under any circumstances. Any misconduct — including code sharing, plagiarism, or cheating — will result in a failing grade and disciplinary action.

Use of AI in Coursework

You are encouraged to explore and responsibly use artificial intelligence tools as part of your learning process. AI can be a powerful aid for writing, coding, and problem-solving — but its value depends entirely on how thoughtfully you engage with it.

Guiding Principle: You may only use AI-generated material if you fully understand it, can explain it in your own words, and are prepared to take complete responsibility for it. Never submit a sentence, figure, or line of code that you could not have produced yourself without assistance. Think of it this way: anything you include from AI should be something you would confidently sign your name under.

Note: Using AI to answer TopHat questions or in-class quizzes is not permitted, as these are real-time assessments of your own understanding.

Attendance

Attendance is required for both lectures and labs.

  • Missing 12 or more lecture hours will be grounds for failure.
  • Missing 3 or more lab weeks will be grounds for failure.

Make-ups are available only for midterm and final examinations, and only with a medical report. No make-up will be given for missed labs under any circumstances.

Labs

Each lab is worth 2 points, graded as follows:

Points Criteria
0 No meaningful attempt
1 Attempted but incomplete or inaccurate
2 Complete and fully accurate submission

The maximum total lab score is 20 points, but your lab grade will be evaluated out of 16 points (≈ 10% of your final grade). This built-in buffer accounts for lower-scoring labs; it does not excuse absences. Points are not awarded for unexcused missed labs, and no make-ups will be given.

Your instructor or TAs may call you for a review session at any time to verify that you completed and understood your submission.

Participation

Participation points are awarded based on your engagement in class. Regular attendance and active participation will earn full points. Participation scores are evaluated subjectively by the instructor.

TopHat questions may be used throughout the course and will count as quiz grades.

Group Project

You will complete a group project focused on a rare genetic disease of your group’s choosing. More details — including group size, milestones, deliverables, and grading criteria — will be provided during the semester.

Exams

Midterms

All exams are paper-based and cover all material from lectures and labs up to that point.

Final Exam

The final exam is comprehensive and covers all material from the entire semester.

Automatic Failure Conditions

Important: You will automatically receive a failing grade (NA) if you miss a midterm or the final without an approved medical excuse, or if you miss more than two lab sessions. Additionally, your average exam score (midterms and final combined) must be above 40 to pass the course, regardless of other grades.

Grade Objections

After each exam result is announced, specific objection days and time slots will be provided. You may only raise objections during these designated periods.

If the announced time slots do not fit your schedule, you must contact the instructor on the same day the objection period opens to request an appointment. Objections raised after the period closes will not be considered.

When submitting an objection, be prepared to clearly explain which question(s) you are disputing and your reasoning.

Letter Grade Scale

If overall class performance is low, letter grades may be adjusted using a curve based on class average. No additional assignments or extra credit will be offered at the end of the semester. Individual circumstances, including graduation timelines, cannot affect final letter grades.

Guidelines on the “Use of Generative AI” for students

SU Academic Integrity Statement

Health Report Requirements: Student Medical Reports Instruction Letter

Evaluation

No Component Weight Notes
1 Lab 10% Scored out of 16/20 points
2 Participation and Quiz 10%  
3 Project 10%  
4 Midterm (2) 40%  
5 Final 30%  

Course Plan

The course plan given below is subject to change.

Week # Date Topic
1 30 Sep Course introduction - Introduction to Genomics
    Pre-lab: Git setup
    Lab 0: Introduction to Git - Git setup
     
2 7 Oct Lab Setup
    Pre-lab: Introduction to UNIX
    Lab 1: Analyze Files in Linux
     
3 14 Oct What is a gene? From DNA to Protein
    PROJECT description
    Pre-lab: Useful command line tools
    Lab 2: Analyze Genomic Files in Linux
     
4 21 Oct Epigenomics
    Pre-lab: Introduction to Python
    Lab 3: Sequence processing in Python
     
5 24 Oct Make-up lecture for Oct 28 (Republic Day)
    Homology
     
  28 Oct Republic Day — No Class
    DEADLINE: Project Milestone 1 (by 23:59)
     
6 4 Nov Homology - Multiple sequence comparison
    Pre-lab: FASTA format and file handling
    Lab 4: Finding CpG islands
     
7 11 Nov Pairwise sequence comparison
    Pre-lab: Codons
    Lab 5: DNA to Protein
    DEADLINE: Project Milestone 2 (by 23:59)
     
8 18 Nov Midterm
     
9 25 Nov Multiple sequence alignment algorithms
    Pre-lab: NCBI BLAST interface
    Lab 6: BLAST
     
10 2 Dec Protein Domains and Motifs
    Pre-lab: MSA methods
    Lab 7: Multiple sequence alignment
     
11 9 Dec Phylogenetic Trees
    Pre-lab: Mega + Jalview
    Lab 8: Measure conservation
     
12 16 Dec Midterm II
    Pre-lab: MEGA + Figtree
    Lab 9: Phylogenetics
     
13 23 Dec NGS Methods - Variant calling
    Lab 10: Cancer Genomics
    DEADLINE: Project final report (by 23:59)
     
14 30 Dec Wrap-up / Project presentations