ENS210 - Fall 2025

Instructor

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	Tuesday	12:40pm-14:30pm	FENS-L038
Yagmur Sozeri	yagmur.sozeri@sabanciuniv.edu	Thursday	9:40pm-11:30pm	FENS-L038
Cem Azgari	cemazgari@sabanciuniv.edu	Thursday	12:40pm-14:30pm	FENS-L038
Veysel Ogulcan Kaya	vogulcan@sabanciuniv.edu	Thursday	14:40am-16:30pm	FENS-L038

Learning Assistants

LA	E-mail
Neval Yaprak	neval.yaprak@sabanciuniv.edu

Class hours

Wed 14:40-16:30 FENS-L030 (lectures)
Wed 16:40-17:30 FENS-G032 (prelab)
Wed 17:40-19:30 FENS-G032 (lab)

Content

ENS210 - Fall 2025

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? Do you know why you and the annoying fly buzzing in the middle of the night are unique? Why does a diet work well for you but not for others? It’s all about the genome! If the genome contains so much information, why can’t we design personalized drugs, diets, or treatments? It’s because we don’t fully understand what the code exactly means. Identifying the code is no longer the main challenge; analyzing it is. In this course, we will learn the basics of computational genomics with the goal of understanding bioinformatics applications. We will explore using publicly available tools and writing custom Python scripts to answer biological questions. More details about the content, grading, and policies can be found below.

Learning objectives

Explain why bioinformatics is necessary today.
Use UNIX environment to parse genome data files.
Write Python scripts to perform basic DNA and protein sequence analyses.
Find hypothetical genes in a given DNA sequence.
Synthesize protein sequence with a given DNA sequence.
Use regular expressions to find protein motifs and visualize them on protein structure.
Understand what homology is, how homology information can be used in protein function identification.
Build and interpret multiple sequence alignments.
Build, visualize and analyze phylogenetic trees.
Understand what protein domains are and how they are predicted with a given protein sequence.
Know a variety of NGS methods and what they are designed for.
Build NGS analysis pipelines.

Requirements and expectations

There is no official textbook for the class. Slides will be made available after each class. In order to be successful the best way is in-class learning and taking notes.
Being active in lectures and lab sessions is encouraged.
There is NO stupid question. Do not hesitate to ask any question.
Bring a laptop to every class and lab.
Late work will not be accepted.
Lab work should be completed within lab hours. The assignment system will have a firm deadline unless your instructor (or TA) agrees that extra time is required. If extra time is given, the new due date will be midnight. Therefore, please arrange your program accordingly.

Honesty

All the work should be completed personally unless stated otherwise.
Plagiarism will NOT be tolerated. This does not mean that you are disallowed to use the internet. However, you may not copy and paste any code from the internet. You need to cite the references/websites properly whenever you get inspired otherwise your work will be treated as plagiarism.
You are not allowed to share code in any case.

Use of AI in Coursework

In this course, you are encouraged to explore and responsibly use artificial intelligence tools as part of your learning process. These tools can be powerful aids for writing, coding, and problem-solving, but their value depends entirely on how thoughtfully you engage with them. The guiding principle is simple: 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. You should 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.” AI can be a partner in your work, but it cannot replace your own understanding.

Attendance

Attendance is required. If you are not able to attend, send an e-mail to me and state your excuse before the class. 6 (for lectures) or 2 (for labs) unexcused absences will be considered legitimate for grade reduction.
Make-ups are only given for midterms and the final examination. A medical report must be brought.
No make-up will be given for any missed lab.

Academic Integrity

To uphold with Sabanci University Academic Integrity Statement

I will not lie and cheat in my academic work.

I will act (by letting the instructor know) if the academic integrity is compromised.

I will not share the course materials with anyone else.

By being registered in this class, you will be assumed to have accepted the rules written in this syllabus.

Evaluation

No	Component	Weight
1	Lab	10%
2	One-to-one lab review	10%
3	Participation and Quiz	5%
4	Project	10%
5	Midterm	25%
6	Final	40%

1. Lab

Each lab will be evaluated out of 2 points. You will get 0 point if you show no attempt in completing the lab. If you complete the lab but your submission is not fully accurate, you will get 1 point. If your submission is complete and fully accurate, you will get 2 points.

Max total lab points you can get is 20. But, it will be evaluated out of 16. This means that if you get 2 points from 6 of the labs, and get 1 from others, your lab grade will be full.

No make-ups will be given for the missed labs. For the unexcused missed labs no points will be given.

2. One-to-one lab review

This replaces the lab midterm. You can consider one-to-one lab review as an oral exam. You will be asked questions regarding your lab assignments and the codes you have written. If you complete your tasks yourself, and understand each and every aspect of the lab assignments and be able to explain them in this session, you will get full points.

3. Participation and Quiz

Participation points will be given based on your class performance. If you regularly attend and if you are active in the class you will gain full points. Please note that Participations scores will be subjective.

You may receive tophat questions throughout the course. These will be considered as quizes.

Enrol in Tophat

Please go to this link to enrol in the tophat classroom.

4. Project

You are supposed to complete an individual project on a genetic rare disease you pick. Details will be given.

5. Midterm

Midterm will be paper-based. It will cover everything that you will have learnt in the class and labs.

6. Final

Final will be comprehensive. It means that you will be responsible from the entire material starting from the beginning of the semester.

If you miss an exam (midterm or final) or more than two labs you will automatically fail and get NA.

Objections

After the results are announced for each exam, and objection days and time slots will be announced. You will only be able to object on the announced days. If the time slots don’t fit to your schedule, you are supposed request an appointment from the instructor on the same date of the announcement for the objection.

If you miss the objection period, you won’t be given a second chance to see your exam papers.

Letter Grades

The letter grading will be based on the class performance. Curve-based grading will be applied.

There will be no extra homework/project to increase grades at the end of semester. This is not negotiable.

Individual graduation situations are not important, and they won’t change your letter grade at the end of the semester

ANY kind of misconduct including code sharing, plagiarism, cheating etc will NOT be tolerated. You will fail the course. Disciplinary actions will be taken.

Course Plan

The course plan given below is subject to change.

Week #	Date	Topic
1	1 Oct	Course introduction - Introduction to Genomics
		Pre-lab: Git setup
		Lab 0: Introduction to Git - Git setup

2	8 Oct	Introduction to Genomics
		Pre-lab: Introduction to UNIX
		Lab 1: Analyze Files in Linux

3	15 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	22 Oct	Homology
		Pre-lab: Introduction to Python
		Lab 3: Sequence processing in Python

5	29 Oct	Republic Day
		DEADLINE: Project Milestone 1 (by 23:59)

6	5 Nov	Homology - Multiple sequence comparison
		Pre-lab fundamentals of a gene as a unit
		Lab 4: Finding a gene

7	12 Nov	Pairwise sequence comparison
		Pre-lab: Codons
		Lab 5: DNA to Protein
		Homology - Multiple sequence comparison
		DEADLINE: Project Milestone 2 (by 23:59)

8	19 Nov	Midterm

9	26 Nov	Multiple sequence alignment algorithms
		Pre-lab: NCBI BLAST interface
		Lab 6: BLAST

10	3 Dec	Protein Domains and Motifs
		Pre-lab: MSA methods
		Lab 7: Multiple sequence alignment


11	10 Dec	Phylogenetic Trees
		Pre-lab: Mega + Jalview
		Lab 8: Measure conservation

12	17 Dec	NGS methods
		Pre-lab: MEGA + Figtree
		Lab 9: Phylogeneticsm

13	24 Dec	Variant calling
		Lab 10: Cancer Genomics
		DEADLINE: Project final report (by 23:59)
	27 Dec	Code Review

14	31 Dec	Wrap-up / Project presentations

Week 1

IMPORTANT: Please complete this form after you create an account on GitHub.

Lecture Slides

They will be here after the class.

Introduction to the Course - slides
Introduction to Genomics - slides –>
Genome statistics
Why do we need computational biology?
Central Dogma of Biology
Chargaff’s First Parity Rule
Structure of Nucleic Acids DNA and RNA
DNA structure discovery
Genetics and envrionment interaction

Lab-0

Open an account on GitHub (if you do not have one)
Fill in this form.
Install VScode.
In VScode, login with your GitHub credentials.

Setup for the lab

Extra configuration for Git SSH key

Go to the following assignment
Accept the assignment.
Copy the link of your repository
Clone the repo to your local machine with git clone REPOSITORY_LINK
Follow the instructions in the readme.md file in your cloned repository.

Extra Work

Go to the following assignment.
Accept the assignment. Go to the link you get. Read the material, watch videos.

Week 2

Lecture Slides

Introduction to Genomics Part 2 - slides –>
DNA vs RNA
RNA structures
How to predict RNA structure
Sanger sequencing
Gel electrophoresis
Shotgun sequencing
How to calculate the size of a genome in bytes
Next Generation Sequencing
Independent assortment of chromosomes
Meiosis vs Mitosis
Consanguinity

Lab-1

Go to the following assignment
Accept the assignment.
Copy the link of your repository
Clone the repo to your local machine with git clone REPOSITORY_LINK OR directly from VScode with “Clone from Git repository…”.
Follow the instructions in the readme.md file in your cloned repository.

Pre-lab Guide