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MBG 326

Introduction to Bioinformatics

MBG 326 is where molecular biology meets the command line: you learn to treat sequences, expression matrices, and genomes as data you can actually query, align, and model rather than just memorize. Most of the work happens in R, weekly labs and five homeworks walk you through tidyverse, ggplot2, clustering, PCA, and eventually a full RNA-seq project, scaffolded by Akalin's Computational Genomics with R. It's the course that turns the wet-lab intuition from earlier MBG classes into the computational fluency you'll need for genomics electives, thesis work, or anything downstream of a sequencer.

Kredi3ECTS5FakülteFaculty of ScienceBölümMolecular Biology and GeneticsKoordinatörÖzlen Konu Karakayalı

→ STARS müfredatı (resmi syllabus)

Haftalık müfredat 14 hafta

Hafta 114–20 Eyl

Hücre, DNA ve protein sentezine giriş

Syllabus; Introduction: cells; DNA, RNA, protein: genetics, replication, transcription, translation. Book: Computational Genomics with R, Chapter 1. Additional Reviews.

DNAreplicationtranscriptionComputational Genomics with R

Hafta 221–27 Eyl

R programlama temelleri laboratuvarı

R programming basics lab: scalars, vectors, matrices, lists and indexing. Computational Genomics with R, Chapter 2.1-2.6.

vectorsmatriceslistsindexing

Hafta 328 Eyl – 4 Eki

R, Rmarkdown ve genomik veritabanları

HOMEWORK 1a: Introduction to R and Rmarkdown; R programming basics: basic plotting and ggplot2; Databases: NCBI, Ensembl, UCSC, BLAST; sequence alignment; Computational Genomics with R, Chapter 2.6-2.9; Molecular Biology and Bioinformatics Article Readings on Moodle

Rmarkdownggplot2NCBI/Ensembl/UCSC/BLASTsequence alignment

Hafta 45–11 Eki

Quiz 1: R'a giriş ve genomik istatistik

QUIZ 1. Introduction to R (vectors, scalars, matrices, lists, factors and basic stats and plotting); Computational Genomics with R, Chapter 3: Statistics for Genomics

Rvector/matrix/listComputational Genomics with R Bölüm 3

Hafta 512–18 Eki

tidyverse: ggplot2 ve dplyr

HOMEWORK Ib: tidyverse; R programming basics lab: ggplot2 and dplyr; Molecular Biology and Bioinformatics Article Readings on Moodle

tidyverseggplot2dplyr

Hafta 619–25 Eki

Quiz 2: dplyr, ggplot2 ve clustering

QUIZ 2: dplyr, ggplot2; R programming basics:I ntermediate R (conditionals, loops and apply); Clustering and Dimension Reduction. Comparative Genomics with R, Chapter 4.

dplyrggplot2clusteringdimension reduction

Hafta 726 Eki – 1 Kas

Unsupervised clustering ve R fonksiyonları

HOMEWORK II: Unsupervised clustering; Molecular Biology and Bioinformatics Article Readings on Moodle; programming basics lab: Writing Functions in R;

unsupervised clusteringwriting functions in R

Hafta 82–8 Kas

Midterm I, k-medoids ve PCA

MIDTERM I (writen/essay-closed book, weeks 1-8); kmedoids, PCA

closed book midtermk-medoidsPCAhafta 1-8

Hafta 99–15 Kas

Intermediate R ve Bölüm 5

HOMEWORK III: intermediate R; Computation Genomics with R, Chapter 5.

intermediate RComputational Genomics with R Bölüm 5

Hafta 1016–22 Kas

Machine learning ve prediction

HOMEWORK IV: Machine Learning and Prediction; Readings from molecular biology and bioinformatics literature

machine learningprediction

Hafta 1123–29 Kas

Quiz 3: limma ve unsupervised learning

QUIZ 3: limma and unsupervised learning; Genomics with R, Chapter 7; Sequencing and QC

limmaunsupervised learningsequencingQC

Hafta 1230 Kas – 6 Ara

Midterm II, machine learning ve MultiOmics

MIDTERM II (written/essay-closed book, weeks 7-12); Machine Learning, Reading MultiOmics

Midterm IImachine learningMultiOmicshafta 7-12

Hafta 137–13 Ara

RNAseq analizi ve next generation sequencing

HOMEWORK V: RNAseq analysis; Next Generation Sequencing and applications. Computational Genomics with R, Chapter 8. RNAseq preprocessing and analyses and annotation on RNAseq analyses and Advanced Topics; RNAseq project and presentation prep

RNAseqnext generation sequencingpreprocessingannotation

Hafta 1414–20 Ara

Quiz 4: RNAseq ve machine learning

QUIZ4: RNAseq and machine learning; Machine learning; RNAseq project data selection and prep for presentation

RNAseqmachine learningproje veri seçimisunum hazırlığı

Değerlendirme 95% · 5 adım

10%

20%

25%

20%

Homework Datacamp assignments and lab attendance 10%

Midterm:Essay/written Midterm I, Midterm II 40%

Final:Essay/written Final Exam (Comprehensive) 25%

Quiz Moodle or in class Quiz on lab material/worksheets 20%

sınav ağırlığı %89 · 19 dönem ortalaması 3.00 (1014 öğrenci) nasıl hesaplanıyor

Önerilen kaynaklar 2 kitap

📕

Zorunlu

Weekly readings will be announced Required - Web Link: datacamp

📕

Zorunlu

Computational Genomics with R

Altuna Akalin

1st edition · selected chapters

Bu dersi alınca · 7 öğrenme çıktısı

assess different molecular data types, e.g., DNA/protein sequences, RNA expression, protein structure, interaction dataMidterm I, Final Exam (Comprehensive), Midterm II, Moodle or in class Quiz on lab material/worksheets
evaluate online genome databasesMidterm I, Moodle or in class Quiz on lab material/worksheets
apply sequence alignment, phylogeny, motif search techniquesMidterm I, Moodle or in class Quiz on lab material/worksheets
analyze gene expression data and solve relevant problemsFinal Exam (Comprehensive), Moodle or in class Quiz on lab material/worksheets
practice R programming in relationship to biological dataDatacamp assignments and lab attendance, Final Exam (Comprehensive), Midterm II, Moodle or in class Quiz on lab material/worksheets
construct an R program for a biological problemDatacamp assignments and lab attendance, Moodle or in class Quiz on lab material/worksheets
Identify, formulate, solve biological problems and utilize computers and technical/scientific software including bioinformatics approaches.Datacamp assignments and lab attendance, Midterm I, Final Exam (Comprehensive), Midterm II, Moodle or in class Quiz on lab material/worksheets

Bilkent'in resmî syllabus'ünden. Sağdaki etiket o çıktının hangi değerlendirmeyle ölçüldüğünü söylüyor.

Ders notları · henüz yok

MBG 326 için defter ekibi henüz not yazmadı.

İlk dosyayı sen atarsan: not, slayt, geçmiş sınav, çözüm, cheat-sheet, ne varsa. defter ekibi öğrenci paylaşımlarından bu dersin notlarını yazar. Drive linki / PDF / ZIP, hepsi olur.

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Geçmiş GPA dağılımı 19 dönem · ort. 3.00

Dönem	Course CPA
2025-2026 Fall	3.02	1 sec · 58 öğr
2024-2025 Fall	2.89	1 sec · 63 öğr
2023-2024 Fall	3.09	1 sec · 53 öğr
2022-2023 Fall	3.41	1 sec · 43 öğr
2021-2022 Fall	2.64	1 sec · 35 öğr
2020-2021 Fall	3.25	1 sec · 65 öğr
2019-2020 Fall	2.75	1 sec · 66 öğr
2018-2019 Fall	2.73	1 sec · 59 öğr
2017-2018 Fall	2.88	2 sec · 85 öğr
2016-2017 Fall	2.87	1 sec · 52 öğr

Aggregate course GPA · Bilkent STARS'tan public data. Hoca-bazlı per-section detayı için STARS evaluation report →. Öğrenci anket cevapları KVKK kapsamında defter'de tutulmaz. Tüm derslerin ortalamaları →

Bu dönem · 2026-2027 Güz · 1 şube · 55 kontenjan

haftada 3 saat ders

Haftalık ızgarada 4 saat görünüyor, kayıt sistemi dersi 3 saat yazıyor. Aradaki 1 saat yedek saat: Bilkent programlarında derse genelde bir fazla saat ayrılır ve çoğu zaman kullanılmaz. Hangisinin düşeceği dönem başlayınca belli olur.

Özlen Konu Karakayalı

Çar08:30–10:20Cum13:30–15:20

kişilik

programıma ekle → CGPA'ya ekle kayıt tarihleri

Genelde sadece güzSon yıllarda 22 kez güz döneminde açılmış, 6 kez bahar. Bu dönem alamazsan bir sonraki güzü beklemen gerekebilir.

⚠️ FZ engelleyen şartlar

to have two midterm grades; IMPORTANT NOTE: this course is BIOLOGICAL in nature. There will be NO CHEATSHEETS provided in midterms or the final. There will be at least one hour/week lab hour that you need to attend. This course requires a VERY GOOD understanding of molecular biology and genetics and their applications. There will be biological research articles that use bioinformatics for additional reading, which will be on the exams.

Hocalar 1 bu dönem · 1 geçmiş

Bu dönem (2026-2027 Fall) · 1 section

Özlen Konu Karakayalı

Geçmişte ders veren (1 kişi)

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