Skip to content

Bioinformatics Exam

When: Oct 3, 2024 at 4:00 pm. Points: 100

2024 Fall Bioinformatics Exam (Key)

Grade statistics

Question statistics

Review guide

This guide covers the major themes of the exam, providing a broad framework for your review. Since the exam is open note, concentrate on developing a deep understanding of the major concepts and approaches, rather than memorizing specific facts.

  • Different Sequencing Approaches: Be familiar with the types of sequencing technologies used in bioinformatics.
  • Read Types: Understand the differences between sequencing reads (e.g., single-end and paired-end) and how they influence downstream analysis.
  • General Workflow Components: Know the steps involved in typical sequencing workflows and common issues that arise.
  • Basic Terminology: Ensure you understand key terms related to genome assembly.
  • General Algorithms: Be aware of the types of algorithms used for assembling genomes.
  • Assembly Evaluation: Know the kinds of metrics used to evaluate genome assemblies and why they are important.
  • Process Overview: Understand what gene annotation entails and how computational tools are used to predict gene function.
  • Gene Features: Be familiar with the typical elements of a gene that are annotated.
  • Types of Alignment: Understand the key differences between global and local alignment methods.
  • Gap Penalty Models: Be aware of how gaps are handled during sequence alignment and why different models exist.
  • Multiple Sequence Alignment (MSA): Know what MSA is used for and how it helps in comparing sequences.
  • RNA-seq Overview: Understand what transcriptomics involves, especially in the context of RNA sequencing.
  • Single-cell vs. Bulk Data: Know the differences between these approaches and why you would use one over the other.
  • Normalization: Be familiar with the idea of normalization in RNA-seq data analysis and why it’s necessary.
  • Basic Mapping Concepts: Know how reads are mapped to reference genomes and the key challenges involved in this process.
  • Suffix Arrays: Understand the role of suffix arrays in bioinformatics.
  • Different Approaches: Understand the general types of alignment strategies.
  • Transcript Quantification: Be aware of the methods used to quantify gene expression levels.
  • Generative Models in RNA-seq: Understand the purpose of generative models.
  • Expectation-Maximization (EM) Algorithm: Be familiar with the general purpose of the EM algorithm.
  • Two-Phase Inference Process: Understand Salmon's distinction between the online and offline phases.
  • Transcript-Fragment Assignment Matrix: Know the role of the assignment matrix in RNA-seq data analysis.
  • Statistical Models: Know why certain statistical models are used to analyze gene expression data.
  • Interpreting Results: Be familiar with general principles for interpreting the significance of changes in gene expression.

Past exams

These are relevant, past exams. Note that in the Spring 2024 semester we had two quizzes instead of one exam.