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Lecture 06B
Read mapping

Methodology

Date: Feb 13, 2025

DRAFT

This page is a work in progress and is subject to change at any moment.

By the end of this lecture, you'll have a comprehensive understanding of transcriptomics' power in unraveling the intricacies of gene expression and its broad applications in biological research.

Learning objectives

What you should be able to do after today's lecture:

  1. Define transcriptomics and explain its role in understanding gene expression patterns.
  2. Discuss emerging trends in transcriptomics.
  3. Compare and contrast transcriptomics and genomics.
  4. Explain the principles of RNA-seq technology and its advantages over previous methods.
  5. Outline the computational pipeline for RNA-seq data analysis.

Supplementary material

Relevant content for today's lecture.

  • None! Just the presentation.

DRAFT

This page is a work in progress and is subject to change at any moment.

We'll explore the challenges of aligning millions of short reads to a reference genome and discuss various algorithms and data structures that make this process efficient. The session will focus on the Burrows-Wheeler Transform (BWT) and the FM-index, two key concepts that revolutionized read alignment by enabling fast, memory-efficient sequence searching. We'll examine how these techniques are implemented in popular alignment tools and compare their performance characteristics.

Learning objectives

What you should be able to do after today's lecture:

  1. Describe the challenges of aligning short reads to a large reference genome.
  2. Compare read alignment algorithms, including hash-based and suffix tree-based approaches.
  3. Explain the basic principles of the Burrows-Wheeler Transform (BWT) for sequence alignment.

Supplementary material

Relevant content for today's lecture.