Local Alignment and Statistics
This course, from the MIT Open Courseware library, provides an overview of the principles and tools used in classical and next-generation sequencing. The second half of the course covers BLAST- Basic Local Alignment Search Tool- which is an algorithm that allows a researcher to compare a query sequence with a larger set of sequences. This course is the second lecture in a larger Foundations of Computational and Systems Biology course taught at MIT in Spring 2014.
- Describe the process of conventional sequencing technologies
- Define the Sanger Sequencing method
- Identify examples and characteristics of conventional sequencing technologies
- Distinguish between various next generation sequencing technologies
- Highlight the benefits of local alignment
- Develop a BLAST-like algorithm
- Define statistics of matching
- Target frequencies and mismatch penalties for nucleotide alignments
Overview - 3 minutes
- What do you already know?
- Overview and objectives
Conventional Sequencing - 10 minutes
- 1D, 2D, and 3D models of DNA
- Sanger Sequencing Method
- Practical Examples of Conventional Sequencing
- Read Lengths of Conventional Sequencing
Next Gen Sequencing - 10 minutes
- Next Generation Sequencing Technologies
- Roche Technology
- Illumina Technology
- Helicose and Pac Bio Technologies
- Deeper Dive into Roche Technology
- Deeper Dive into Illumina Technology
Local Alignment - 15 minutes
- Why Align Sequences?
- Finding Top Scoring Alignment Matches
- Creating an Algorithm to Find Top Matches
- Calculating Running Time
- Expected Score Has to Be Negative
Deeper Dive into the Algorithm - 10 minutes
- Identifying Frequency of High Scoring Segments Across a Sequence
- Calculating Lambda
- What Happens to Lambda If Scores Are Doubled?
- What Does Lambda Mean?
Scoring Matrix and Mismatch Penalty - 8 minutes
- What Scoring Matrix to Use for DNA
- Choosing the Mismatch Penalty
Wrap Up - 2 minutes
- What Do You Know Now?
About the Expert
MIT Open Courseware