Boston Univ.

Protein and DNA Sequence Alalysis Lecture Notes

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Description

 Presents fundamental concepts from molecular biology and molecular genetics. Teaches how to make biological inferences from DNA and protein sequences using mathematical and computer science techniques. Pairwise sequence comparison is studied in detail. The algorithm is extended to deal with more general cases and applied to RNA structure prediction. Multiple sequence alignment and conserved sequence pattern recognition are considered. Methods of using phylogenetic trees to study the molecular evolution are described. Methods of identifying coding regions in genomic data are studied. Algorithms to solve the fragment assembly problem of DNA sequencing are discussed. Mathematical models and computational algorithms for genetic regulation are described. An introduction to protein 3-dimentional structure prediction is given.

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Number
Lecture

Introduction

2

Motivation to Pairwise sequence alignment (1)

3

Pairwise Sequence Alignment (2): Dynamic programming for global alignment

4

Pairwise Sequence Alignment (3): Dynamic programming for local alignment

5

Pairwise Sequence Alignment (4): Dynamic programming in a band & Affine gap penalties

6

Pairwise Sequence Alignment (5): Development of scoring matrices (PAM)

7

Pairwise Sequence Alignment (6): Development of scoring matrices (BLOSUM) 
Fast pair-wise alignment & database searching tools - BLAST

8

Introduction to probability and statistics (1)

9

Introduction to probability and statistics (2)

10

Statistical Significance of Sequence Alignment and Database Search (1) 

11

Statistical Significance of Sequence Alignment and Database Search (2) 

12

How Do We Evaluate Database Search Results?

13

Multiple Sequence Alignment: Multi-dimensional Dynamic Programming

14

Phylogenetic trees: Molecular Evolution (1)

15

Phylogenetic trees: Molecular Evolution (2)

16

Multiple Sequence Alignment: Heuristic algorithms

17

Conserved Sequence Pattern Discovery and Statistical Significance: sequence profiles (1)

18

Conserved Sequence Pattern Discovery and Statistical Significance: sequence profiles (2)

19

Genetic Regulation: Identifying Promoter Sites

20

Markov Models (1)

21

Hidden Markov Models and Their Applications to Sequence Analysis (2)

22

Hidden Markov Models and Their Applications to Sequence Analysis (3)

23

From Sequence to Protein Structure: Homology Modeling