Module Overview

The student will master the basic tools and approaches for analysis of DNA sequences, protein sequences, gene expression profiles, etc. He will have a good understanding of important problems and applications of computational biology such as identification of functional features in DNA and protein sequences, prediction of protein function, deriving diagnostic models from gene expression profiles, etc. He will also have the confidence to propose new solutions in these applications as well as new emerging problems in computational biology. 
 

1. Development of flexible and logical problem-solving skills.
2. Understanding of main bioinformatics problems.
3. Appreciation of main techniques and approaches to bioinformatics.

To achieve the goals above, students are exposed to a series of case studies spanning gene feature recognition, gene expression and proteomic analysis, sequence homology interpretation, phylogeny analysis, mutation-phenotype association, etc. 
 

The course plan is as follows:

• Essence of Bioinformatics [1 hour, integrated into various lectures]
  • Overview of molecular biology
  • Overview of tools and instruments for molecular biology
  • Overview of themes and applications of bioinformatic
     
• Essence of Knowledge Discovery [3-4 hours]
  • Basic classification performance measures and techniques
  • Introduction to feature-selection techniques
  • Introduction to machine-learning techniques
  • [Brief overview only. No algorithmic details] 
     
• Gene-Feature Recognition from Genomic DNA [3-4 hours]
  • The “feature generation, feature selection, feature integration” approach.
  • Case study of “translation initiation site” (TIS) recognition
  • Case study of “transcription start site” (TSS) recognition
  • [Some other gene feature recognition problems may be used in the lecture instead of TIS and TSS.]
  • [Concentrate on methodologies, not algorithms.]
     
• Gene Expression Analysis [3-4 hours]
  • Microarray basics
  • Case study of classification of gene expression profiles
  • Case study of clustering of gene expression profiles
  • Case study of molecular network reconstruction by gene expression profiles
  • [Some other gene expression analysis problems may be used in the lecture instead of molecular network reconstruction.] 
  • [Concentrate on methodologies, not algorithms.]
     
• Essence of Sequence Comparison [3-4 hours] 
  • Dynamic programming basics
  • Sequence comparison and alignment basics
  • Case study of the Needleman-Wunsh global alignment algorithm
  • Case study of the Smith-Waterman local alignment algorithm
  • Case study of the BLAST heuristic-based similarity search method
  • [Basic concepts only]
     
• Sequence Homology Interpretation [3-4 hours]
  • Case study of protein function prediction by sequence alignment
  • Case study of protein function prediction by phylogenetic profiling
  • Case study of active site and domain prediction
  • Case study of key mutation sites prediction
     
• Phylogenetic Trees [3-4 hours]
  • Phylogeny reconstruction method basics
  • Case study on the origin of Polynesians
  • Case study on the origin of Europeans
  • Reconciliation of phlogenetic trees
     
• Some additional topics---e.g. disease/phenotype mutations---may be discussed if there is sufficient time.

• 3 assignments worth 35% of the total module marks.
• 1 project worth 15% of the total module marks.
• 1 final exam worth 50% of the total module marks.