Module Overview

OBJECTIVES: 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, we expose the students to a series of case studies spanning gene feature recognition, gene expression and proteomic analysis, gene finding, sequence homology interpretation, phylogeny analysis, physical mapping, and genome sequencing.

The preliminary course plan is as follows:

• Essence of Bioinformatics [2 hours, integrated into various lectures]
  • Overview of molecular biology
  • Overview of tools and instruments for molecular biology
  • Overview of themes and applications of bioinformatics

• Essence of Knowledge Discovery [4 hours]
  • Basic classification performance measures and techniques
  • Introduction to feature selection techniques
  • Introduction to machine learning techniques
  • Introduction to WEKA
  • [Brief overview only. No algorithmic details ]

• Gene Feature Recognition from Genomic DNA [2 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 [2 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 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
  • [Basic concepts only]

• Sequence Homology Interpretation [3 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

• Gene Finding [3 hours]
  • Gene structure basics
  • Overview of gene finding
  • Case study of GRAIL
  • Handling of frame shifts and in-dels

• Phylogenetic Trees [2 hours]
  • Phylogeny reconstruction method basics
  • Case study on the origin of Polynesians
  • Case study on the origin of Europeans

• Physical Mapping and Genome Sequencing [3 hours]
  • Large-scale sequencing basics
  • Physical mapping basics
  • Case study on sequence assembly algorithm
  • Case study on the shortest common superstring problem
• [Some other topics may be discussed instead of physical mapping and sequencing.]