Training

Bioinformatics Educational Module Series

In addition to the courses offered for the bioinformatics concentration, we also provide various educational modules for students and people who are interested in bioinformatics training. Listed below are educational modules available to public:

Title: Incremental Approach for Developing Algorithms in Bioinformatics

Bioinformatics problems are computational and/or data intensive. For example, finding/detecting motifs from multiple of DNA sequences are both computational and data intensive. Developing efficient algorithms which can find approximate optimal solutions is critical in Bioinformatics. This education module demonstrates how to develop efficient algorithms for bioinformatics problems step by step. The module consists of (i) Fundamental issues in algorithm design and analysis; (ii) Mathematical modeling: how to change a biologic problem into a mathematical and computational problem, (iii) Primary algorithm design: how to solve problem straight forward by using Brute Force; (iv) Greedy/Heuristics algorithm design: how to solve problem by making locally optimal choice at each step in the hope that this choice will lead to a globally optimal solution; (v) Divide-and-Conquer algorithm design: how to solve a problem by conquering the sub-problems and then find the solution of the problem from the solutions of the sub-small problems; and (vi) Incremental algorithm improvement: how to improve the solution from Greedy/Heuristics and divide-and-conquer. The module also shows tradeoff analysis between quality of the solution and the time complexity.

Title: High Performance Computing

This module is aimed at providing a compact overview of the far-reaching subject of Parallel Computing and its application in the field of Bioinformatics. The module presents the essentials of parallel computing and some parallel programming techniques. The module begins with a discussion on parallel computing and concepts and terminology related to parallel computing. The topics of parallel memory architectures and programming models are then discussed. These topics are followed by a number of practical discussions on some bioinformatics problems related to designing and running parallel programs.

Title: Bioinformatics Computing

This module deals with computing perspective of bioinformatics. This module introduces and explains many computing skills and tools used in Bioinformatics research. While molecular biology's fundamentals are also introduced, the module will focus on developing bioinformatics computing skills. Contents include overview of computational approaches to biological problems, bioinformatics programming, pattern matching, automating data analysis, bioinformatics tools for sequence analysis, tools for genomics and proteomics, and bioinformatics tools for protein structure and function.

Title: Bioinformatics Database Design and Information Retrieval

The module introduces database systems designed for storing and accessing biological data. The focus is on the database aspects of designing and managing biological data. Contents include overview of database concepts, database design process, introduction to SQL, database design and relational data model, design of biological database - sequence database and gene database, and guideline of biological database design.

Title: Pariwise Sequence Alignment

Pairwise alignment of DNA, RNA and protein sequences is essential to biological sequence database search. Various algorithms have been developed. This module contain brief introduction to these algorithms, including sliding window, dot matrix, Needleman-Wunsch, Smith-Waterman, FASTA and BLAST algorithms. Exercises are provided for students to practice.

Title: Hidden Markov Model

Hidden Markov Model (HMM) is a widely use probabilistic approach to computational problems. In bioinformatics application, HMM is used for build and search protein domain databases, segmentation of chromosomal regions for array comparative hybridization. This module start with introduction to probability, Bayesian probability theorem, then discussed how to build matrices based HMM approach. The module used several examples to illustrate the approach and application. Exercises are
provided for student to practice.

Title: Motif Recognition

Motifs are short stretch of sequences in protein or DNA that are conserved due to functional requirements. In biological sequence analysis, motif recognition includes two aspects: (1) discovery of previously unknown motifs and (2) determine if a sequence contains a known motif. The first is a sampling problem and the second is a pattern matching problem. This module introduced exhaustive searches approach and Gibbs sampling approach, then discussed pattern match algorithms, and the application of these techniques in transcription factor binding site search and protein motif search. Exercises are provided

Title: Molecular Evolution and Phylogeny

The evolution of a particular group of organism is of great interest in evolutionary studies. Reconstruction of evolutionary history can be done based on not only morphological, physiological characteristics and fossil record, but also on molecular information of the organism. This module introduced molecular clock, construction of phylogenetic trees using distance based and character based approaches. Existing programs were indicated. The module also includes exercises.

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