Instructors

Victor Jongeneel (Research Professor, Bioengineering)

Chris Fields (Technical Lead in Genome Informatics)

Jenny Drnevich (Functional Genomics Bioinformatics Specialist)

Jessica Kirkpatrick (Research and Instructional Specialist in Life Sciences)


HPCBio
1206 W Gregory
Urbana, IL  61801
 

Required Materials

Outline of Modules

  1. Characteristics of biological sequence data
    • Current sequencing technologies
    • Sources of errors in sequence data
    • Commonly used data formats
    • Basics of initial processing and quality control of data
       
  2. Genome and transcriptome assembly
    • Use cases for genome/transcriptome assemblies
    • Overview of assembly strategies based on sequencing technologies
    • Assembly complexities and strategies for dealing with them
    • Assessment of assembly quality
    • How transcriptome assembly differs from genome assembly
       
  3. Methods based on read alignment
    • Use cases for read alignment
    • Basics of read alignment algorithms
    • Alignment artifacts
    • Methods for extracting various information from alignments: ChIP-Seq, RNA-Seq, variant calling, etc.
    • Alternatives to read alignments
       
  4. Metagenomics
    •  Use cases for metagenomics
    • The advantages and limitations of different sequencing approaches
    • Steps to analyze and assess 16S rRNA amplicons
    • Metagenome assembly methods and analysis environments

Learning Objective

By the end of these modules, students should be able to explain the differences between current sequencing technologies and to recognize common errors and noise that can occur in resultant data. Students should be able to describe the various analyses that can be performed on sequencing data and the obstacles unique to them. Students should then be prepared to apply this knowledge to real life scenarios.

Evaluation

Learning will be assessed via iClicker questions interspersed throughout the lectures. In addition, a take home assignment will be delivered on the last day challenging students to use what they’ve learned to solve a hypothetical sequencing problem. 

Schedule