Course Description

This course is designed to train students in basic bioinformatics skills to address biological questions through acquisition and analyses of nucleotide and amino acid sequences, annotation of novel genomes, and examination of evolutionary relationships. No programming knowledge is needed or taught in this course. The training uses only publicly available, free, web-based programs for data acquisition and analysis. No textbook is required since readings come from peer-reviewed published papers on the topics and programs.

The topics covered in this class provide an application focus and include but are not limited to:

• Sequence and structure location in publicly accessible databases
• Genomic annotation of eukaryotic, prokaryotic, and viral genomics
• Sequence alignments
• Structural alignments
• Multiple sequence alignments
• Phylogeny
• Domain identification
• Protein modeling
• Systems biology
• Gene expression
• Pathway enrichment

Recommended prerequisites: undergraduate level biochemistry, genetics, and statistics

Recommended concurrent: Project Course - Hypothetical Protein Annotation (coming soon)

Estimated hours for completion: 45-50

Modality: 100% asynchronous and completely self-paced with a recommended pace of 1 unit/week

Certificate Option

Certificates will be awarded upon successful course completion. Students must score at least 80% on unit homework and exam assignments to earn a completion certificate. Students will have 3 attempts at each unit homework and exam to achieve this score.

Learning Outcomes

Upon successful completion of this course, the student will be able to:

• Retrieve information from free publicly available bioinformatics knowledgebases
• Identify protein coding and non-coding regions from genomic sequences
• Analyze alignments between different nucleotide and amino acid sequences
• Construct a phylogenic tree
• Identify protein secondary features, tertiary structure, and ligand binding partners from a primary sequence
• Perform gene and pathway analyses on transcriptomics data