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CS 489: Bioinformatics Programming (JSON)

Catalog description: Computer programming to analyze high-throughput molecular biology data including genomic sequences, bulk and single-cell transcriptome, epigenome, and other omics data. Quality control, library size normalization, confounding effect removal, clustering, statistical modeling, trajectory inference, and visualization. Taught with C S 509. May be repeated up to 3 credits.

Prerequisites: At least a C- in C S 272 and C S 278.    (Catalog Link)

Credits: 3 (3)

Coordinator: Joe Song

Textbook: Holmes, S. & Huber, W. (2019). Modern Statistics for Modern Biology. Cambridge University Press; and Matloff, N. (2011). The Art of R Programming: A Tour of Statistical Software Design. No Starch Press
    (also: online reading)

BS degree role: selected elective

Course Learning Objectives

  1. Write R scripts and functions to manipulate biological sequences, genome annotation, and gene expression data
  2. Perform high-throughput data analysis with established R packages
  3. Detect differential gene expression on RNA sequencing data
  4. Perform single-cell RNA sequencing data analysis (quality control, library size normalization, confounding effect removal, modeling)
  5. Assess statistical significance of analytical results
  6. Create automatic data analysis pipeline to link multiple software packages

Course Practicum Requirements

  1. Write R scripts involving vectors, lists, and data frames
  2. Design R functions to implement vectorized operations
  3. Perform pattern analysis using regular expressions
  4. Run programs to assemble transcriptome
  5. Analyze differential gene expression using R packages
  6. Normalize and analyze single-cell RNA-sequencing data

Course Topics

  1. Basics of molecular biology
  2. R programming
  3. Genome assembly and annotation
  4. Genomic sequence variant calling
  5. Transcriptome assembly
  6. Differential gene expression analysis
  7. Single-cell RNA sequencing data analysis
  8. Gene ontology and pathway enrichment analysis

Course Improvement Decisions

(Course improvement decisions or recommendations from past assessments)

  1. none

ABET Outcome Coverage

(Provide Mapping to ABET Student Outcomes)

  1. TBD

Other Notes

(Any important notes or issues to consider)

  1. none