This list has every graduate level ‘omics class WSU offers. If you would like to add to this list please email res.dev@wsu.edu.

  • Biology:
    • BIOLOGY 519 Introduction to Population Genetics Survey of basic population and quantitative
      genetics
    • BIOLOGY 521 Quantitative Genetics Fundamentals of quantitative genetics; evolutionary quantitative
      genetics
    • BIOLOGY 534 Modern Methods in Population Genomics Problems and prospects of designing a
      study with genomic data: from raw data to demography and selection inferences.
    • BIOLOGY 566 Mathematical Genetics See MATH 563.
  • Computer science:
    • CPT S 570 Machine Learning Introduction to building computer systems that learn from their experience;
      classification and regression problems; unsupervised and reinforcement learning.
    • CPT S 571 Computational Genomics Fundamental algorithms, techniques and applications.
    • CPT S 572 Numerical Methods in Computational Biology Prereq cell biology, probability and
      statistics, graduate standing in computer science, or permission of the instructor. Computational methods
      for solving scientific problems related to information processing in biological systems at the molecular and
      cellular levels.
  • Crop and Soil Sciences:
    • CROP SCI 545 Statistical Genomics Concepts and applications in modern breeding programs.
    • CROP SCI 555 Epigenetics in Plants Understanding principles of epigenetics in plants with a focus on
      its role in understanding and improving plant genomes and their adaptation to the changing environment.
      Recommended preparation: General genetics.
  • Horticulture:
    • HORT 503 Advanced Topics in Horticulture
  • Mathematics
    • MATH 563 Mathematical Genetics Mathematical approaches to population genetics and genome
      analysis; theories and statistical analyses of genetic parameters. (Crosslisted course offered as MATH
      563, BIOLOGY 566).
  • Molecular Biosciences:
    • MBIOS 503 Advanced Molecular Biology I DNA replication and recombination in prokaryotes and
      eukaryotes; recombinant DNA methods and host/vector systems; genome analysis; transgenic
      organisms. Recommended preparation: Introductory genetics and biochemistry coursework.
    • MBIOS 578 Bioinformatics Computer analysis of protein and nucleic acid sequences, functional
      genomics and proteomics data; modeling biological networks and pathways. Recommended preparation:
      Introductory genetics or biochemistry coursework.
  • Statistics:
    • STAT 523 Statistical Methods for Engineers and Scientists Hypothesis testing; linear, multilinear, and
      nonlinear regression; analysis of variance for designed experiments; quality control; statistical computing.
    • STAT 530 Applied Linear Models The design and analysis of experiments by linear models.
    • MATH/STAT 536 Statistical Computing Generation of random variables, Monte Carlo simulation,
      bootstrap and jackknife methods, EM algorithm, Markov chain Monte Carlo methods.
    • STAT 565 Analyzing Microarray and Other Genomic Data Statistical issues from pre-processing
      (transforming, normalizing) and analyzing genomic data (differential expression, pattern discovery and
      predictions).