PLBIO 4000

PLBIO 4000

Course information provided by the Courses of Study 2020-2021.

This course is geared towards graduate students and advanced biology undergraduates seeking a better understanding of computational biology. Lectures will be a combination of presentations, paper discussions and hands-on sessions. Labs and paper discussions will have a significant component of plant science, but students from non-plant fields are also encouraged to register. Students will learn to work in a Unix environment, code using Python/R, and deploy tools for genome assembly, RNA-seq data analysis, local and global sequence alignment, protein domain searching using Hidden Markov Models, phylogenetic reconstruction, metabolomic analysis, and machine learning. Lectures will cover the algorithmic concepts underlying popular tools. The students will also learn practical aspects of implementing these tools in their own research using facilities available at Cornell.

When Offered Spring.

Prerequisites/Corequisites Prerequisite: biology courses: BIOMG 2800 or PLBRG 2250, BIOMG 3320 or BIOMG 3350, or equivalent. Computational courses: CS 1110 or equivalent. Statistics courses: BTRY 3010, STSCI 2150, or equivalent. Permission of instructor required if prerequisites not met.

  • Implement popular bioinformatics tools using Unix, Python and R.
  • Explain the theory behind different bioinformatics tools.
  • Identify the applicability, strengths and weaknesses of different bioinformatics algorithms.
  • Integrate popular bioinformatics tools into their own research.
  • Critique plant science research papers utilizing bioinformatics tools, and identify the caveats of the performed analyses.

View Enrollment Information

  •   Regular Academic Session.  Combined with: PLBIO 6000

  • 4 Credits GradeNoAud

  •  2936 PLBIO 4000   LEC 001

    • TR Online Meeting
    • Feb 8 - May 14, 2021
    • Moghe, G

  • Instruction Mode: Online
    Prerequisites: Biology courses: BIOMG 2800 or PLBRG 2250, BIOMG 3320 or BIOMG 3350, or equivalent. Computational courses: CS 1110 or equivalent. Statistics courses: BTRY 3010, STSCI 2150, or equivalent.