BIOMI 6300
Last Updated
- Schedule of Classes - November 13, 2024 8:41AM EST
- Course Catalog - November 12, 2024 10:26AM EST
Classes
Links for textbooks and Cornell Store open in new tab.
BIOMI 6300
Course Description
Course information provided by the 2023-2024 Catalog.
High-throughput sequencing has revolutionized and become common practice across the field of microbiology. This course will prepare students for analyzing large sequencing datasets through a meaningful biological lens. Via a combination of lectures, discussions of primary literature, and hands-on, data-driven computational labs, we will learn how to organize computational projects, work in the command line, perform cloud computing, and gather, interpret, and analyze amplicon, genomic, and shot-gun metagenomic data to advance our understanding of microbial systems. We will evaluate the distribution of microbial biodiversity and gene abundances and compare the taxonomic and genomic composition of microbial communities. This course is geared towards graduate students and upper-level undergraduate students across biology. We will focus on how to use software for biological analyses while touching on broader concepts of statistical algorithms. (Note: the specifics of statistical models will not be the focus.)
Prerequisites/Corequisites Prerequisite: BIOMI 2900/BIOMI 2911, BIOMG 2800.
Permission Note Enrollment preference given to: graduate students.
Outcomes
- Develop proficiency in command line tools and cloud computing within the shell.
- Analyze the quality of sequencing data.
- Explain and compare the different sequencing technologies and their applications to microbial gene and genome analysis.
- Evaluate various meanings of diversity and interpret compositional changes in microbial communities through statistical approaches and analysis of amplicon sequencing.
- Build and describe the steps to generating (meta)genomes from microbial sequencing data that can be used for downstream genomic analyses.
- Develop, visualize, and statistically test biological hypotheses in R.
When Offered Spring.
Comments No prior knowledge of coding is required as an introduction to coding and data science will be covered in the first unit of the course.
Regular Academic Session. Choose one lecture and one laboratory.
-
Credits and Grading Basis
3 Credits Opt NoAud(Letter or S/U grades (no audit))
-
Class Number & Section Details
-
Meeting Pattern
- MW Riley-Robb Hall B15
- Jan 22 - May 7, 2024
Instructors
Schmidt, M
-
Additional Information
Instruction Mode: In Person
-
Class Number & Section Details
-
Meeting Pattern
- W Riley-Robb Hall B15
- Jan 22 - May 7, 2024
Instructors
Schmidt, M
-
Additional Information
Instruction Mode: In Person
Enrollment Preference is given to Graduate Students If the course is full please add yourself to the Student Center waitlist and click on the following Qualtrics link and complete the survey. https://cornell.ca1.qualtrics.com/jfe/form/SV_2mkJ8YKtfL0EwaG
Share
Disabled for this roster.