I-CIPS-related courses taught by faculty in the School of Integrative Plant Science

PLSCI 4000 / 6000  Concepts and Techniques in Computational Biology

Gaurav Moghe
Spring. 4 credits. Letter grades only.  TR 9:05-11:00am

Since a significant amount of learning for this class occurs outside the class, auditing this class is not permitted.

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.

Prerequisites/Corequisites Prerequisite: biology courses: BIOMG 2800 or PLSCI 2250, BIOMG 3320 or BIOMG 3350, or equivalent. Computational courses: CS 1110, CS 1133 or equivalent. Statistics courses: BTRY 3010, STSCI 2150, or equivalent.

Outcomes

• 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.

PLSCI 4170/7170 – Quantitative Genetics for Analysis and Improvement of Complex Traits

Kelly Robbins & Jean-Luc Jannink
Spring. 4 credits. Letter grades only.
Offered in odd-numbered years only.

Prerequisite: PLSCI 4030 and BTRY 6010 or equivalent.

This course will provide students with a solid foundation in quantitative genetics theory, as applied to the field of plant and animal breeding, introduce students to modern-day modeling approaches, simulation tools and applications of genomic selection. While the methodologies of plant and animal breeding are distinct in many ways, the core principles are the same, and this course will cover topics in a way that is inclusive of animal breeding applications.

Prerequisites/Corequisites Prerequisite: STSCI 2150 or BTRY 3010, plus familiarity with matrix algebra.

Outcomes

• Interpret quantitative genetics research with substantial mathematical and statistical components.
• Analyze complex breeding datasets using advanced modeling methods.
• Estimate gain from selection and impact on genetic variance of breeding decisions.
• Design optimal breeding approaches.

PLSCI 4200 Geographic Information Systems (GIS): Concepts and Applications

Ying Sun
Spring. 3 credits. Letter grades only.  MW 12:20-2:15

This course introduces the fundamental principles and concepts necessary to carry out meaningful and appropriate geospatial applications using geographic information science (GIS), with a particular emphasis on characterizing, assessing, and understanding agronomic and environmental systems. The course covers key issues in GIS such as geographic coordinate systems, map projections, spatial analysis, use of remotely sensed data, visualization of spatial data, spatial statistics, and spatial modeling. Laboratory exercises include database query, database acquisition, spatial analysis and visualization to address issues in hydrology, ecology, agriculture, and sustainability. Students will gain proficiency with the leading open-source GIS platform, QGIS, and familiarity with commercial GIS software such as ArcGIS Pro.

Prerequisites/Corequisites Prerequisite: PLSCI 2200.

Outcomes

• Explain the basic principles and functions of GIS (including coordinate systems, projections and datums, spatial data models and their appropriate application environment.
• Generate effective maps for visualization and communication with end users for decision-making.
• Acquire geospatial datasets from various sources.
• Perform geospatial data analysis in QGIS.
• Apply geostatistical methods for pattern analysis and spatial interpolation.
• Perform land cover classification using built-in machine learning algorithms in QGIS and analyze temporal changes.
• Design, perform, and present a collaborative group project using geospatial skills acquired in the course.

PLSCI 4400 Phylogenetic Systematics

Jacob Landis
3 cr, MW 11;15-12:05 and Thu (Lab) 2-4:30)
Offered in odd-numbered years only.

Basic and advanced theory and methods of phylogenetic analysis. Introduces students to cladistic analysis using parsimony and gain experience with computer-aided analysis of taxonomic data, including both morphological and molecular data sources. Topics include applications of phylogenetic methods to biogeography and evolutionary studies.

Prerequisites/Corequisites Prerequisite: one majors-level biology course.

Outcomes

• Obtain, organize, and analyze molecular (DNA, RNA and protein) and morphological data, and combine the two data sources to produce phylogenetic trees that will reveal the history of various plant groups.
• Use computational approaches to generate, plot, and compare phylogenetic trees in R and the Terminal command line.
• Discuss and interpret phylogenetic trees to describe patterns of plant evolution, diversity, and adaptation/ecological interaction.
• Formulate original questions about the evolutionary history and evolution of plants and plant traits, translate these into empirically testable hypotheses, and then perform the analyses to see if the hypothesis is supported.
• Collect and analyze data obtained from original research, using methods that are reproducible.
• Translate and apply genetic and phylogenetic data to advance the field and solve real-world problems.
• Discuss the effects of understanding phylogenetics on environmental sustainability and human health (e.g., evolution of plant, animal and human disease).
• Demonstrate an awareness of the ethical principles and global consequences associated with past, present, and future advances in evolutionary studies.
• Succinctly and clearly communicate information about the breadth of issues in phylogenetics to diverse audiences in oral and written formats.

PLSCI 4290/5290 Remote Sensing and Modeling for Ecosystems

Ying Sun
Fall. 3 credits. Tue/Thu: 2:55-4:10

This course introduces advanced concepts of remote sensing and numerical modeling, with hands-on experience in data acquisition, processing, and interpretation. This course aims to explore key questions facing the agronomic and natural eco-systems using remote sensing techniques and ecological modeling at various scales. It provides hands-on experience in remote sensing techniques and using datasets/tools and model simulations to address research questions.

Prerequisites/Corequisites Prerequisite: knowledge of the basics of remote sensing, calculus, physics, and programming skills, and some background in agro-ecosystems.

Outcomes

• Describe the basic principles in remote sensing.
• Describe the spectral signatures of land surface properties and appropriate application.
• Acquire satellite dataset from NASA, ESA, and Google Earth Engine.
• Process remote sensing data using ENVI, and R (or Python).
• Run mechanistic model simulations in the CLM framework.
• Apply remote sensing observations and model simulations to interpret agro-ecological phenomena.
• Conduct an independent applications-based project.
• Develop and present an oral and collaborative group project.

PLSCI 7201: Advanced Statistics and Experimental Design

Kelly Robbins
Fall. 2 credits. Friday 1:25-2:15

This course will provide a comprehensive introduction to experimental designs that are commonly used in plant science and provide participants with the practical coding skills necessary to analyze data from such designs. This basic knowledge will be extended to accommodate high-dimensional data generated by modern ‘omics techniques. This course will provide a foundational introduction of experimental designs and statistical analyses to guide independent research and avoid mistakes that are often made by new scientists. While this course will cover a wide range of topics, it is by no means an exhaustive coverage of experimental design and statistics. Students are strongly encouraged to complement the foundational knowledge learned in this course with classes on advanced statistical methods and/or experimental design.

Permission Note Enrollment limited to: graduate students. Undergraduates must obtain permission of instructor.
Prerequisites/Corequisites Prerequisite: undergraduate-level course in statistics.

Outcomes

• Interpret experimental designs that are commonly used in plant science and plant breeding.
• Apply and interpret linear models to account for systematic effects in commonly used experimental designs.
• Extend classical experimental designs and statistical frameworks to challenges and limitations associated with high-dimensional ‘omics data.
• Apply these concepts to meet independent research objectives.