01:694:390 Introductory Computational Biology

Introductory Computational Biology is a hands-on, computer-intensive course designed for students interested in exploring the intersection of biology and data science. It is suitable for students with a basic understanding of molecular biology who aspire to scientific or biomedical careers. The curriculum progresses from Unix and Cloud file management to practical bioinformatics analyses, such as DNA mutation mapping (module I), RNA-seq data analysis (module II), and ChIP-seq data analysis (module III). Each module is centered around real-world research questions, with instruction through R/Python coding examples. Assignments and a final project are designed to solidify students' skills in applying computational programs to biological inquiries, preparing them for advanced study or career development in this evolving discipline.
Semester Offered: Spring
Credits: 3
Course URL: canvas

Prerequisites: 01:447:380 Genetics or 01:447:384 Genetic Analysis I
Co-requisites: None
Stop Point (enrollment): 27 (limit due to capacity of a life science computer teaching lab)

Course Syllabus
Spring 2024 (subject to change)

Course satisfies Learning Goals:

After completing this course, students will be able to :

Explain the basic principles and concepts of computational biology research;
Appraise the application of computational techniques in current molecular biology;
Independently utilize Python and R programming languages to analyze DNA and RNA sequences.

These goals aim to provide students with both the theoretical framework and practical expertise necessary for the pursuit of advanced studies or professional opportunities in modern biology.

Course Materials

No textbook is required as most of the needed material is available during class.
Weekly readings on the applications of omics analyses will be posted at the Canvas course site.
Students are encouraged to study R and Python with the following sources:

R for Data Science by Hadley Wickham and Garrett Grolemund
Focus: Data science techniques using the tidyverse package.
Link: https://r4ds.had.co.nz/index.html

An Introduction to R
Focus: Basics of R, official manual from The R Project.
Link: https://cran.r-project.org/doc/manuals/r-release/R-intro.html

Automate the Boring Stuff with Python by Al Sweigart
Focus: General Python automation and scripting.
Link: https://automatetheboringstuff.com/

Course Closed?: If the course is closed, contact Dr. Yang Lyu (Coordinator) This email address is being protected from spambots. You need JavaScript enabled to view it. for a special permission number.

Course Instructors:

Course Coordinator: Dr. Yang Lyu , This email address is being protected from spambots. You need JavaScript enabled to view it.Dr. Miguel Zaratiegui Biurrun, This email address is being protected from spambots. You need JavaScript enabled to view it.Dr. Sam Gu, This email address is being protected from spambots. You need JavaScript enabled to view it.

For office hours or discussion, times are open. Email to set up a time to meet or talk after class. You can also ask questions through emails. You can expect us to reply to emails within 1 business day, not including weekends or holidays.

Academic Integrity:

Students are expected to be familiar with Rutgers’ Code of Academic Integrity and the possible penalties (including suspension and expulsion) for violating the policy. Academic dishonesty includes (but is not limited to):

Cheating
Plagiarism
Aiding others in committing a violation or allowing others to use your work
Failure to cite sources correctly, including artificial intelligence-based tools
Fabrication
Using another person’s ideas or words without attribution, including re-using a previous assignment
Unauthorized collaboration
Sabotaging another student’s work

We have a zero-tolerance policy for cheating, and all violations will result in substantial penalties. If you have any doubts or questions about what constitutes academic misconduct, please do not hesitate to contact Dr. Lyu.

Student Support and Mental Wellness Services

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Disability Services
(848) 445-6800 / Lucy Stone Hall, Suite A145, Livingston Campus, 54 Joyce Kilmer Avenue,
Piscataway, NJ 08854 / https://ods.rutgers.edu/

The Office of Disability Services works with students with a documented disability to determine the eligibility of reasonable accommodations, facilitates and coordinates those accommodations when applicable, and lastly engages with the Rutgers community at large to provide and connect students to appropriate resources.

** All information is subject to change at the discretion of the course coordinator.

Contacts

Biological Sciences

Undergraduate Quicklinks

Course Descriptions

01:694:390 Introductory Computational Biology