Course Syllabus

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Note: for Spring 2021, this entire course will be held remotely. No in-person attendance is required and all materials will be made available asynchronously

Instructor: Dr. Melissa Gymrek, mgymrek@eng.ucsd.edu

TAs:

Mia Altieri (she/her), Eleonora Rachtman

Lectures: TR 11-11:50am (Zoom)

Labs: TR 1-2:50pm (Zoom)

Office Hours:

Private appointments to discuss accommodations or special circumstances can be arranged by email.

Course description

This course emphasizes the hands-on application of bioinformatics to biological problems. Students will gain experience in the application of existing software, as well as in combining approaches to answer specific biological questions. Sequence alignment, fast database search, comparative genomics, expression analysis, computational proteomics, genome-wide association studies, next generation sequencing, genomics and big data. This course is open to bioinformatics majors only.

Prerequisites

CSE 11, CSE 12, MATH 20C, BILD 1, BILD 4 or BIMM 101

Course objectives

By the end of the course, students will:

  • Be able to explain why bioinformatics approaches are important to understand and interpret results in many different biological areas of study.
  • Be able to utilize existing bioinformatics tools individually and in combination to analyze various kinds of biological data.
  • Develop critical analysis and research skills that can be applied to understand and use new bioinformatics tools that are developed in the future.
  • Be able to access publicly available datasets and bioinformatics tools.
  • Learn and use accepted best practices for bioinformatics.

Course structure

The course consists of two lectures (1 hour) and two lab sessions (2 hours) each week. Lectures will cover background material needed to complete the lab projects. Lectures will be held by Zoom and will be recorded. Labs will consist of hands-on assignments focusing on real-world analyses of biological datasets. Labs will also be held over Zoom.

During week 1, students will get set up in our compute environment including Python, Jupyter notebooks, and basic command line navigation. During weeks 2-8, students will work on weekly lab assignments. You are allowed and encouraged to collaborate on lab assignments, but all students must turn in the assignment separately. Lab assignments are due the following Sunday at 11:59pm. Assignments will be completed using JupyterLab, which can be accessed at datahub.ucsd.edu.

Grades will be released using Canvas. We will strive to post grades within one week of the due date of each lab.

There will be a short quiz at the end of weeks 3, 5, and 7 in the Thursday lab meant to test students' understanding of and ability to apply the concepts learned.

For the last 3 weeks of the quarter, students will work in groups on an independent project.

Grading

Grades will be based on:

  • Homework (70%): Each lab counts for 10% total (usually 2% for exercises, 8% for main lab assignment)
  • Quizzes (15%): Each quiz is worth 5% each
  • Final projects (15%): Proposal 2%, report 10%, presentation 3%

Occasional extra credit problems will be available on homework assignments.

Final grades will be out of 100 points. >=70 points is passing. Grades will be assigned using the following scale:

A+:100+ A:93-99, A-:90-92, B+:87-89, B:83-86, B-:80-82, C+:77-79, C:73-76, C-:70-72, F:0-69.

You will lose 5 points on your lab assignment (out of 100) for each day it is late. This is capped after 4 days, so after 4 days your baseline score for the report is 80/100 points.

If you miss a quiz you must email to notify us before the class period with an excused absence and we can reschedule a makeup.

Academic integrity

During lab, while working on coding and analysis you are encouraged to chat with your classmates, look things up online, and ask instructors for help. However, when writing up your lab reports, work independently and make sure that the lab report is in your own words and reflects your own understanding. You may use scientific literature, but must cite it in the text. DO NOT directly copy/paste code to/from other students in the class or websites. DO NOT post questions about assignments on sites like seqanswers or stack overflow (you can research existing threads on these sites, just don't start new ones for the coursework). 

Accommodations for students with disabilities

If you have a disability for which you are or may be requesting accommodations, please contact the Office for Students with Disabilities. You must have documentation from the the Office before accommodations can be granted.

Diversity and inclusion

We are committed to fostering a learning environment for this course that supports a diversity of thoughts, perspectives and experiences, and respects your identities (including race, ethnicity, heritage, gender, sex, class, sexuality, religion, ability, age, educational background, etc.). Our goal is to create a diverse and inclusive learning environment where all students feel comfortable and can thrive.

Our instructional staff will make a concerted effort to be welcoming and inclusive to the wide diversity of students in this course. If there is a way we can make you feel more included please let one of the course staff know. Our learning about diverse perspectives and identities is an ongoing process, and we welcome your perspectives and input.

We also expect that you, as a student in this course, will honor and respect your classmates, abiding by the UCSD Principles of Community (https://ucsd.edu/about/principles.html). Please understand that others’ backgrounds, perspectives and experiences may be different than your own, and help us to build an environment where everyone is respected and feels comfortable.

If you experience any sort of harassment or discrimination, please contact the instructor as soon as possible. If you prefer to speak with someone outside of the course, please contact the Office of Prevention of Harassment and Discrimination: https://ophd.ucsd.edu/.

Disclaimer

While we have every intention of following this syllabus, any information here is subject to change.

Course Summary:

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