Southern Utah University

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Southern Utah University
Southern Utah University
Fall Semester 2026

Fundamentals of Bioinformatics (Face-to-Face)

BIOL 3330-01

Course: BIOL 3330-01
Credits: 3
Term: Fall Semester 2026
Department: Biology
CRN: 33856

Course Description

Bioinformatics is an interdisciplinary field that applies statistical and computational techniques to analyze large biological datasets. This course covers three core areas in bioinformatics: command-line computing, biological data management, and computer programming. Students will gain hands-on experience with high-level programming languages, including Bash, SQL, and Python, to parse, modify, and analyze data from DNA, RNA, and protein sequencing. Together, these skills provide a foundation for understanding how biological information is organized, processed, and interpreted within the biomedical sciences and the biotechnology workforce. (Fall) [Graded Letter) Three (3) hours of lecture per week. A minimum grade of “C” (2.0) or above must be earned in this course before it can be counted in a biological science major or minor or as a prerequisite for any other biology course. Prerequisite(s): BIOL 3060 and BIOL 3065 - Prerequisite Min. Grade: C

Instructor Information

Dr. Marcos Corchado-Sonera 

·       Office hours: SCA 202 | MWRF 3:00pm – 4:15pm 

·       Email: marcoscorchadosonera@suu.edu 

Background 

I am an Assistant Professor of Biology at Southern Utah University, where I teach Genetics, Developmental Biology, and Bioinformatics. I earned my Ph.D. in Molecular Genetics from The Ohio State University, where my research focused on developmental genetics, cell communication, and evolutionary developmental biology using the nematode C. elegans. I studied how cells communicate during development and disease, as well as how developmental processes evolve among closely related species. 

My current research uses genomics and bioinformatics to address questions in conservation, ecology, and human health. This work includes studying threatened and endangered species, investigating ecological processes involving microbial communities and harmful algal blooms, and using genomic approaches to better understand genetic diseases such as hyperemesis gravidarum (HG). Across these areas, I am interested in using modern sequencing and computational tools to better understand biological diversity, environmental interactions, and disease. 

Communication policy 

I will do my best to respond to messages within 24 hours during weekdays. I typically do not respond to messages in the evenings, on weekends, or on holidays. 

Required Texts

Required Texts 

·       Python for Everybody: Exploring Data Using Python 3, by Charles R. Severance, CreateSpace Independent Publishing Platform, 2016. 

·       Practical Computing for Biologists, 1st Edition, by Steven H. D. Haddock and Casey W. Dunn, Sinauer Associates, 2011. ISBN: 978-0-87893-391-4 

Required Technologies and Resources 

·       iClicker Student app (https://www.iclicker.com/) for attendance tracking (free subscription). 

Learning Outcomes

There are many things I hope you will take away from this course. Some of my objectives pertain specifically to biology, while others are more broadly applicable and may be considered “life-long” learning attributes. After the completion of this lecture/lab course, successful students will: 

1.     Apply core biological and computational concepts to analyze and interpret large-scale biological data, building on knowledge from genetics, molecular biology, and statistics. 

2.     Demonstrate proficiency in using command-line tools and scripting languages (Bash, SQL, and Python) to organize, parse, and analyze biological datasets. 

3.     Construct, manage, and analyze biological databases containing DNA, RNA, and protein sequence data, applying principles of data integrity, reproducibility, and FAIR data management. 

4.     Evaluate and interpret commonly used bioinformatic analyses by selecting appropriate algorithms and pipelines for sequence alignment, annotation, and visualization. 

5.     Develop custom computational programs to parse data in genomics, transcriptomics, and proteomics, showing increasing independence in coding and data analysis. 

6.     Understand the formatting of biological data outputs (e.g., FASTA, FASTQ, SAM/BAM, and VCF formats, among others) and demonstrate the ability to reformat data in a bioinformatic workflow. 

7.     Identify inherent errors and biases present in large-scale sequencing data. 

8.     Communicate results and interpretations effectively through written reports, data visualizations, and oral presentations tailored to both scientific and applied biotechnology audiences. 

9.     Articulate how bioinformatics tools and data analysis are applied across academic research, biotechnology, and healthcare settings, preparing for employment or graduate study in the life sciences. 

Course Requirements

Assignments 

Your grades and assignment information will be found on Canvas. Most assignments will be projects that require students to evaluate, use, and create programming scripts to analyze biological data. All assignments will be submitted through Canvas, so please meet with me if you are unsure about how to submit your work. The number of assignments may vary slightly. 

Quizzes 

Quizzes will be assigned in Canvas. Quizzes are based on book chapters and in-class discussions. Students have 1 attempt and a 1-hour time limit per quiz. 

Final Project 

A final project that encapsulates the course’s core concepts will be due during Finals week. The project will require students to create a computer program that analyzes biomedical sequencing data and displays results in a human-readable format. 

Points Breakdown 

·       Quizzes — 14 quizzes × 15 points = 210 (34% of course grade) 

·       Assignments — 4 assignments × 100 points = 200 (33% of course grade) 

·       Final Project — 200 (33% of course grade) 

·       Total — 610 points 

Grading Scale

You must earn a minimum grade of “C” (2.0 or above) in this course for it to be counted in a biological science major or minor or as a prerequisite for any other biology course. The percentage/letter grade scale is below; the number in parentheses is the corresponding 4.0-scale grade. 

·       A — 93.0% and above (4.0) 

·       A- — 90.0% (3.7) 

·       B+ — 86.0% (3.3) 

·       B — 83.0% (3.0) 

·       B- — 80.0% (2.7) 

·       C+ — 76.0% (2.3) 

·       C — 73.0% (2.0) 

·       C- — 70.0% (1.7) 

·       D+ — 66.0% (1.3) 

·       D — 63.0% (1.0) 

·       D- — 60.0% (0.7) 

·       F — below 60.0% (0.0) 

Course Outline

Please keep track of Canvas assignments and course announcements by checking Canvas regularly. I will also post updated versions of the syllabus on Canvas as needed.

Week 1 — Handout I · Getting Started 

·       Wed, Sept 2 — Ch 1 | What is a Computer, Anyways? 

·       Fri, Sept 4 — Ch 2 | Information Flow in Biology: The Central Dogma of Genetics 

Week 2 — PCfB · Part II: The Shell 

·       Mon, Sept 7 — Labor Day - No Classes 

·       Wed, Sept 9 — Ch 3 | Next-Generation Sequencing Technologies: A Comparative Analysis 

·       Fri, Sept 11 — Ch 4 | Command-Line Operations: The Shell 

·       Due: Quiz 1: HI | Ch 1, 2, 3 

Week 3 

·       Mon, Sept 14 — Ch 4 | Command-Line Operations: The Shell 

·       Wed, Sept 16 — Ch 5 | Handling Text in the Shell 

·       Due: Quiz 2: PCfB | Ch 4 

·       Fri, Sept 18 — Ch 5 | Handling Text in the Shell 

Week 4 — Handout II · Bioinformatic Workflows 

·       Mon, Sept 21 — Ch 6 | Scripting with the Shell 

·       Due: Quiz 3: PCfB | Ch 5 

·       Wed, Sept 23 — Ch 6 | Scripting with the Shell 

·       Fri, Sept 25 — Ch 1 | Assembling and Annotating a Genome 

·       Due: Quiz 4: PCfB | Ch 6 

Week 5 

·       Mon, Sept 28 — Ch 1 | Assembling and Annotating a Genome 

·       Wed, Sept 30 — Ch 1 | Assembling and Annotating a Genome 

·       Due: Assignment 1: The Shell 

·       Fri, Oct 2 — Ch 2 | Variant Discovery and Genome Browsing 

·       Due: Quiz 5: HII | Ch 1 

Week 6 

·       Mon, Oct 5 — Ch 2 | Variant Discovery and Genome Browsing 

·       Wed, Oct 7 — Ch 2 | Variant Discovery and Genome Browsing 

·       Fri, Oct 9 — Ch 3 | Differential Expression Analysis 

·       Due: Quiz 6: HII | Ch 2 

Week 7 — PCfB · Part III: Programming 

·       Mon, Oct 12 — Ch 3 | Differential Expression Analysis 

·       Wed, Oct 14 — Ch 3 | Differential Expression Analysis 

·       Fri, Oct 16 — Ch 7 | Components of Programming 

·       Due: Quiz 7: HII | Ch 3 

Week 8 

·       Mon, Oct 19 — Fall Break - No Classes 

·       Wed, Oct 21 — Ch 7 | Components of Programming 

·       Due: Assignment 2: Bioinformatic Workflows 

·       Fri, Oct 23 — Ch 8 | Beginning Python Programming 

·       Due: Quiz 8: PCfB | Ch 7 

Week 9 

·       Mon, Oct 26 — Ch 8 | Beginning Python Programming 

·       Wed, Oct 28 — Ch 9 | Decisions and Loops 

·       Due: Quiz 9: PCfB | Ch 8 

·       Fri, Oct 30 — Ch 9 | Decisions and Loops 

Week 10 

·       Mon, Nov 2 — Ch 9 | Decisions and Loops 

·       Wed, Nov 4 — Ch 9 | Decisions and Loops 

·       Fri, Nov 6 — Ch 10 | Reading and Writing Files 

·       Due: Quiz 10: PCfB | Ch 9 

Week 11 

·       Mon, Nov 9 — Ch 10 | Reading and Writing Files 

·       Wed, Nov 11 — Ch 10 | Reading and Writing Files 

·       Due: Assignment 3: Programming I 

·       Fri, Nov 13 — Ch 11 | Merging Files 

·       Due: Quiz 11: PCfB | Ch 10 

Week 12 

·       Mon, Nov 16 — Ch 11 | Merging Files 

·       Wed, Nov 18 — Ch 12 | Modules and Libraries 

·       Due: Quiz 12: PCfB | Ch 11 

·       Fri, Nov 20 — Ch 12 | Modules and Libraries 

Week 13 

·       Mon, Nov 23 — Thanksgiving Break - No Classes 

·       Wed, Nov 25 — Thanksgiving Break - No Classes 

·       Fri, Nov 27 — Thanksgiving Break - No Classes 

Week 14 — PCfB · Part IV: Combining Methods 

·       Mon, Nov 30 — Ch 15 | Relational Databases 

·       Due: Quiz 13: PCfB | Ch 12 

·       Wed, Dec 2 — Ch 15 | Relational Databases 

·       Fri, Dec 4 — Ch 15 | Relational Databases 

·       Due: Assignment 4: Programming II 

Week 15 

·       Mon, Dec 7 — Ch 15 | Relational Databases 

·       Wed, Dec 9 — Ch 15 | Relational Databases 

·       Fri, Dec 11 — Final Project Review 

·       Quiz 14: PCfB | Ch 15 

Week 16 

·       Mon-Thu, Dec 14-17 — Final Exams 

·       Final Project: Thurs Dec 17 11:50 pm 

Instructor's policies on late assignments and/or makeup work

All assignments are due on the assigned date/time. Assignments turned in late will not be graded. If you need to turn in an assignment late due to extenuating circumstances (i.e., university-sponsored travel, illness, ADA accommodation, etc.), please contact me at least 3 days before the due date to discuss the issue and work together to find a solution (obviously, some things like hospitalizations might prevent this). Please note that I cannot guarantee I will be able to provide an accommodation for every assignment.

Attendance Policy

Attendance 

Attendance in this course is taken seriously; please read the following statement carefully to avoid setbacks. Timely arrival is important; showing up late is disrespectful to your fellow students and me. If you know you will frequently be late, please contact me so we can discuss options for full participation. 

iClicker 

The iClicker student app is used to take attendance in this course. This means students need to check in on iClicker at the beginning of each class (they can do this on their phones). If you are unable to log in to iClicker, please notify me before the class ends. Any notifications made after class will not be considered, and the student will be marked absent for that day. 

Absences 

Excused absences include, but are not limited to, the examples listed below. Please note that it is the student’s responsibility to be proactive and notify me of their absence before the scheduled absence date. Notifications made after the date of absence will NOT be considered unless the absence was due to an emergency or accident. Evidence has to be provided for an excused absence. Family trips, personal trips, and vacations are NOT excused absences. Students who miss class will need to contact another professor to attend their lab session during the same week. If there is no lab session to attend, make-up work will be provided. Three unexcused absences will result in an automatic withdrawal (W) from the course. 

Examples of excused absences 

·       Covid-19-related absences 

·       Medical emergencies 

·       Accidents 

·       Inclement weather (snowstorms, floods, etc.) 

·       Interviews for jobs, internships, med school, etc. 

·       Conferences related to academia 

·       University-sponsored absences for student-athletes 

·       Military leave 

·       Jury duty 

Examples of unexcused absences 

·       Family/personal trips and vacations 

·       Job shifts 

·       Attending another professor’s lecture or office hours 

·       Sleeping in/forgetting about class 

ADA Statement

Students with medical, psychological, learning, or other disabilities desiring academic adjustments, accommodations, or auxiliary aids will need to contact the Disability Resource Center, located in Room 206F of the Sharwan Smith Center or by phone at (435) 865-8042. The Disability Resource Center determines eligibility for and authorizes the provision of services.

If your instructor requires attendance, you may need to seek an ADA accommodation to request an exception to this attendance policy. Please contact the Disability Resource Center to determine what, if any, ADA accommodations are reasonable and appropriate.

Academic Credit

According to the federal definition of a Carnegie credit hour: A credit hour of work is the equivalent of approximately 60 minutes of class time or independent study work. A minimum of 45 hours of work by each student is required for each unit of credit. Credit is earned only when course requirements are met. One (1) credit hour is equivalent to 15 contact hours of lecture, discussion, testing, evaluation, or seminar, as well as 30 hours of student homework. An equivalent amount of work is expected for laboratory work, internships, practica, studio, and other academic work leading to the awarding of credit hours. Credit granted for individual courses, labs, or studio classes ranges from 0.5 to 15 credit hours per semester.

Academic Freedom

SUU is operated for the common good of the greater community it serves. The common good depends upon the free search for truth and its free exposition. Academic Freedom is the right of faculty to study, discuss, investigate, teach, and publish. Academic Freedom is essential to these purposes and applies to both teaching and research.

Academic Freedom in the realm of teaching is fundamental for the protection of the rights of the faculty member and of you, the student, with respect to the free pursuit of learning and discovery. Faculty members possess the right to full freedom in the classroom in discussing their subjects. They may present any controversial material relevant to their courses and their intended learning outcomes, but they shall take care not to introduce into their teaching controversial materials which have no relation to the subject being taught or the intended learning outcomes for the course.

As such, students enrolled in any course at SUU may encounter topics, perspectives, and ideas that are unfamiliar or controversial, with the educational intent of providing a meaningful learning environment that fosters your growth and development. These parameters related to Academic Freedom are included in SUU Policy 6.6.

Academic Misconduct

Scholastic honesty is expected of all students. Dishonesty will not be tolerated and will be prosecuted to the fullest extent (see SUU Policy 6.33). You are expected to have read and understood the current SUU student conduct code (SUU Policy 11.2) regarding student responsibilities and rights, the intellectual property policy (SUU Policy 5.52), information about procedures, and what constitutes acceptable behavior.

Please Note: The use of websites or services that sell essays is a violation of these policies; likewise, the use of websites or services that provide answers to assignments, quizzes, or tests is also a violation of these policies. Regarding the use of Generative Artificial Intelligence (AI), you should check with your individual course instructor.

Emergency Management Statement

In case of an emergency, the University's Emergency Notification System (ENS) will be activated. Students are encouraged to maintain updated contact information using the link on the homepage of the mySUU portal. In addition, students are encouraged to familiarize themselves with the Emergency Response Protocols posted in each classroom. Detailed information about the University's emergency management plan can be found at https://www.suu.edu/emergency.

HEOA Compliance Statement

For a full set of Higher Education Opportunity Act (HEOA) compliance statements, please visit https://www.suu.edu/heoa. The sharing of copyrighted material through peer-to-peer (P2P) file sharing, except as provided under U.S. copyright law, is prohibited by law; additional information can be found at https://my.suu.edu/help/article/1096/heoa-compliance-plan.

You are also expected to comply with policies regarding intellectual property (SUU Policy 5.52) and copyright (SUU Policy 5.54).

Mandatory Reporting

University policy (SUU Policy 5.60) requires instructors to report disclosures received from students that indicate they have been subjected to sexual misconduct/harassment. The University defines sexual harassment consistent with Federal Regulations (34 C.F.R. Part 106, Subpart D) to include quid pro quo, hostile environment harassment, sexual assault, dating violence, domestic violence, and stalking. When students communicate this information to an instructor in-person, by email, or within writing assignments, the instructor will report that to the Title IX Coordinator to ensure students receive support from the Title IX Office. A reporting form is available at https://cm.maxient.com/reportingform.php?SouthernUtahUniv

Non-Discrimination Statement

SUU is committed to fostering an inclusive community of lifelong learners and believes our university's encompassing of different views, beliefs, and identities makes us stronger, more innovative, and better prepared for the global society.

SUU does not discriminate on the basis of race, religion, color, national origin, citizenship, sex (including sex discrimination and sexual harassment), sexual orientation, gender identity, age, ancestry, disability status, pregnancy, pregnancy-related conditions, genetic information, military status, veteran status, or other bases protected by applicable law in employment, treatment, admission, access to educational programs and activities, or other University benefits or services.

SUU strives to cultivate a campus environment that encourages freedom of expression from diverse viewpoints. We encourage all to dialogue within a spirit of respect, civility, and decency.

For additional information on non-discrimination, please see SUU Policy 5.27 and/or visit https://www.suu.edu/nondiscrimination.

Pregnancy

Students who are or become pregnant during this course may receive reasonable modifications to facilitate continued access and participation in the course. Pregnancy and related conditions are broadly defined to include pregnancy, childbirth, termination of pregnancy, lactation, related medical conditions, and recovery. To obtain reasonable modifications, please make a request to title9@suu.edu. To learn more visit: https://www.suu.edu/titleix/pregnancy.html.

Disclaimer Statement

Information contained in this syllabus, other than the grading, late assignments, makeup work, and attendance policies, may be subject to change with advance notice, as deemed appropriate by the instructor.