In this course, students will analyze eukaryotic High Throughput Sequencing (aka "Next Generation Sequencing") data. Students will carry out a research project in small groups, have weekly lab meetings, and write lab reports.
Note that this course counts as Writing Intensive (department) but does not count toward Experiential Learning.
Students taking this section must have permission of instructor.
| Texts: | None needed |
| Meetings: | Lecture MW 11-11:50pm, (*TBD*) |
| Workshop Th 9:30-11:20am, (*TBD*) |
| Instructor: | Michael Osier |
| Office: | 08-1338 |
| Instructor Schedule | Online |
| Contact: | mvoscl@rit.edu |
Under certain circumstances, the instructor may have to alter course requirements, assignment deadlines, and grading procedures; and the university may have to alter the academic calendar.
| Monday | Wednesday | Thursday | Writing due | |||
|---|---|---|---|---|---|---|
| Week 1 | Jan 11 | Introduction, Finding data | HTS basics: technologies and analysis, including readings | Unix/Linux command line | None | |
| Week 2 | Jan 18 | No class | HTS basics | Trapnell paper | Exploring Trapnell data set | |
| Week 3 | Jan 25 | Assembler readings | Start Trapnell data set - quality control | Project picking - Speed teaming | Weekly reports | |
| Week 4 | Feb 1 | RNASeq readings | Trapnell data set - QC debrief and alignment | First and second picks with justification (required, due week 5 Monday) | ||
| Week 5 | Feb 8 | Composite structures readings | Trapnell data set - Alignment debrief and differential expression | Weekly reports | ||
| Week 6 | Feb 15 | Barcoding readings | First stage analyses - Quality control | |||
| Week 7 | Feb 22 | eDNA and scRNASeq readings | ||||
| Week 8 | Mar 1 | First stage analyses - Quality control | ||||
| Week 9 | Mar 8 | Spring Break | Optional weekly report | |||
| Week 10 | Mar 15 | Second stage analyses - assembly/alignment | Second stage analyses - assembly/alignment | |||
| Week 11 | Mar 22 | Variant callers and missing genes readings | Weekly reports | |||
| Week 12 | Mar 29 | Third stage analyses - differential expression | Third stage analyses - differential expression and visualization | |||
| Week 13 | Apr 5 | Long read technologies readings | Third stage analyses - differential expression and visualization | Weekly report; Final paper draft 1 - outline | ||
| Week 14 | Apr 12 | Microbiome and epigenetics readings | Third stage analyses - differential expression and visualization | Weekly report; Final paper draft 2 - half of prose/figures | ||
| Week 15 | Apr 19 | Cleanup | Cleanup | |||
| Week 16 | Apr 26 | Cleanup | Exam week activity | |||
| 10 weekly lab reports | 3 pts each x 10 = 30 pts total |
| 3 Trapnell data set Analyses | 10 pts each x 3 = 30 pts |
| Participation in First stage Analysis | 30 pts |
| Participation in Second stage Analysis | 30 pts |
| Participation in Third stage Analysis | 30 pts |
| 3 Final Paper Drafts | 2 pts each = 6 pts |
| Final Paper | 24 pts |
| Two peer evaluations | 10 pts each = 20 pts total |
| Total | 200 pts |
|---|
| A | [190-200] | B+ | [173.3-180) | C+ | [153.3-160) | ||||
| B | [166.7-173.3) | C | [146.7-153.3) | D | [120-140) | F | <120 | ||
| A- | [180-190) | B- | [160-166.7) | C- | [140-146.7) | ||||
Each project will use real HTS data. Student form teams and pick one project during Week 3. Students will spend the rest of the semester analyzing this data set.
For each project, there may be trees of primary, secondary, and tertiary analysis steps, which are dependent on each other. For example, any Secondary Analysis will be dependent upon having a Primary Analysis that was completed correctly.
Participation for each step of analysis will be graded for elements such as completeness, choice of analysis method, appropriate parameter choice, demonstration of active participation, and regular online communications through the myCourses Discussion groups. Individual participation, including full participation in all class sessions, will also be observed and incorporated.
Readings are due before the lecture meetings of that week. These readings are intended both to acclimate you to the topic of the week, and serve as discussion for that week. All papers should be available through myCourses, although some are linked out. In no case should you have to pay for any articles!
Over the semester, students must submit individual lab reports for 10 out of the 12 available weeks, counting the project choice. Reports must be 3/4 to 2 single spaced pages for undergraduates, or 1 to 2 pages for graduate students, submitted to the correct myCourses dropbox before the Thursday meeting. Reports must include the following elements:
A final paper written by the group, explaining your work, in a typical journal format, is due submitted to the myCourses dropbox before the end of the last day of classes (Monday, April 26th, 11:59pm). No late papers will be accepted.
Part of a draft, written by the group, is due during Weeks 13 through 15 on the following schedule. Drafts must be submitted to the appropriate myCourses DropBox before Thursday workshop. Each draft is worth 1% of your final grade, for a total of 3%. No late drafts will be accepted for a grade. For drafts, color your individual text in a color unique to you among your group. You must submit in MS Word, LibreOffice, or OpenOffice format. PDFs or Google Docs (for example) will not be accepted. Word is available from the COS computer labs and the others are free. Be sure to include one comment at the start of the draft with your full name so that your revision color can be identified.
Note that the class may receive official communication in person in class, by email, or in the myCourses Discussion group(s). Any and all are considered official communication methods.
The legal use of distributed material is strictly limited to course activities, and not activity outside the course. The use of copyright protected material outside of this RIT course may be prohibited by law.
All course materials are to be considered copyrighted by Dr. Michael V. Osier, all rights reserved. Any distribution of course materials outside of the immediate course purposes, including making material available to anyone not enrolled in the course, will be handled through all appropriate RIT and legal channels.
Unless written permission is granted by the faculty member, or a specific accommodation has been approved by the Disability Services Office, students are prohibited from recording lectures or presentations.
In making a determination about whether to grant a Late Course Withdrawl or Incomplete, the faculty member will consult with members of the RIT administration to gather the facts. At a minimum, the student must have experienced a major event that was not within their control at any point. All RIT and COS policies and procedures will be followed. For an Incomplete, this also means that the student must have been passing the course at the time the Incomplete was requested.
In the event of imminent disruption, such as due to weather, the class will follow the lead of RIT in making a determination of whether to move to online instruction or cancel class for the affected day(s). Changes to the syllabus will be made by Dr. Osier, potentially without student input, to retain the robustness of overall course content.
In order for a letter of recommendation to be written by Dr. Osier, the student must have earned a "A-" or better in any relevant course, have had no major issues (e.g. disciplinary), have a high quality academic record, and be of good character. In addition, the student must email Dr. Osier at least four weeks in advance with program information, a current transcript, a current CV, and a statement of what the student would like Dr. Osier to emphasize. Priority is given to students who ask first. Dr. Osier reserves the right to limit the number of letters of recommendation written given his availability. Letters will only be submitted directly to the organization applied to and never directly to the student. Dr. Osier reserves the right to determine if any given letter or recommendation will or will not be written.
As with any tool, Artificial Intelligence (AI) is best used for specific circumstances. For courses that I teach, AI may not be used for writing or drafting of an entire assignment or a significant portion of one. This includes, but is not limited to, prose or programming code. It may, however, be used to answer specific questions and enrich your understanding of the course material. For example, while it would not be appropriate to ask AI to write the introduction to a paper, or a subroutine in a program, it would be appropriate to ask AI what is currently known about gene therapy methods including citations, or how to open a file for writing in Java. However, it is your responsibility to check that what the AI is telling you is correct. I have personally seen non-existent citations, or inappropriate citations for a concept, generated by AI of various forms. Another example of appropriate use would be using AI to test your code and find errors that you may have missed. However, be sure to test your code on your own first, as AI is likely to not understand the full context of your code requirements...debugging is your responsibility.
As appropriate in this course, we will explore how AI can be used ethically and appropriately.
Use of AI in a way that is inappropriate by the above rules will be treated as cheating, including any applicable penalties. AI should be used as a tool and not as a replacement for learning.
If you have any questions about whether or not AI usage is approved and appropriate, please email the instructor or stop into office hours. I'm happy to explore this topic with you. Often there is something for me to learn, too.
For the first offense, anyone caught plagiarizing or otherwise cheating will receive a 0 (zero) on the assignment, and be referred to the Head of the School of Life Sciences. In the event of a second offense, the student will receive an "F" for the course. Duplicate submissions or excessive patchwriting will also receive a grade of 0 (zero) for the assignment. In the case of especially egregious offenses, the instructor reserves the right to assign a grade of "F" for the course, as per RIT policy.
If you have any questions about whether or not something constitutes plagiarism and/or cheating, please ask the instructor in advance.
Artificial Intelligence (AI) may not be used in this course except for the exploration of topics. Among other things, it may not be used for writing or drafting papers/reports. You may use it to get advice on how to do an analysis, for example. If you have any questions about acceptable use of AI in this course, please email the instructor immediately.
Contents last updated 9/8/26