BMEG3105: Data analytics for personalized genomics and precision medicine-Fall 2022

[Pre-course survey, Piazza, Scribing preference, Logistics, Course schedule and materials, Assignments]

Course description

With social-economic development, people are increasingly caring about health. Consequently, in the field of genomics and healthcare, especially personalized genomics and precision medicine, we have accumulated a tremendous amount of data, which are waiting to be analyzed. This course is designed to equip students with the ability to analyze such data, which would benefit both the students’ personal development and society. In the course, we will cover high-throughput experimental methods, standard data processing pipelines, sequence alignment and mapping, foundational concepts of data analytics, data exploration and visualization, clustering and classification, dimension reduction, and their applications in personalized genomics and precision medicine. For personalized genomics, we will also cover the integration of heterogeneous sequencing and non-sequencing data, single-cell data analysis, multi-omics analysis methods, and cancer genomics. For precision medicine, we will cover protein-RNA interactions, biological graph analysis, and a gentle introduction to biomedical imaging and electronic health records.

Teaching team

Lecturer: Yu LI (, SHB-106. Office hour: 3pm-5pm, Friday
Qinze YU (, SHB-116. Office hour: 2pm-4pm, Monday
Yixuan WANG (, SHB-116. Office hour: 2pm-4pm, Tuesday
Liang HONG (, SHB-116. Office hour: 1pm-3pm, Monday

Time and location

Wednesday: 9:30am-11am, SC-L4.
Friday: 9:30am-10:15am, LSB-LT3.
Friday: 10:30-11:15am, LSB-LT3. Tutorial


Mixed. Slides will be available the day before the lecture day. Video recordings will be available after the lecture.



Blackboard is the main software to manage the course, and grading will be through blackboard. We will use Piazza (BMEG3105) for discussion. You can ask questions through Piazza, even anonymously. For a personal matter, please use the private post to the instructor and the TA. You are also very welcomed to send emails to the instructor and TAs.


Bonus (up to 6%): One bonus question in Midterm. One additional scribing: 1%. Pre-course survey + Post-lecture survey: 0.5% for each, and the maximum is 3%. I do encourage you to complete all of them so that to let me know your feedback and adjust the course accordingly. Send your names to the TA.

Open-book quiz and exam policy

All exams and quiz are open-book. You are allowed to take any paper-based materials. However, no phone or computer is allowed. Other communication tools are also not allowed. Discussion is not allowed.


Python (the TA will prepare a recitation class to introduce it, mainly for the non-grading homework and your project) or any other you are familiar with. For python, we suggest you to use Colab.
The programming credits include Non-grading assignment (10%) and Grading programming included in the project (5%). The bonus is sufficient to cover all the programming credit. If you really do not want to try hand-on experiments at all. We do encourage you try.


Please sign Scribing preference. We should have at least one student for each lecture. We may adjust the assignment if necessary. Notice that your note and scribing will be posted online, for others reference. You can choose to remove your name or not. Deadline for signing the scribing: 11:59 pm on 12th Sep. After that, the Google sheet will be closed.


We will do the project individually. You can give us your project and seek our help or we will predefine some projects for you to choose. Some potential projects:

Both the mid-term report (1 page) and the final report should be submitted.

Late days

Each student will have 6 late days to turn in assignments, which can be used on A1, A2, A3, PA1, and the project mid-term report. They cannot be used on the project final report and the scribing note. A maximum of 2 late days can be used for each assignment. Grades will be deducted by 25% for each additional late day.

Post-lecture survey

Deadline for each survey: 11:59pm on the day before the next lecture. We do this because I could have time to answer the questions you mentioned in the survey. Please fill 1 in the Google sheet: Survey results, once you have finished one survey. Usually, we will trust the 1s you fill in the Google sheet. But we will check the things in detail if the number of survey forms we received and the number of 1s on the Google sheet is not consistent.

Course schedule and materials

Lecture Date Location Topic Slides/Video Notes Reading Important dates (All due at 11:59 pm)
1 Sep 7 (Wed) SC-L4 Introduction Lec-1 note1 Course outline  
2 Sep 9 (Fri) LSB-LT3 Data & Python Lec-2 note1, note2, note3, note4 sample code, python cheat sheet, numpy cheat sheet PA 0 posted
3 Sep 14 (Wed) SC-L4 Sequence and DP Lec-3 note1, note2, note3 Chapter 2&3 PA0 due
4 Sep 16 (Fri) LSB-LT3 Assembly & Mapping Lec-4 note1, note2 Sample code, RNA-seq analysis, intro to python tutorial, anaconda starter guide, conda cheatsheet A1 posted
5 Sep 21 (Wed) SC-L4 Data exploration Lec-5 note1 Python for DA, Sample code, Sample code-2  
6 Sep 23 (Fri) LSB-LT3 Distance and clustering Lec-6 note1 Data mining book, Sample code, Sample code-2, intro to pandas&numpy tutorial  
7 Sep 28 (Wed) SC-L4 Classification Lec-7 note1, note2 Data mining book, Correlation A1 due
8 Sep 30 (Fri) LSB-LT3 Classification Lec-8 note1, note2 Data mining book, tut4 slides  
9 Oct 5 (Wed) SC-L4 Perf evaluation Lec-9 note1, note2, note3 Data mining book A2 posted
10 Oct 7 (Fri) LSB-LT3 Feat selection Lec-10 note1, note2 PML book, tut5 slides note1, note2
11 Oct 12 (Wed) SC-L4 Dim reduction Lec-11   PML book  
12 Oct 14 (Fri) LSB-LT3 Overfitting Lec-12 note1 D2L book, tut6 slides  
13 Oct 19 (Wed) SC-L4 Mid-term review Lec-13   tut A2 (passcode: ef94@^+f) ParticipationQ, A2 due
14 Oct 21 (Fri) LSB-LT3 Mid-term   - - 8:30am-11:15am, Mid-term
      Module 2 start        
15 Oct 26 (Wed) SC-L4 Multi-omics overview Lec-15 note1 Intro to cancer, Cancer genomics PA1 posted
16 Oct 28 (Fri) LSB-LT3 Cancer genomics overview Lec-16 note1, note2 Intro to cancer, Cancer genomics  
17 Nov 2 (Wed) SC-L4 Genomics data analysis Lec-17 note1, note2, note3 Cancer genomics, GATK, GWAS, Epigenetics, ENCODE  
18 Nov 4 (Fri) LSB-LT3 Single cell genomics Lec-18 note1, note2, note3 Current best practice, Tutorial-1, Tutorial-2, Tutorial-3, Clustering challenges , PyTorch Tutorial, GATK-related software installation  
19 Nov 9 (Wed) SC-L4 Data visualization Lec-19 note1 Sequence motif, PCA-tSNE-UMAP Project M-report (Proposal)
20 Nov 11 (Fri) LSB-LT3 TUT Potential Lec-20 note1 Pytorch examples, Tensorflow and PA1, Umap/tSNE/PCA, Scanpy  
      Module 3 start        
21 Nov 16 (Wed) SC-L4 Deep learning/CNN Lec-21 note1 Pytorch examples PA1 due, A3 posted
22 Nov 18 (Fri) LSB-LT3 EHRs & Text Lec-22   EHRs processing  
23 Nov 23 (Wed) SC-L4 Drug & Presentation Lec-23      
24 Nov 25 (Fri) LSB-LT3 Project pres       A3 due
25 Nov 30 (Wed) SC-L4 Course review Lec-25     ParticipationQ
26 Dec 2 (Fri) LSB-LT3 Project pres       Project report