Weekly Discussion Sections & Readings
Time and Location
| Session | Time | Location | Note |
|---|---|---|---|
| Section 1 | Mon 11am-12am | YSB 352 | ZC |
| Section 2 | Thur 12-1pm | YSB 352 | SZ |
| Section 3 | Thur 1-2pm | YSB 352 | MY |
| Section 4 | Fri 10-11am | YSB 352 | DB |
Format
The standard discussion section involves student presentations on 1 or 2 papers. Some discussion sections will involve hands-on skill-building demos taught by the teaching fellows, such as the use of R, High Performance Computing, and GitHub. The exact format will be determined based on the size of the class. However, we generally require the following:
- Each week, students should read the assigned papers and write at a minimum of 200 words (half a page, single-spaced, per paper) summaries of each paper (two articles = approx. 1 page). We would like to encourage electronic submission, via Canvas. For those who have trouble accessing canvas, we will also accept submission over email to cbb752 (at) gersteinlab.org BEFORE the start of each section.
- Each student will give one presentation about a selected paper (approx. 20 min) in one of the sessions.
- Students will be graded on a combination of the written summary, presentation, and participation in discussions.
- If you are presenting, you are exempt from writing a summary.
- Please notify TFs in advance if you cannot come to the discussion session. Student can miss up to one discussion section without a penalty.
For write-ups and presentation, think about the following:
- What was missing in the field? (introduction/background)
- What were the questions the paper aim to address? (hypothesis)
- What they did and what was the breakthrough? (method/results)
- Conclusion and future direction (discussion/conclusion)
- Questions you have about the paper, can be either elucidatory or critical
Section Readings
Reading assignments for discussion sessions are listed below:
(Optional) Suggested Reading for Week 1
- How to (seriously) read a scientific paper, on your own. [Link]
Session 1, 1/23(Thur), 1/24(Fri), 1/27(Mon)
Topic
- Proteomics
Reading Assignment
- A draft map of the human proteome. Nature 509,575–581 (29 May 2014) [PDF]
- Mass-spectrometry-based draft of the human proteome. Nature 509, 582–587 (29 May 2014) [PDF]
Session 2, 1/30(Thur), 1/31(Fri), 2/3(Mon)
Topic
- Next-Gen Sequencing and database
Reading Assignment
- Wheeler DA et al. “The complete genome of an individual by massively parallel DNA sequencing,” Nature. 452:872-876 (2008) [PDF]
- Logsdon GA et al. “Long-read human genome sequencing and its applications” Nature Reviews Genetics. 21:597-614 (2020) [PDF]
Session 3, 2/6(Thur), 2/7(Fri), 2/10(Mon)
Topic
- Debate I
Reading Assignment
- Gencode vs Salzberg et al. debate
- (Main paper) Salzberg et al. CHESS paper using GTEx [PDF]
- (Main paper) GENCODE’s rebuttal [PDF]
- (Optional) New human gene tally reignites debate [News Article]
- (Optional) Why most published research finding are false [PDF]
–
Session 4, 2/13(Thur), 2/14(Fri), 2/17(Mon)
Topic
- Review Session for quiz
Reading Assignment
- none
Session 5, 2/20(Thur), 2/21(Fri), 2/24(Mon)
Topic
Debate II - Cancer incidence
Reading Assignment
- Variation in cancer risk among tissues can be explained by the number of stem cell divisions [Link]
- Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention [Link]
The following two are short comments on the above paper. You should read these comments as well.
- Debate reignites over the contributions of ‘bad luck’ mutations to cancer [Link]
- The simple math that explains why you may (or may not) get cancer [Link]
Session 6, 2/27(Thur), 2/28(Fri), 3/3(Mon)
Topic
- Single Cell
Reading Assignment
- Temporal modelling using single-cell transcriptomics [Link]
- Single-cell landscape of immunological responses in patients with COVID-19 [Link]
Session 7, 3/27(Thur), 3/28(Fri), 3/31(Mon)
Topic
Additional Topics: Biomedical Image analysis / Multimodal learning
Reading Assignment
- Multimodal biomedical AI [Link]
- A visual–language foundation model for pathology image analysis using medical Twitter [Link]
Session 8, 4/3(Thur), 4/4(Fri), 4/7(Mon)
Topic
- Deep learning I