CSE 590C,  Wi '21:  Reading & Research in Comp. Bio.

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Course Info		CSE 590C is a weekly seminar on Readings and Research in Computational Biology, open to all graduate students in computational, biological, and mathematical sciences. When/Where:  Mondays, 3:30 - 4:50, (by zoom) Zoom Link:  https://washington.zoom.us/j/99957044876  (Ask Ruzzo for password.) Organizers:  Su-In Lee, Sara Mostafavi, Bill Noble, Larry Ruzzo, Chris Thachuk, Sheng Wang, Yuliang Wang Credit:  1-3 Variable Grading:  Credit/No Credit. Talk to the organizers if you are unsure of our expectations.
Email		cse590cb@cs.washington.edu Course-related announcements and discussions   Manage Your Subscription List Archives compbio-seminars@cs.washington.edu Biology seminar announcements from all around campus   Manage Your Subscription List Archives compbio-group@cs.washington.edu Computational biology discussions, conference/job postings, etc.,   Manage Your Subscription List Archives
Theme		This quarter's discussion will focus (loosely) on single-cell -omics and multi-omic integration. See suggested papers below, or propose another one.
Schedule		Date  Presenters/Participants Topic Details 01/04 ---- Organizational Meeting ----  Video 01/11 Ayse; Larry Overview of Single Cell Omics Details;  Slides;  Video;  Chat 01/18 Holiday 01/25 Lee, Ethan; Larry Biologically Interpretable Deep Learning On Single-Cell Data Details;  Slides;  Video;  Chat 02/01 Ashley, Addie; Sheng SC Integration with Transfer Learning Details;  Slides;  Video 02/08 Johannes, Aji; Yuliang Benchmarking SC Network Inference Methods Details;  Slides;  Video 02/15 Holiday 02/22 No Class: Instead, check out the CSE colloquium, Zoom link here. 03/01 Erin; Larry Sequence Motif Learning & Representation Details;  Slides;  Video 03/08 No Class
Papers, etc.		Note on Electronic Access to Journals The UW Library is generally a paid subscriber to non-open-access journals we cite. You can freely access these articles from on-campus computers. For off-campus access, follow the "[offcampus]" links below or look at the library "proxy server" instructions. You will be prompted for your UW net ID and password.   01/04:   -- ---- Organizational Meeting ---- 01/11: Overview of Single Cell Omics -- Ayse; Larry Suggested reading: C Paolillo, E Londin, P Fortina, "Single-Cell Genomics." Clin Chem, 65, #8 (2019) 972-985. [offcampus] Other reviews, if you want more: B Hwang, JH Lee, D Bang, "Single-cell RNA sequencing technologies and bioinformatics pipelines." Exp Mol Med, 50, #8 (2018) 96. [offcampus] T Baslan, J Hicks, "Unravelling biology and shifting paradigms in cancer with single-cell sequencing." Nat Rev Cancer, 17, #9 (2017) 557-569. [offcampus] E Hedlund, Q Deng, "Single-cell RNA sequencing: Technical advancements and biological applications." Mol Aspects Med, 59, (2018) 36-46. [offcampus] G Chen, B Ning, T Shi, "Single-Cell RNA-Seq Technologies and Related Computational Data Analysis." Front Genet, 10, (2019) 317. [offcampus] QR Xing, NO Cipta, K Hamashima, YC Liou, CG Koh, YH Loh, "Unraveling Heterogeneity in Transcriptome and Its Regulation Through Single-Cell Multi-Omics Technologies." Front Genet, 11, (2020) 662. [offcampus] 01/18:   -- Holiday 01/25: Biologically Interpretable Deep Learning On Single-Cell Data -- Lee, Ethan; Larry N Fortelny, C Bock, "Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data." Genome Biol, 21, #1 (2020) 190. [offcampus] 02/01: SC Integration with Transfer Learning -- Ashley, Addie; Sheng Lotfollahi, Mohammad, Mohsen Naghipourfar, Malte D. Luecken, Matin Khajavi, Maren Buttner, Ziga Avsec, Alexander V. Misharin, and Fabian J. Theis. "Query to reference single-cell integration with transfer learning." bioRxiv (2020). https://www.biorxiv.org/content/10.1101/2020.07.16.205997v1.full.pdf 02/08: Benchmarking SC Network Inference Methods -- Johannes, Aji; Yuliang A Pratapa, AP Jalihal, JN Law, A Bharadwaj, TM Murali, "Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data." Nat Methods, 17, #2 (2020) 147-154. [offcampus] 02/15:   -- Holiday 02/22:   -- No Class: Instead, check out the CSE colloquium, Zoom link here. 03/01: Sequence Motif Learning & Representation -- Erin; Larry PK Koo, SR Eddy, "Representation learning of genomic sequence motifs with convolutional neural networks." PLoS Comput Biol, 15, #12 (2019) e1007560. 03/08:   -- No Class
Other Papers		Other suggested topics/papers: See suggested papers below, or propose another one. (Contact ruzzo at uw.edu.) Bill suggests: Y Baran, A Bercovich, A Sebe-Pedros, Y Lubling, A Giladi, E Chomsky, Z Meir, M Hoichman, A Lifshitz, A Tanay, "MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions." Genome Biol, 20, #1 (2019) 206. [offcampus] G Xiang, CA Keller, B Giardine, L An, Q Li, Y Zhang, RC Hardison, "S3norm: simultaneous normalization of sequencing depth and signal-to-noise ratio in epigenomic data." Nucleic Acids Res, 48, #8 (2020) e43. [offcampus] Ayse suggests: R Argelaguet, B Velten, D Arnol, S Dietrich, T Zenz, JC Marioni, F Buettner, W Huber, O Stegle, "Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets." Mol Syst Biol, 14, #6 (2018) e8124. [offcampus] Su-In suggests an image genomics paper: B He, L Bergenstråhle, L Stenbeck, A Abid, A Andersson, Å Borg, J Maaskola, J Lundeberg, J Zou, "Integrating spatial gene expression and breast tumour morphology via deep learning." Nat Biomed Eng, 4, #8 (2020) 827-834. [offcampus]
Other Seminars		Past quarters of CSE 590C COMBI & Genome Sciences Seminars
Resources		Molecular Biology for Computer Scientists, a primer by Lawrence Hunter (46 pages) A comprehensive FAQ at bioinformatics.org, including annotated links to online tutorials and lectures. CSE 527: Computational Biology CSEP 527: Computational Biology (Professional Masters Program) Genome 540/541: Introduction to Computational Molecular Biology: Genome and Protein Sequence Analysis CSE's Computational Molecular Biology research group Interdisciplinary Ph.D. program in Computational Molecular Biology

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