Welcome to the home page for BIOL 5496/CSE 7800, the Computational Molecular Biology Journal Club. The Journal Club meets Tuesdays at 10:30 AM in 4515 McKinley Rm 6001B.
Your course masters are Jeremy Buhler and Gary Stormo.
Participants in the Journal Club present recent published research in the areas of computational molecular biology, systems biology, and bioinformatics. Every student receiving credit for participation must present a paper at one meeting of the Journal Club and must otherwise participate as described in the course overview. Other students, postdocs, faculty, and staff are welcome to attend and participate.
Announcements of upcoming meetings will be made via the mailing list firstname.lastname@example.org. Please ask one of the course masters if you want to join the list.
|Jan 15||Organizational Meeting||(at 11:30 AM, Rm 4304)|
|Jan 22||no meeting|
|Jan 29||Ariel Hernandez-Leyva||B Wang, AM Mezlini, F Demir, M Fiume, et al. Similarity network fusion for aggregating data types on a genomic scale. Nature Methods 11:333-337, 2014.|
|Feb 5||Nick Jensen||EF Lock, KA Hoadley, JS Marron, and AB Nobel. Joint and individual variation explained (JIVE) for integrated analysis of multiple data types. Annals of Applied Statistics 7:523-542, 2013.|
|Feb 12||Ethan Stancliffe||C Cotten and JL Reed. Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models. BMC Bioinformatics 14:32, 2013.|
|Feb 19||Emily Butka||E Becht, L McInnes, J Healy, C-A Dutertre, et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nature Biotechnology 37:38-44, 2019.|
|Feb 26||Luke Diorio-Toth||S Raguideau, S Plancade, N Pons, M Leclerc, and B Laroche. Inferring aggregated functional traits from metagenomic data using constrained non-negative matrix factorization: application to fiber degradation in the human gut microbiota. PLOS Computational Biology 12:e1005252, 2016.|
|Mar 5||Mati Nemera||HM Levitin, J Yuan, YL Cheng, FJR Ruiz, et al. De novo gene signature identification from single-cell RNA-seq with hierarchical Poisson factorization. Molecular Systems Biology 15(2):e8557, 2019.|
|Mar 12||Spring Break -- no meeting|
|Mar 19||Avi Ramu||LA Furchtgott, S Melton, V Menon, and S Ramanathan. Discovering sparse transcription factor codes for cell states and state transitions during development. eLIFE 6:e20488, 2017.|
|Mar 26||Hyeim Jung||P Lin, M Troup, and JWK Ho. CIDR: ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biology 18:59, 2019.|
|Apr 2||Kai Loell||J Wang, M Huang, E Torre, H Dueck, S Shaffer, et al. Gene expression distribution deconvolution in single-cell RNA sequencing. PNAS 115(28):E6437-E6446, 2018.|
|Apr 9||Patrick DeSouza||G Eraslan, LM Simon, M Mircea, NS Mueller, and FJ Theis. Single-cell RNA-seq denoising using a deep count autoencoder. Nature Communications 10:390, 2019.|
|Apr 16||Julia Wang||H Dai, L Li, T Zeng, and L Chen. Cell-specific nework constructed by single-cell RNA sequencing data. Nucleic Acids Research gkz172, 2019.|