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Week of Topic Reading ideas (open to suggestion)!

27-Jan History of sequencing and analysis

3-Feb

10-Feb Networks part I

  • Iancu, O.D., Kawane, S., Bottomly, D., Searles, R., Hitzemann, R. and McWeeney, S., 2012. Utilizing RNA-Seq data for de novo coexpression network inference. Bioinformatics, 28(12), pp.1592-1597.

  • Sinha, S., Jones, B.M., Traniello, I.M., Bukhari, S.A., Halfon, M.S., Hofmann, H.A., Huang, S., Katz, P.S., Keagy, J., Lynch, V.J. and Sokolowski, M.B., 2020. Behavior-related gene regulatory networks: A new level of organization in the brain. Proceedings of the National Academy of Sciences, 117(38), pp.23270-23279.

15-Feb Networks part II

  • Patalano, S., Vlasova, A., Wyatt, C., Ewels, P., Camara, F., Ferreira, P.G., Asher, C.L., Jurkowski, T.P., Segonds-Pichon, A., Bachman, M. and González-Navarrete, I., 2015. Molecular signatures of plastic phenotypes in two eusocial insect species with simple societies. Proceedings of the National Academy of Sciences, 112(45), pp.13970-13975.

22-Feb Gene modules part I

  • Stone, E.A. and Ayroles, J.F., 2009. Modulated modularity clustering as an exploratory tool for functional genomic inference. PLoS Genet, 5(5), p.e1000479.

1-Mar Gene modules part II

  • Morandin, C., Brendel, V.P., Sundström, L., Helanterä, H. and Mikheyev, A.S., 2019. Changes in gene DNA methylation and expression networks accompany caste specialization and age‐related physiological changes in a social insect. Molecular ecology, 28(8), pp.1975-1993.

8-Mar Machine learning part I

  • Jabeen, A., Ahmad, N. and Raza, K., 2018. Machine learning-based state-of-the-art methods for the classification of rna-seq data. In Classification in BioApps (pp. 133-172). Springer, Cham.

15-Mar Machine learning part II

  • Wyatt, C.D., Bentley, M., Taylor, D., Brock, R.E., Taylor, B.A., Bell, E., Leadbeater, E. and Sumner, S., 2020. Genetic toolkit for sociality predicts castes across the spectrum of social complexity in wasps. bioRxiv.

22-Mar Comparative analysis part I: across methods

  • Jaakkola, M.K., Seyednasrollah, F., Mehmood, A. and Elo, L.L., 2017. Comparison of methods to detect differentially expressed genes between single-cell populations. Briefings in bioinformatics, 18(5), pp.735-743.

  • Soneson, C. and Delorenzi, M., 2013. A comparison of methods for differential expression analysis of RNA-seq data. BMC bioinformatics, 14(1), p.91.

29-Mar Comparative analysis part II: across species

  • Hunt, V.L., Hino, A., Yoshida, A. and Kikuchi, T., 2018. Comparative transcriptomics gives insights into the evolution of parasitism in Strongyloides nematodes at the genus, subclade and species level. Scientific reports, 8(1), pp.1-9.

  • Cheng, H., Wang, Y. and Sun, M.A., 2018. Comparison of gene expression profiles in nonmodel eukaryotic organisms with RNA-seq. In Transcriptome Data Analysis (pp. 3-16). Humana Press, New York, NY.

  • Wolf, T., Kämmer, P., Brunke, S. and Linde, J., 2018. Two's company: studying interspecies relationships with dual RNA-seq. Current opinion in microbiology, 42, pp.7-12.

5-Apr Computational workgroup meeting- work on final project

12-Apr Computational workgroup meeting- work on final project

19-Apr Work on final project AND/OR begin final presentations (depending on number of participants)

26-Apr Finish final presentations

*Topics denoted as "part I" will consist mostly of background to the analysis technique and a comparison to conventional pipelines; "part II" will mostly consist of examples of the technique in the literature and a discussion about what the technique does well (or poorly) compared to conventional techniques.

**Final projects will consist at least one practical application of the gene expression analyses covered in class. You are encouraged to use your own data set. Presentations should be approx. 15 minutes long