BIME 591

Biological Pathway Analysis: Trends and Applications

Winter 2017

Wednesday 11:00-11:50AM SOCC 350

 

Biological pathway analysis plays a vital role in genomics, systems biology, and disease modeling. In this one-credit seminar, we will delve into the evolving roles of pathway resources in genomic analysis and the motivation behind some of the techniques that exist today. I will also introduce some of the tools that will help you, as informaticists, select and perform analyses on available ‑omic or medical data sets.

Don't know what biological pathways are or why they're relevant? We will discuss motivations for these resources in our first class meeting but in the meantime, read the background section.

The following is an overview of the topics we will discuss. It is subject to change and updates as I build the rest of this site. If you have any suggestions for content you would like to see, please send me an email.

 

Class Notes Topic Resources
1/4 pdf Introductions and overview
1/11 pdf Pathway resources, data access, and pathway standards Khatri et al1
1/18 pdf In-class exercise: APIs, SPARQL endpoints, RDF libraries Pathway Commons API2
Intro to SPARQL3
1/25 pdf Pathway enrichment analysis Subramanian et al4
2/1 pdf In-class exercise: GSEA and other methods gsea_example.tar.gz
2/8 pdf Pathway visualization tools and techniques Ideker et al5
King et al6
2/15 pdf In-class exercise: Cytoscape for visualizing pathways
2/22 pdf Modeling disease pathways and networks Elbers et al7
3/1 *Guest lecture*
3/8 pdf Inferring gene regulatory networks from data Ghosh et al8
Finals No finals

 

[1]  Khatri, P., Sirota, M., & Butte, A. J. (2012). Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges. PLoS Computational Biology, 8(2), e1002375. http://doi.org/10.1371/journal.pcbi.1002375 [PubMed, PDF]

[2]  Pathway Commons, the Web API. [link]

[3]  Sequeda, J. Introduction to: SPARQL [link]

[4]  Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A. et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Science, 102(43): 15545-50. https://dx.doi.org/10.1073/pnas.0506580102 [PubMed, PDF]

[5]  Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D. et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research, 13: 2498-504. https://dx.doi.org/10.1101/gr.1239303 [PubMed, PDF]

[6]  King, Z. A., Dra╠łger, A., Ebrahim, A., Sonnenschein, N., Lewis, N. E., and Palsson, B. O. (2015). Escher: a web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLoS Computational Biology, 11(8): e1004321. https://dx.doi.org/10.1371/journal.pcbi.1004321 [PubMed, PDF]

[7]  Elbers, C. C., van Eijk, K. R., Franke, L., Mulder, F., van der Schouw, Y. T., Wijmenga, C., & Onland-Moret, N. C. (2009). Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genetic Epidemiology, 33(5): 419-31. http://doi.org/10.1002/gepi.20395 [PubMed, PDF]

[8]  Ghosh, S., Matsuoka, Y., Asai, Y., Hsin, K. Y., & Kitano, H. (2011). Software for Systems Biology: From Tools to Integrated Platforms. Nature Reviews Genetics, 12(12): 821-32. http://doi.org/10.1038/nrg3096 [PubMed, PDF]