Section Readings and MOOC Lectures

Section 1: Monday 2:15 - 3:15pm, students will give 15 ~ 20 min presentations of the papers (Bass 405). To sign up for presentations, see the spreadsheet. 

Section 2: Monday 4:00 - 5:00pm, will have a discussion format. (Bass 405 or Bass 205)

Each section will include discussion of papers assigned (below). Students are expected to bring 1-2 paragraph summaries of each paper to the section (hard copy is preferred). The written assignment will be the same, and students will be graded on a combination of the written assignments and your participation in discussions.

MOOC Lectures (for class on March 2 and March 4):

X. Advice for Applying Machine Learning (Week 6)

10.1: https://www.youtube.com/watch?v=DYCv5e0Isow

10.2: https://www.youtube.com/watch?v=75PiDvoQc7o

10.3: https://www.youtube.com/watch?v=2bgDlLJtjoQ

10.4: https://www.youtube.com/watch?v=wfmmNmXqGrc

10.5: https://www.youtube.com/watch?v=pk8sz0akmck

10.6: https://www.youtube.com/watch?v=g4XluwGYPaA

10.7: https://www.youtube.com/watch?v=TxJe4xeDI7g

 XI. Machine Learning System Design (Week 6)

11.1: https://www.youtube.com/watch?v=E9Ki-sThq5E

11.2: https://www.youtube.com/watch?v=cSehNXLYU54

11.3: https://www.youtube.com/watch?v=Aikq-iPQtx0

11.4: https://www.youtube.com/watch?v=OEYvj3sW9t4

11.5: https://www.youtube.com/watch?v=eVA-n9V4fQ8

Section Readings

Session 1: Next Gen Sequencing 

Metzker ML. "Sequencing technologies - the next generation” Nature Reviews Genetics. 11 (2010) PDF 

Wheeler DA et al. "The complete genome of an individual by massively parallel DNA sequencing,” Nature. 452:872-876 (2008) PDF 

Session 2: Proteomics/Sequence Alignment 

T.F. Smith and M.S. Waterman. (1981) Identification of common molecular subsequences. Journal of Molecular Biology,147(1): 195-7. PMID: 7265238. PDF  

Nevan J. Krogan et al (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae Nature 440, 637-643 (30 March 2006) PDF 

Additional readings suggested by Professor Rinehart 

Session 3: Sequence Alignment/Machine learning 

Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. (1990) Basic local alignment search tool. Journal of Molecular Biology, 215(3):403-10. PMID: 2231712. PDF 

Yip, KY, Cheng, C, Gerstein, M (2013). Machine learning and genome annotation: a match meant to be?. Genome Biol., 14, 5:205. PDF 

Session 4: Bioinformatics for Next-Gen Sequencing 

Rozowsky, J, Euskirchen, G, Auerbach, RK, Zhang, ZD, Gibson, T, Bjornson, R, Carriero, N, Snyder, M, Gerstein, MB (2009). PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nat. Biotechnol., 27, 1:66-75 PDF 

Cooper, GM, Shendure, J (2011). Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nat. Rev. Genet., 12, 9:628-40 PDF

Session 5: Networks 

Ekman D, Light S, Björklund AK, Elofsson A. (2006) What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae? Genome Biol. 2006;7(6):R45. PDF 

Barabási, AL, Oltvai, ZN (2004). Network biology: understanding the cell's functional organization. Nat. Rev. Genet., 5, 2:101-13. PDF 

Session 6: Immunological Modeling/Semantic Web 

Perelson AS. Modelling viral and immune system dynamics. Nat Rev Immunol. 2002 Jan;2(1):28-36. PDF 

Antezana E, Egaña M, Blondé W, Illarramendi A, Bilbao I, De Baets B, Stevens R, Mironov V, Kuiper M. (2009) The Cell Cycle Ontology: an application ontology for the representation and integrated analysis of the cell cycle process. Genome Biol. 2009;10(5):R58. Epub 2009 May 29. PDF 

Session 7: Protein Simulation 1 

Martin Karplus and J. Andrew McCammon. (2002) Molecular dynamics simulations of biomolecules. Nature Structural Biology,9, 646-52. PMID: 12198485.PDF 

Zhou, AQ, O'Hern, CS, Regan, L (2011). Revisiting the Ramachandran plot from a new angle. Protein Sci., 20, 7:1166-71 PDF 

Session 8: Protein Simulation 2 

Dill KA, Ozkan SB, Shell MS, Weikl TR. (2008) The Protein Folding Problem.Annu Rev Biophys,9, 37:289-316. PMID: 2443096.PDF 

Bowman GR, Beauchamp KA, Boxer G, Pande VS. “Progress and challenges in the automated construction of Markov state models for full protein systems,” J. Chem. Phys. 131 (2009) 124101 PDF

To sign up for presentations, click  http://goo.gl/TmBfoz 

Google Spreadsheet