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	Jane Leng, Bass Rm 437, (203) 432-5405		Jane Leng, Bass Rm 437, (203) 432-5405
		+
		+	==Topics==
		+
		+	[https://spreadsheets.google.com/pub?key=0AnSCkRiRBZ5KdEp1T0NuWkZHTjZKLXZyV1Y0WnFNZGc&hl=en&output=html Class Schedule] (including a list of topics and quiz dates)
		+
		+	==Discussion Sections==
		+
		+	===Session 1===
		+	Metzker ML. "Sequencing technologies - the next generation” Nature Reviews Genetics. 11 (2010) [http://www.gersteinlab.org/courses/452/10-spring/pdf/ngs.pdf PDF]
		+
		+	Wheeler DA et al. "The complete genome of an individual by massively parallel DNA sequencing,” Nature. 452:872-876 (208) [http://www.gersteinlab.org/courses/452/10-spring/pdf/WatsonGenome.pdf PDF]
		+
		+	===Session 2===
		+	Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M. (2006) Global, in vivo, and site-specific phosphorylation dynamics in signaling networks.Cell. 2006 Nov 3;127(3):635-48. [http://www.pil.sdu.dk/1/MSQuant/Cell_GlobalPhosphorylationSignalingDynamics.pdf PDF]
		+
		+	Nevan J. Krogan et al (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae Nature 440, 637-643 (30 March 2006) [http://www.nature.com/nature/journal/v440/n7084/pdf/nature04670.pdf PDF]
		+
		+	===Session 3===
		+	T.F. Smith and M.S. Waterman. (1981) Identification of common molecular subsequences. Journal of Molecular Biology,147(1): 195-7. PMID: 7265238. [http://www.gersteinlab.org/courses/452/10-spring/pdf/sw.pdf PDF]
		+
		+	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. [http://www.gersteinlab.org/courses/452/10-spring/pdf/Altschul.pdf PDF]
		+
		+	===Session 4===
		+	Bailey TL, Williams N, Misleh C, Li WW. (2006) MEME: discovering and analyzing DNA and protein sequence motifs, Nucl Acids Res.34:W369-373 [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1538909/?tool=pubmed PDF]
		+
		+	Garnier J, Gibrat JF, Robson B. (1996) GOR method for predicting protein secondary structure from amino acid sequence.Methods in Enzymology,266: 540-53. PMID: 8743705. [http://www.gersteinlab.org/courses/452/10-spring/pdf/GOR.pdf PDF]
		+
		+	===Session 5===
		+	Laura J. van 't Veer et al. Gene expression profiling predicts clinical outcome of breast cancer Nature 415, 530-536 (31 January 2002) | doi:10.1038/415530a; Received 24 August 2001; Accepted 22 November 2001 [http://www.nature.com/nature/journal/v415/n6871/full/415530a.html TEXT]
		+
		+	Kwang-Il Goh, Michael E. Cusick, David Vall, Barton Child, Marc Vidal, and Albert-La ́szlo ́ Barabasi (2007) The human disease network Proc Natl Acad Sci U S A. 2007 May 22;104(21):8685-90. Epub 2007 May 14. [http://www.pnas.org/content/104/21/8685.full.pdf+html PDF]
		+
		+	===Session 6===
		+	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. [http://genomebiology.com/content/10/5/R58 PDF]
		+
		+	===Session 7===
		+	Perelson AS. Modelling viral and immune system dynamics. Nat Rev Immunol. 2002 Jan;2(1):28-36. [http://www.gersteinlab.org/courses/452/10-spring/pdf/perelson.pdf PDF]
		+
		+	===Session 8===
		+	ML Connolly. (1983) Solvent-accessible surfaces of proteins and nucleic acids. Science, 221(4612): 709-13. PMID: 6879170.[http://www.gersteinlab.org/courses/452/10-spring/pdf/Connolly.pdf PDF]
		+
		+	Martin Karplus and J. Andrew McCammon. (2002) Molecular dynamics simulations of biomolecules. Nature Structural Biology,9, 646-52. PMID: 12198485.[http://www.gersteinlab.org/courses/452/10-spring/pdf/Karplus.pdf PDF]
		+
		+	===Session 9===
		+	Dill KA, Ozkan SB, Shell MS, Weikl TR. (2008) The Protein Folding Problem.Annu Rev Biophys,9, 37:289-316. PMID: 2443096.[http://www.gersteinlab.org/courses/452/10-spring/pdf/proteinFolding.pdf 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 [http://www.gersteinlab.org/courses/452/10-spring/pdf/bowman.pdf PDF]

---

Revision as of 20:50, 17 September 2011

Contents 1 CBB 752 1.1 Course Information 1.1.1 Course Description 1.1.2 Concise undergraduate course description 1.1.3 Quizzes and Final Project 1.1.4 Literature discussion section 1.1.5 Programming Projects/Problem Sets 1.1.6 Grade Categories 1.1.6.1 CBB and CPSC Sections: 1.1.6.2 MBB and MCDB Sections: 1.1.7 Differences Between Class Sections 1.2 Timing & location 1.3 Instructors 1.3.1 Instructor-in-Charge 1.3.2 Instructors 1.3.3 Teaching Fellows 1.4 Topics 1.5 Discussion Sections 1.5.1 Session 1 1.5.2 Session 2 1.5.3 Session 3 1.5.4 Session 4 1.5.5 Session 5 1.5.6 Session 6 1.5.7 Session 7 1.5.8 Session 8 1.5.9 Session 9

CBB 752

Course Information

Course Description

Bioinformatics encompasses the analysis of gene sequences, macromolecular structures, and functional genomics data on a large scale. It represents a major practical application for modern techniques in data mining and simulation. Specific topics to be covered include sequence alignment, large-scale processing, next-generation sequencing data, comparative genomics, phylogenetics, biological database design, geometric analysis of protein structure, molecular-dynamics simulation, biological networks, normalization of microarray data, mining of functional genomics data sets, and machine learning approaches for data integration.

Concise undergraduate course description

Techniques in data mining and simulation applied to bioinformatics, the computational analysis of gene sequences, macromolecular structures, and functional genomics data on a large scale. Sequence alignment, comparative genomics and phylogenetics, biological databases, geometric analysis of protein structure, molecular-dynamics simulation, biological networks, microarray normalization, and machine-learning approaches to data integration.

See entry from undergraduate catalog: http://students.yale.edu/oci/resultDetail.jsp?course=21914&term=201201 , viz:

MB&B 452 01 (21914) /MCDB452/MB&B752/CB&B752/MCDB752/CPSC752
Bioinformatics: Practical Application of Simulation and Data Mining 
Mark Gerstein
MW 1.00-2.15 BASS 305
Spring 2012 
No regular final examination
Areas Sc
Prerequisites: MB&B 301b and MATH 115a or b, or permission of instructor.
MCDB 120a or 200b is a prerequisite for courses numbered MCDB 202 and above.

Quizzes and Final Project

There will be approximately four short quizzes during the semester and a take-home final project. For CBB and CS sections, the final project will be a programming assignment. For MB&B, the final project will be a paper. Further details will be announced at a later date.

Literature discussion section

One session of 60 minutes per week, time to be arranged. Student presentations of recent research papers relevant to the topics of the course. Led by Lucas Lochovsky (Bass, Rm 437; 432-5405; lucas.lochovsky(at)yale.edu) and Jane Leng (Bass, Rm 437; 432-5405; jing.leng(at)yale.edu).

Programming Projects/Problem Sets

Students taking this course listed under Computational Biology and Bioinformatics or Computer Science will be required to complete several short programming assignments. Further details will be discussed in the literature discussion section and during class.

Grade Categories

CBB and CPSC Sections:

Quizzes - 33% Final Project - 33% Discussion Section - 8.25% Programming Assignments - 24.75%

MBB and MCDB Sections:

Quizzes - 33% Final Project - 33% Discussion Section - 16.5% Problem Sets - 16.5%

Differences Between Class Sections

In general, the graduate level CS/CBB course is significantly different than MBB/MCDB (graduate and undergraduate) in several ways. Although the lectures are the same for each section, the graduate level CPSC/CBB course has additional programming assignments in addition to the work being completed by the MBB students. homework for the MBB section centers on the completion of several problem sets without a programming component. The CPSC/CBB section forgoes these problem sets and instead requires that students implement several of the algorithms discussed in class. Also, the final project for CPSC/CBB MUST be a programming assignment rather than the final paper equired for the MBB section. Due to the distinct course requirements, category weightings for final grades are also different.

Timing & location

Class: Meeting from 1:00-2:15 pm on Monday and Wednesday, in 305 BASS. (First meeting will be on 9 Jan.)

Discussion section: TBA

Instructors

Instructor-in-Charge

Mark Gerstein, 432A BASS, Phone 203 432-6105, e-mail mark.gerstein(at)yale.edu

Instructors

Corey O'Hern, Mason Laboratory e-mail corey.ohern(at)yale.edu, Office Hours: M 2:15-3:15 PM

Others to be listed

Teaching Fellows

Lucas Lochovsky, Bass Rm 437, (203) 432-5405

Jane Leng, Bass Rm 437, (203) 432-5405

Topics

Class Schedule (including a list of topics and quiz dates)

Discussion Sections

Session 1

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 (208) PDF

Session 2

Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M. (2006) Global, in vivo, and site-specific phosphorylation dynamics in signaling networks.Cell. 2006 Nov 3;127(3):635-48. 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

Session 3

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

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

Session 4

Bailey TL, Williams N, Misleh C, Li WW. (2006) MEME: discovering and analyzing DNA and protein sequence motifs, Nucl Acids Res.34:W369-373 PDF

Garnier J, Gibrat JF, Robson B. (1996) GOR method for predicting protein secondary structure from amino acid sequence.Methods in Enzymology,266: 540-53. PMID: 8743705. PDF

Session 5

Laura J. van 't Veer et al. Gene expression profiling predicts clinical outcome of breast cancer Nature 415, 530-536 (31 January 2002) | doi:10.1038/415530a; Received 24 August 2001; Accepted 22 November 2001 TEXT

Kwang-Il Goh, Michael E. Cusick, David Vall, Barton Child, Marc Vidal, and Albert-La ́szlo ́ Barabasi (2007) The human disease network Proc Natl Acad Sci U S A. 2007 May 22;104(21):8685-90. Epub 2007 May 14. PDF

Session 6

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

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

Session 8

ML Connolly. (1983) Solvent-accessible surfaces of proteins and nucleic acids. Science, 221(4612): 709-13. PMID: 6879170.PDF

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

Session 9

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