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Contents 1 CBB 752 1.1 Course Information 1.1.1 Course Description 1.1.2 Concise undergraduate course description 1.1.3 Different headings for this class 1.1.4 Prerequisites 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/Class Schedule 1.5 Discussion Section Readings 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 1.6 Class Requirements 1.6.1 Discussion Section / Readings 1.6.2 Bioinformatics quizzes 1.6.3 Programming Assignments (CBB and CS) 1.6.4 Non-CBB Final Project 1.6.5 CBB Final Project 1.6.6 Grade Categories 1.6.6.1 CBB and CPSC Sections: 1.6.6.2 MBB and MCDB Sections: 1.6.7 Relevant Yale College Regulations 1.7 Misc 1.8 Pages from previous years

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/MB&B753/MB&B754/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.

Different headings for this class

MB&B452/MCDB452

This version of the course consists of lectures, written problem sets, and a final written project.

MB&B752/MCDB752

This version of the course consists of lectures, written problem sets, and a final, graduate level written project.

CB&B752/CPSC752

This version of the course consists of lectures, programming assignments, and a final programming project.

For graduate students the course is broken up into two "modules" (each counting 0.5 credit towards MB&B course requirement):

MB&B 753b3, Bioinformatics: Practical Application of Data Mining (1st half of term)

MB&B 754b4, Bioinformatics: Practical Application of Simulation (2nd half of term)

Each module consists of lectures, written problem sets, and a final, graduate level written project that is half the length of the full course's final project.

For the grade weighting schemes of each course version, see Class Requirements section.

Prerequisites

The course is keyed towards CBB graduate students as well as advanced MB&B undergraduates and graduate students wishing to learn about types of large-scale quantitative analyses that whole-genome sequencing will make possible. It would also be suitable for students from other fields such as computer science or physics wanting to learn about an important new biological application for computation.

Students should have:

A basic knowledge of biochemistry and molecular biology. A knowledge of basic quantitative concepts, such as single variable calculus, some probability and statistics, and basic programming skills. These can be fulfilled by the following prerequisites statement: "Prerequisites: MBB 200 and Mathematics 115 or permission of the instructor."

Timing & location

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

Discussion section: Poll for students to indicate good times for the weekly discussion section

Instructors

Instructor-in-Charge

Name	Office	Email
Mark Gerstein	Bass 432A	mark.gerstein(at)yale.edu

Instructors

Name	Office	Email
Corey O'Hern	Mason Laboratory	corey.ohern(at)yale.edu
Jesse Rinehart	300 George St	jesse.rinehart(at)yale.edu
James Noonan	333 Cedar St	james.noonan(at)yale.edu
Kei Cheung	300 George St	kei.cheung(at)yale.edu
Steven Kleinstein	300 George St	steven.kleinstein(at)yale.edu

Teaching Fellows

Name	Office	Email
Lucas Lochovsky	Bass 437	lucas.lochovsky(at)yale.edu
Jane Leng	Bass 437	jing.leng(at)yale.edu

Topics/Class Schedule

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

Discussion Section Readings

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

Class Requirements

Discussion Section / Readings

Papers will be assigned throughout the course. These papers will be presented and discussed in weekly 60-minute sections with the TFs. A brief summary (a half-page per article) should be submitted at the beginning of the discussion session.

Bioinformatics quizzes

There will be four short quizzes (25 minutes) in class comprising SIMPLE questions that you should be able to answer from the lectures plus the main readings.

Programming Assignments (CBB and CS)

There will be several short programming assignments required for CBB and CS students taking this course. Acceptable languages and submission requirements will be discussed prior to the first assignment. These assignments are NOT required for students not taking the CBB or CS sections of the course.

Non-CBB Final Project

[tbd]

CBB Final Project

[tbd]

Grade Categories

CBB and CPSC Sections:

Category	% of Total Grade
Quizzes	33.25%
Final Project	33.25%
Discussion Section	8.50%
Programming Assignments	25.00%

MBB and MCDB Sections:

Category	% of Total Grade
Quizzes	33.25%
Final Project	33.25%
Discussion Section	16.75%
Problem Sets	16.75%

Relevant Yale College Regulations

Students may have questions concerning end-of-term matters. Links to further information about these regulations can be found below:

http://yalecollege.yale.edu/content/reading-period-and-final-examination-period

http://yalecollege.yale.edu/content/completion-course-work

Misc

Permissions on using website material

Graphic for course homepage

If you're really motivated, take a look at http://gersteinlab.org/jobs for further Research Opportunities

Pages from previous years

2011, 2010, 2009 and earlier