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Bioinformatics: Practical Application of Data Mining and Simulation

the 17th iteration at Yale!


Course will be offered starting 13 Jan 2014 at 1p in Bass 305. Office hours in Bass 432a immediately after the class and before the next class.


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


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=23441&term=201401, viz:

MB&B 452 01 (23441) /MCDB452/CB&B752/MCDB752/CPSC752/MB&B452 
Mark Gerstein
MW 1.00-2.15
BASS 305
Fall 2014
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


This version of the course consists of lectures, written problem sets, and a final (semi-computational section and a literature survey) project.


This version of the course consists of lectures, written problem sets, and a final (semi-computational section and a literature survey) project.


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

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

MB&B 753a3, Bioinformatics: Practical Application of Data Mining (1st half of term)

MB&B 754a4, 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.


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 13 Jan 2014 (Mon). The third meeting will be 17 Jan 2014 (Fri), as part of Yale's compensation for canceling classes on 20 Jan 2014 (Mon.), in observance of MLK day. See Course Schedule for details.)

Discussion section: TBD First Section: Week of 1/20/2014

Each section will include discussion of papers assigned (below) and each paper will be presented by a student. Each presentation should be approx. 10 min. Powerpoint slides are optional. Please note the presentation is separate from the write-up, i.e. you still need to do the write-up.


Consultation is available UPON REQUEST or according to times stipulated by the individual instructors.


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

Guest 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
Michael Rutenberg Schoenberg Bass 437 michael.rutenbergschoenberg(at)yale.edu
Cong Li 300 George, Suite 503 cong.li(at)yale.edu

Discussion Section Readings

Session 1: Next Gen Sequencing (Experimental)

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: 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

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 3: Proteomics

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 4: Machine Learning/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

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

Session 5: Bioinformatics for Next-Gen Sequencing 2

Lior Pachter. Models for Transcript Quantifications from RNA-Seq (2011) ArXiV 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 6: 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 7: 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 8: 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 9: 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

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.

Answer keys to Quizzes 1-4 cbb752a12: found here

Programming Assignments (CBB and CS) and Programming issues

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.

These are the programming languages that we permit in the programming assignments and final project: Perl, Python, C, C++, MATLAB and R. If you really feel more comfortable with other languages, please email the TFs to discuss. Also, packages such as BioPerl and BioPython are not allowed in the assignments and final project. If in doubt, please consult the TFs.

We recommend the use of PERL for most of the programming. A useful resource is the following book: Programming Perl, 3rd Edition in the O' Reilly series, by Larry Wall, Tom Christiansen, Jon Orwant. The Yale Library has also older editions, which would work too. We would also recommend the following online resources: http://www.perlmonks.org/ and http://stackoverflow.com/. Otherwise, Google is your best friend.

Assignment postings

Final Project

Grade Categories

The following are the approximate grading systems:

CBB and CPSC Sections:

Category  % of Total Grade
Quizzes 33%
Final Project 33%
Discussion Section 9%
Programming Assignments 25%

MBB and MCDB Sections:

Category  % of Total Grade
Quizzes 33%
Final Project 33%
Discussion Section 17%
Problem Sets 17%

Relevant Yale College Regulations

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



Brief presentation on how to cite correctly : http://archive.gersteinlab.org/mark/out/log/2012/06.12/cbb752b12/cbb752_cite.ppt


Below is a message from Dean Mary Miller of Yale College about citing your references and sources of information and plagiarism:

" You need to cite all sources used for papers, including drafts of papers, and repeat the reference each time you use the source in your written work. You need to place quotation marks around any cited or cut-and-pasted materials, IN ADDITION TO footnoting or otherwise marking the source. If you do not quote directly – that is, if you paraphrase – you still need to mark your source each time you use borrowed material. Otherwise you have plagiarized. It is also advisable that you list all sources consulted for the draft or paper in the closing materials, such as a bibliography or roster of sources consulted.
You may not submit the same paper, or substantially the same paper, in more than one course. If topics for two courses coincide, you need written permission from both instructors before either combining work on two papers or revising an earlier paper for submission to a new course.

It is the policy of Yale College that all cases of academic dishonesty be reported to the chair of the Executive Committee.... "

Also, it might be of interest to people, to look at this recent article regarding academic dishonesty.


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


Poll for students' sign up and good times for the weekly discussion section

Pages from previous years

2014 is the 17th time Bioinformatics has been taught at Yale. Pages for the 16 previous iterations of the class are available. Look at how things evolve!

2012 fall, 2012 spring (quizzes), 2011, 2010, 2009 and earlier (12 years of classes, staring in '98)

(Pointers on finding things on old class pages)

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