Cbb752b14
From GersteinInfo
Bioinformatics: Practical Application of Data Mining & Simulation
17th iteration at Yale, with material from all previous years available! (GersteinLab.org/courses/452)
News
Discussion sections start next week (week of 27 Jan)! One discussion section will be Wed 29 Jan, from 3-4pm. See readings. Please write a 1-2 paragraph summary of each paper, to be turned in before section. Please write to Michael at michael.rutenbergschoenberg (at) yale.edu if you want to present one of the first two papers.
The other discussion section is still unscheduled due to conflicts between the schedules of students who cannot come to the Wednesday section. If you cannot come to the Wednesday section, email Cong at cong.li (at) yale.edu.
If you are still not receiving class emails, please contact Michael at michael.rutenbergschoenberg (at) yale.edu.
Schedule
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 Bioinformatics:Mining&Simulatn 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
MB&B452/MCDB452
This version of the course consists of lectures, written problem sets, and a final (semi-computational section and a literature survey) project.
MB&B752/MCDB752
This version of the course consists of lectures, written problem sets, and a final (semi-computational section and a literature survey) project.
CB&B752/CPSC752
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.
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 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:
Section 1 (Michael): Wednesdays 3-4pm, starting 29 Jan 2013. Section 2 (Cong): TBD, starting week of 27 Jan 2013. Email Cong at cong.li (at) yale.edu if you want to attend this section to help him with scheduling.
Each section will include discussion of papers assigned (below). Students are expected to submit 1-2 paragraph summaries of each paper before the section. In Section 1 (Wed 3-4pm), students will give 15-20 min presentations of the papers. Section 2 will be smaller and will have group discussions of the papers.
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.
Instructors
Consultation is available UPON REQUEST or according to times stipulated by the individual instructors. Email cbb752(at)gersteinlab.org to reach the instructor and the TFs .
Instructor-in-Charge
| Name | Office | |
|---|---|---|
| Mark Gerstein | Bass 432A | mark.gerstein *at* yale.edu |
Guest Instructors
| Name | Office | |
|---|---|---|
| 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 | |
|---|---|---|
| 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: 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
Additional readings suggested by Professor Reinhart
Session 3: 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 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:
http://yalecollege.yale.edu/content/reading-period-and-final-examination-period
http://yalecollege.yale.edu/content/completion-course-work
Brief presentation on how to cite correctly : http://archive.gersteinlab.org/mark/out/log/2012/06.12/cbb752b12/cbb752_cite.ppt
Plagiarism
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.
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
Polls
Poll for students' sign up and good times for the weekly discussion section
Poll 2 Section sign-up
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)
