From GersteinInfo

Latest revision as of 00:32, 23 April 2014

Bioinformatics: Practical Application of Data Mining & Simulation

17th iteration at Yale, with material from all previous years available! (GersteinLab.org/courses/452)

News

In class poll on 3 March: which of these lectures did you like most: [INTRODUCTION] [ALIGNMENT] [UNSUPERVISED MINING] [SUPERVISED MINING] [NETWORK TOPOLOGY] [FUNSEQ APPLICATION] [NETWORK PREDICTION]

Quiz 2 is on Wednesday, 26 Feb, and will cover all of the material up through Monday, 24 Feb.

Quiz 1 is on Wednesday, 12 Feb, and will cover all of the material up through slide 31 of lecture 7 (3 Feb).

Discussion sections start this week (week of 27 Jan)! Both sections will be held in Bass 405 (directly above our lecture classroom). One will be Wed 2:30-3:30 pm, and the other will be Fri from 4:30-5:30pm. See readings. Please write a 1-2 paragraph summary of each paper, to be turned in before section.

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 2:30-3:30pm, starting 29 Jan 2013. Section 2 (Cong): Fridays 4:30-5:30pm, starting week of 31 Jan 2013.

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

Guest Instructors

Teaching Fellows

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. The second section will likely be much smaller, and will have a discussion format. The written assignment will be the same, and students will be graded on a combination of the written assignments and your participation in discussions.

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/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: Bioinformatics for Next-Gen Sequencing 2

Lior Pachter. Models for Transcript Quantifications from RNA-Seq (2011) ArXiV 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

Assignment 1 DUE: 3 March 2014
Files for programming assignment: Download here
Test input and output files: Download here

Assignment 2 ' DUE: 7 April 2014

Assignment 3 (updated with reference containing values for model constants) DUE: 24 April 2014

supplementary file for programming assignment

Assignment 3 (non-programming)

Final Project

Final Project "Final version" - updated 4 April 2014 (filepaths of project materials corrected) DUE: 30 Apr 2014 11.59pm

Files for MBB/MCDB pseudocomputational section

Accessing and Using BulldogJ

Grade Categories

The following are the approximate grading systems:

CBB and CPSC Sections:

MBB and MCDB Sections:

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)

(Pointers on finding things on old class pages)