Office: Clark Center S266
Office hours: Tue 1:15pm-3:15pm
Phone: (650) 723-3334
Email: ude.drofnats@mifares (written backwards to avoid spam)
Office: Clark Center S260
Office hours: General questions: Tue 1:15pm-3:15pm. Paper discussion: By appointment.
Phone: (650) 725-6094
Email: ude.drofnats@sonemisa (written backwards to avoid spam)
CS161: Design and Analysis of Algorithms, or equivalent familiarity with algorithmic and data structure concepts.
CS262: Computational Genomics, or CS274: Representations and Algorithms for Computational Molecular Biology, or BIOCHEM218: Computational Molecular Biology, or equivalent familiarity with computational biology concepts, problems and algorithms.
Presentation. The main course requirement is to select a topic and give a presentation based on two papers on the topic. The instructor and TA will meet with each student to help with the preparation, and ensure that the resulting presentation will be interesting and accessible to students in the class who are not experts in the given topic. Most of the topics have a strong algorithmic flavor, but some topics are more geared towards biology. To sign up for a presentation, you need to pick a topic and a date. Consult the topic list and the schedule, and then email both the instructor and the TA with subject "CS374, signing up for presentation", listing your choices in order of preference. Requests are handled on a first-come first-serve basis, and therefore you are encouraged to provide us with more than one choice.
Scribing. The second requirement is scribing a lecture. Lecture notes should provide students who are taking the class a useful resource for remembering the material presented. Ideally, lecture notes should be written up in a way so that they are readable by students of next year who did not necessarily read the papers that were presented. To sign up for scribing a lecture, consult the schedule and then email both the instructor and the TA with subject "CS374, signing up for scribing", listing your choices in order of preference. Once again, requests are handled on a first-come first-serve basis. Refer to the sample entry for an example of how lecture notes should look like in terms of format and organization. We suggest preparing your notes in Microsoft Word using the aforementioned sample as a template. Your notes are due one week after the lecture, and they should be submitted by emailing a PDF file to the TA; you can also submit any other popular format (Microsoft Word, Postscript) and in this case it will be converted to PDF by the TA. Keep in mind that your notes constitute an original text; verbatim copying from any sources is not allowed.
Summary. As a third requirement, you need to select two lectures: one of lectures 3 to 14, and one of lectures 15 to 26. For each lecture, you have to find one paper in addition to the two presented; the paper must be related to the topic and relatively recent (after 2001). Then, you must write a single-page summary of what the paper presents and how it relates to the other two. Refer to the sample entry for examples of how the summary should look like in terms of format and structure. Each of your two summaries is due one week after its respective lecture, but it may appear online later, as you may be asked to perform some editing (if necessary).
| Topic description | ||
| Papers | Assigned to | |
| Large Scale Genome Properties | ||
| 1 | Genomic Rearrangements | Nandhini Nandiwada Santhanam |
| 2 | Repetitive DNA Detection and Classification | Vijay Krishnan |
| Searching Biological Sequence Databases | ||
| 3 | Index-based search of single sequences | Omkar Mate |
| 4 | Multiple indexes and multiple alignments | Siddharth Jonathan |
| Sequence Alignment | ||
| 5 | Multiple Sequence Alignment | Sarah Aerni |
| 6 | Inverse Alignment | Bahman Bahmani |
| Regulatory motif finding | ||
| 7 | Ab initio motif finding | Ryo Shimizu |
| 8 | Comparative motif finding | Mayukh Bhaowal |
| RNA Structure | ||
| 9 | RNA Secondary Structure Prediction | Greg Goldgof |
| 10 | RNA regulation | Marc Schaub |
| 11 | RNA finding | Leticia Britos |
| Phylogenetic Trees | ||
| 12 | Inference of phylogenetic trees | |
| 13 | Gene trees | Abhita Chugh |
| Protein Structure | ||
| 14 | Evolution of Multidomain Proteins | Wissam Kazan |
| 15 | Stochastic roadmap simulations of protein kinetics | |
| 16 | Protein Folding Dynamics | |
| 17 | Protein Structure Alignment | Ramji Srinivasan |
| 18 | Machine Learning for Protein Classification | Ashutosh Saxena |
| Networks of Protein Interactions | ||
| 19 | Construction of Networks from Diverse Data Sources | Neda Nategh |
| 20 | Comparison of Networks Across Species | Chuan Sheng Foo |
| 21 | Properties of Interaction Networks | Susan Tang |
| Human Population Genetics | ||
| 22 | Human Migrations
| Anjalee Sujanani |
| 23 | Human Evolution | Sharareh Noorbaloochi |
| 24 | Human-Chimp Speciation | Frank Chan |
| Computation using DNA and cells | ||
| 25 | Robust Self-Assembly of DNA | Eduardo Abeliuk Acuna |
| 26 | Transforming Cells into Automata | Ravi Tiruvury |
| Biological Data mining | ||
| 27 | Rashmi Raj | |
