A computational perspective on the exploration and analysis of genomic and genome-scale information. Provides an integrated introduction to genome biology, algorithm design and analysis, and probabilistic and statistical modeling. Topics include genome sequencing, genome sequence assembly, local and global sequence alignment, sequence database search, gene and motif finding, phylogenetic tree building, and basic gene expression analysis. Methods include dynamic programming, indexing, hidden Markov models, and elementary supervised and unsupervised machine learning. Development of practical experience with handling, analyzing, and visualizing genomic data using the computer language Python.
The course will require students to program often in Python. Students coming in to the course must already know how to program in some computer language, but it need not be Python. If it is not Python, students will be expected to come quickly up to speed in Python on their own. Additionally, students should be comfortable with mathematical thinking and formulas, and should have had some exposure to basic probability as well as molecular or cellular biology; however, the course has no formal course prerequisites, and quick refreshers of relevant background will be provided. Please speak to the instructor if you are unsure about your background. This course is a valid elective in both biology and computer science.
Professor Alex Hartemink
| Vincentius Martin, TA |
Email: vincentius.martin at duke.edu |
| Sneha Mitra, TA |
Email: sneha.mitra at duke.edu |
| Trung Tran, TA |
Email: trung.tran at duke.edu |
| Katherine Molinet, UTA |
Email: katherine.molinet at duke.edu |
| Mihir Paithane, UTA |
Email: mihir.paithane at duke.edu |
| Dylan Powers, UTA |
Email: dylan.powers at duke.edu |
| Aditya Sridhar, UTA |
Email: aditya.sridhar at duke.edu |
| Vivek Sriram, UTA |
Email: vivek.sriram at duke.edu |
| Anngelyque Stevenson, UTA |
Email: anngelyque.stevenson at duke.edu |
| Darryl Yan, UTA |
Email: darryl.yan at duke.edu |
| Annie Yin, UTA |
Email: wen.yin at duke.edu |
Office hours with TAs and UTAs will be held in 154 Biological Sciences.
If these office hours do not work for you, please post questions via Piazza, or send any of us an email to schedule an alternate time. In particular, if you would like to speak with the instructor about anything, he is available immediately after every lecture, or you are welcome to send him an email to schedule a meeting at another time that is more convenient for you.
The class meets on Tuesdays and Thursdays 10:05–11:20AM in 111 Biological Sciences.
Note: The course schedule may change subtly from time to time. Always check the web page for the most up-to-date schedule.
| Session | Date | Instructor | Topic | Assignment (out/due on Fridays) |
|---|---|---|---|---|
| 1 | Tue 28 Aug | AH | Course introduction; Viral genome introduction | |
| 2 | Thu 30 Aug | AH | Molecular biology primer: DNA, RNA, and protein | PS1 out |
| 3 | Tue 04 Sep | AH | Gene/genome organization; Viral genome revisited | |
| 4 | Thu 06 Sep | AH | Algorithm introduction; Time and space resources | |
| 5 | Tue 11 Sep | AH | Analyzing algorithms; Designing efficient algorithms | |
| 6 | Thu 13 Sep | Class cancelled: Hurricane Florence | PS1 due; PS2 out | |
| 7 | Tue 18 Sep | AH | Divide-and-conquer introduction and exploration | |
| 8 | Thu 20 Sep | AH | Memoization; Dynamic programming | |
| 9 | Tue 25 Sep | AH | Heuristics and greedy algorithms; Genome assembly | |
| 10 | Thu 27 Sep | AH | DNA sequencing; Genome assembly; HGP and Celera | PS2 due; PS3 out |
| 11 | Tue 02 Oct | AH | Short-read mapping; Suffix arrays; BWT | |
| 12 | Thu 04 Oct | AH | FM-index, in all its glory | |
| Tue 09 Oct | FALL BREAK — enjoy! | |||
| 13 | Thu 11 Oct | AH | Sequence variation and alignment; Global alignment | PS3 due; PS4 out |
| 14 | Tue 16 Oct | AH | Traceback; Aligning sequences with affine gap scores | |
| 15 | Thu 18 Oct | AH | Affine gap alignment traceback; Local alignment | |
| 16 | Tue 23 Oct | AH | Local alignment traceback; FASTA and BLAST heuristics | |
| 17 | Thu 25 Oct | AH | Phylogenetic trees; Time and distance | PS4 due; PS5 out |
| 18 | Tue 30 Oct | AH | Building phylogenetic trees (UPGMA and NJ) | |
| 19 | Thu 01 Nov | AH | Probability; Discrete and continuous random variables | |
| 20 | Tue 06 Nov | AH | Infinity; Joint, marginal, and conditional | |
| 21 | Thu 08 Nov | AH | Joint, marginal, and conditional; Bayes rule; Models | PS5 due; PS6 out |
| 22 | Tue 13 Nov | AH | Parameter estimation: ML, MAP, PME | |
| 23 | Thu 15 Nov | AH | Factoring; Graphical models; Markov models; HMM intro | |
| 24 | Tue 20 Nov | AH | Hidden Markov models; Viterbi decoding | |
| Thu 22 Nov | THANKSGIVING BREAK — give thanks! | PS6 due; PS7 out | ||
| 25 | Tue 27 Nov | AH | Viterbi traceback; Posterior decoding intro | |
| 26 | Thu 29 Nov | AH | Posterior decoding; Estimating HMM parameters; Baum-Welch | |
| 27 | Tue 04 Dec | AH | HMMs for finding spliced genes, and other applications | |
| 28 | Thu 06 Dec | AH | Course summary; Course evaluations | PS7 due |
AH: Alex Hartemink
An overview of the DFS and BFS algorithms for visiting the nodes of a graph.
A careful description of the algorithm for finding the closest pair of points in O(n log n) time. This is from the 2nd edition of "Introduction to Algorithms" (fondly known as CLRS).
If you'd like to learn a bit more about sorting—how different algorithms work and how they compare in practical terms for specific kinds of inputs—check out this cool demo site. Also, here are some fun videos: quickly visualizing the execution of 15 sorting algorithms and a dance version of bubble sort (if you can't get enough of that, there are many more).
The recurrence relations that arise in analyzing divide-and-conquer algorithms commonly take on a certain form in which the running time for a problem of size n can be expressed in terms of the running time of a copies of a problem that is b times smaller (i.e., size n/b), plus some extra work (which might depend on n). In such cases, a powerful master theorem can help you solve just such a recurrence.
Here is the SARS genome handout from class. Here is a text file containing the SARS genome (Tor2 isolate, in its RNA form). Have fun parsing it! You can also find it, and a lot more information about it, in GenBank: visit the Genbank entry and see what else you can learn.
If you want a little more clarity about how Python creates variables, populates them, and passes them around, or if you want to visualize your code in action for debugging purposes, check out Python Tutor. You can study their code examples as they execute, or paste in your own code.
Here are a few different kinds of resources for those with less biology background, ranging from the comprehensive to a basic overview:
The various books mentioned in class are summarized here; each is linked to Amazon where you can read more (these are not affiliate links). Note that none of these books is compulsory for the class, though you may benefit from one or more. As for the books on Python, many resources are now available free online, even complete textbooks downloadable as PDFs (you'll save trees (unless you print them)).
Let's try downloading and running a Python program in Eclipse to test that everything is set up correctly.
First, ensure that you have the right perspective in Eclipse. The Python perspective will give you a less cluttered set of windows with the smaller PyDev Package Explorer on the left and the main editor window on the right.
For each problem set, we will provide you with some code as a framework and possibly some data files, so you will need to know how install those files into Eclipse as a project. To practice, follow the directions below.
Now try modifying and running the program within Eclipse, and then test the submission process.
NOTE: simple Python documentation is available from within Eclipse—just hover over a Python keyword and a tooltip will pop up with a short description. Try this with the keywords "split", "append", "sort", and "sorted" within the sample program.
You can submit as many times as you like, and every file you submit will be stored on the server each time. Submitting partial work before the deadline can help us recover your files if anything happens to your computer, so it may be a good idea to upload your code and README files any time you've made some good progress on them. Even resubmitting a complete solution is fine: if you realize that you did something wrong at the last minute, you can simply resubmit a new version. As far as grading goes, we will only grade your last submission, so be sure that the final time you submit a project, you submit all the files necessary for grading, not just the ones you modified relative to a previous submission.
All students are expected to abide by generally accepted standards of academic integrity. This includes all the various aspects of Duke's Community Standard. In particular, be reminded that it is not acceptable to take the ideas/work of another and represent it as one's own, even if paraphrased. Ideas/work taken from others—including Internet sources, peers in the class, peers from outside the class—must always be appropriately cited.
Violations of academic integrity will be taken very seriously. At a minimum, assignments in which a student either receives inappropriate input from others or provides inappropriate input to others will be graded as 0. In addition, violations will be discussed with the Dean who directs the Student Conduct Office.
Unless expressly granted in the problem set, all problems should be completed individually; no collaboration is permitted. However, if you have worked for a while on a particular problem and have encountered a mental wall, and if you have banged your head against said wall for a while, we provide mechanisms where you can consult others to make progress, rather than giving up entirely. Your first course of action is to post a question on Piazza, or to speak to the instructor or TAs.
If for any reason you consult your peers outside of Piazza, it should remain understood that such an interaction must be one of consultation and not collaboration: hints to help overcome a small obstacle rather than answers—after consultation, it is expected that you should still have plenty of thinking to do. In addition, if you do happen to consult with another student, both of you must cite this.
Students generally have two weeks to work on problem sets—not because two weeks are generally required to finish, but 1) to allow students who start early sufficient time to reflect/ruminate on problems where an impasse has been reached (the thought process through which students go while solving a problem often includes some gestation period before things become clear) and 2) to provide flexibility as to when students complete their work while they juggle other requirements and commitments during the semester.
Given this latter point, students should not request extensions for turning in their work beyond the two weeks already allotted. However, this rule has two exceptions:
If you turn in work after the deadline but have already used your free extension, we will take off 10 points for every 12 hours late (rounded up). So if you are 0–12 hours late, that would be –10; if you are 12–24 hours late, that would be –20, etc. Also, because we send work out to be graded using automated scripts, work will not be graded if it is turned in after 5pm on Sunday (more than 48 hours late without an extension, or at all late with an extension).
We have designed problem sets in the class to permit you to explore the material, and to develop deeper understanding of the material through that exploration. I ask you to focus on the ideas and the learning rather than on the points and the credit; put another way, consider adopting a perspective of how you can work to satisfy yourself rather than work to satisfy me.
All that said, when it comes to grading, we still need to assign points and credit: that is unfortunately unavoidable. However, we have designed our approach to assigning credit in an attempt to be consistent with the perspective of the previous paragraph, and the approach is perhaps a little different from what you may be familiar with in other classes. Specifically, I have asked the TAs to frame their grading in terms of ‘positive earning’ rather than ‘negative error’.
What do I mean by this? Well, a ‘negative error’ approach is one in which one assumes one's work will earn 100 points unless there are mistakes present. Under such an approach, graders are negatively tasked with finding mistakes and errors, and taking away points for any they find.
I have inverted this by choosing to adopt a ‘positive earning’ approach in which an empty problem set earns 0 points, and students earn more points as they demonstrate deeper levels of mastery of the material and challenge. Under such an approach, graders are instead positively tasked with finding ways that students should earn credit for deeply engaging the material.
A corollary of the ‘negative error’ approach is that unless a student makes a mistake, they are entitled to earn the full 100 points. Conversely, a corollary of the ‘positive earning’ approach is that it is possible for a student to not make any mistakes yet still not earn the full 100 points. For example, this can happen if a student does the bare minimum to engage the material, and while not making any mistakes, never demonstrates mastery or depth of understanding. Our ‘positive earning’ approach not only focuses on the positive instead of the negative, but it also leaves room to grant more credit to students who engage the material more deeply.
I write all this because if you find that you did a problem without making a mistake, but got only +16 when some other student may have gotten +18, it doesn't necessarily mean that something is wrong (though it might be). It could mean that there were some interesting ways to engage the problem you didn't explore that the other student did. An analogy might be from a video game like Mario Brothers: you can successfully rescue the princess but still not end up with the highest score because someone can score higher if they take the time to explore a pipe that leads in a new direction. Analogously, earning the full 100 points usually requires more than just ‘no mistakes’; it also requires demonstration of mastery and engagement. We use rubrics to apply these judgments consistently across the class, and the rubrics are not pre-determined: our rubrics adapt to give credit for the new ways we see students engaging a problem.
Grades for all work will be recorded and made available to students online.
This term we will be using Piazza for course announcements, communication, and discussion. The Piazza system is highly catered to getting you help quickly and efficiently from classmates, the TAs, and the instructor. Rather than emailing questions to the teaching staff, please post your questions on Piazza so everyone can benefit from the responses.
You can find our class posting page at: https://piazza.com/duke/fall2018/compsci260/home.
This is an optional challenge for students interested in applying what we have learned in class to a real computational genomics research problem; practicing the skills of using Python or R (or any other tool you wish) to visualize, analyze, model, and interpret real genomic data; and exploring the science linking chromatin structure and transcriptional regulation. Since this problem represents an open challenge for the genomics community, you are free to choose the approaches you use to analyze the data, as well as the questions you explore. Creative projects are highly encouraged. You may work in small teams (2-3 is ideal). For all submissions we receive by the deadline of 15 Dec 2018, we will provide feedback, and will also designate a best project as well as a most creative project. There will be (simple) prizes!
In this data expedition challenge, we will explore next-generation sequencing reads from MNase-seq experiments in yeast. The data was generated to detect genome-wide binding locations of various kinds of DNA-binding proteins. The MNase-seq data sets were collected at Duke as part of our ongoing computational genomic research collaboration with the lab of Prof. David MacAlpine in the Department of Pharmacology and Cancer Biology.
DNA-binding proteins, including nucleosomes and transcription factors (TFs), play essential roles in gene regulation, and their locations along the genome help give us clues about how genes are regulated. Recently, a new MNase-seq protocol was developed by the MacAlpine group at Duke in conjunction with the Henikoff group at the University of Washington. [1] The basic idea is that genomic locations not bound by proteins are accessible to micrococcal nuclease (MNase) and are therefore more sensitive to MNase digestion. Conversely, genomic locations bound by proteins are less sensitive to MNase digestion.
Consequently, if we sequence the ends of the fragments that remain after MNase digestion, and map the paired sequencing reads that arise, we should be able to see where MNase was able to digest/cut the genome, revealing something about the binding locations of DNA-binding proteins along the genome. It is important to note that the genome of each individual cell in a population may be in a slightly different occupancy/protection state. We collect data from a population of cells so this experiment is sampling the different protection states present in the cell population.
Complicating the issue further, MNase is also known to have a nucleotide-specific bias as it digests DNA, meaning that it tends to cleave/digest certain sequences more than others. For example, it prefers to digest A/T nucleotides compared to G/C (its bias is actually a bit more subtle/complex than that, which is a nice model selection challenge you can explore: what is the simplest model that captures well this bias?). To give you further information about this sequence bias, we are also providing MNase digestion data of naked (deproteinized) DNA in vitro which will allow for the development of models to quantify such bias (because with this data, the variation in cutting that you see is only the result of the MNase interacting with the naked DNA and is not influenced by protein protection).
Usually, sequencing reads are stored in files of fastq format. In this case, we downloaded two large yeast MNase-seq read fastq files: in vivo yeast MNase-seq read files generated by Henikoff et al. [1], and in vitro yeast MNase-seq read files generated by Deniz et al. [2] for use in quantifying MNase digestion bias. Both files contain short sequencing reads, of length 25 and 54 base pairs, respectively. The total number of reads in each file is on the order of 100 million. For reference, the yeast genome contains 16 chromosomes whose total size is approximately 12.5 million base pairs.
To analyze those sequencing reads, you would typically first need to map the reads to a reference genome, using tools like BOWTIE. However, to simplify this challenge, we have already performed this mapping step for you. We are thus providing you one tab-delimited text file for each of the first 12 yeast chromosomes, named with ChrI to ChrXII (yeast geneticists like Roman numerals); we will reserve the remaining 4 yeast chromosomes to evaluate your submitted results. Each file contains the start and end genome coordinates of all the reads mapped to that chromosome, one read per line. You may notice that the distances between the start and end coordinates are larger than 25 or 54 base pairs. That is because the MNase-seq experiments produce paired-end reads and we are indicating the coordinates of the spanned fragment from which the two reads come. So the provided coordinates are the start coordinate of one read, along with the start coordinate of its mated read on the opposite strand; or, put another way, the first and last nucleotide of the fragment.
It is reasonable to think of the start and end coordinates as nucleotides just beyond which MNase cleaved the DNA, while the sequence between the start and end coordinates was not digested by MNase. We also provide the whole yeast genome sequence (sacCer2 2008 version, in separate fasta files) if you wish to extract the actual sequence around the cleavage sites based on the provided coordinates.
All data files for this challenge are available from:
You will need to do some independent exploration to figure out what to do next. You may want to read more about the MNase enzyme and how it works, or what is known about it. You probably want to get more info about the MNase-seq protocol, as described in the original paper. [1] Then you can start exploring one or multiple of the following, depending on what suits your fancy, or you may have other ideas of your own:
Good luck, and have fun on this expedition!