Phillip Compeau

 

 


 

Phillip Compeau is a Ph.D. candidate and instructor in the department of mathematics at UC San Diego. Along with Nikolay Vyahhi, he co-founded Rosalind (http://rosalind.info), a site that teaches bioinformatics algorithms and tools through a series of programming challenges, and he serves as the site's content editor.

More info: http://www.linkedin.com/in/phillipcompeau




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Dec 5th 2016

After sequencing genomes, we would like to compare them. We will see that dynamic programming is a powerful algorithmic tool when we compare two genes (i.e., short sequences of DNA) or two proteins. When we "zoom out" to compare entire genomes, we will employ combinatorial algorithms.

Average: 5.4 (7 votes)
Dec 5th 2016

In this course, we will see how evolutionary trees resolve quandaries from finding the origin of a deadly virus to locating the birthplace of modern humans. We will then use methods from computational proteomics to test whether we can reconstruct Tyrannosaurus rex proteins and prove that birds evolved from dinosaurs.

Average: 6.8 (12 votes)
Dec 5th 2016

In this class, we will compare DNA from an individual against a reference human genome to find potentially disease-causing mutations. We will also learn how to identify the function of a protein even if it has been bombarded by so many mutations compared to similar proteins with known functions that it has become barely recognizable.

Average: 6.8 (5 votes)
Dec 5th 2016

Are you interested in learning how to program (in Python) within a scientific setting? This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python. Each of the four weeks in the course will consist of two required components. First, an interactive textbook provides Python programming challenges that arise from real biological problems.

Average: 7 (1 vote)
Dec 5th 2016

This course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat.

Average: 8 (8 votes)
Dec 5th 2016

Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium.

Average: 10 (1 vote)
May 23rd 2016

How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from the general problem of dividing data points into distinct clusters.

Average: 7.6 (5 votes)
Jan 25th 2016

This course will cover algorithms for solving various biological problems along with a handful of programming challenges testing your ability to implement these algorithms. It offers a gentler-paced alternative to the instructors' two other courses, Bioinformatics Algorithms (Part 1 and Part 2).

Average: 7 (4 votes)
Mar 16th 2015

This is the second course in a two-part series on bioinformatics algorithms, covering the following topics: evolutionary tree reconstruction, applications of combinatorial pattern matching for read mapping, gene regulatory analysis, protein classification, computational proteomics, and computational aspects of human genetics.

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