Pavel Pevzner




Pavel Pevzner is Professor of Computer Science and Engineering at University of California San Diego (UCSD), where he holds the Ronald R. Taylor Chair and has taught a Bioinformatics Algorithms course for the last 12 years. His research concerns the creation of bioinformatics algorithms for analyzing genome rearrangements, DNA sequencing, and computational proteomics.

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Aug 30th 2017

The course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second).

Average: 7.6 (14 votes)
Aug 28th 2017

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: 7.7 (9 votes)
Aug 28th 2017

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: 8.5 (4 votes)

Aug 28th 2017

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)
Aug 28th 2017

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)
Aug 28th 2017

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)

Aug 28th 2017

A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments.

Average: 8.1 (10 votes)
Aug 28th 2017

World and internet is full of textual information. We search for information using textual queries, we read websites, books, e-mails. All those are strings from the point of view of computer science. To make sense of all that information and make search efficient, search engines use many string algorithms. Moreover, the emerging field of personalized medicine uses many search algorithms to find disease-causing mutations in the human genome.

Average: 7.1 (13 votes)
Aug 28th 2017

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: 6.5 (2 votes)
Aug 21st 2017

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: 7.8 (13 votes)

Explore the power of computing in modern biology and apply existing software tools to find recurring biological motifs within genes that are responsible for helping Mycobacterium tuberculosis go "dormant" within a host for many years before causing an active infection.

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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)