Current Research

Pavel Pevzner's research focuses on combinatorial algorithms in computational molecular biology. His interdisciplinary HHMI project involves three programs: an introductory bioinformatics course suitable for all biology students; a research course in bioinformatics in which undergraduates, graduate students, and faculty will collaborate on the same research project; and a residential summer program for gifted high school students.

Our goal is to introduce biologists to computational foundations of modern bioinformatics. This interdisciplinary educational challenge will require forming a team of faculty members from different departments and developing a new bioinformatics course that, unlike existing courses, requires no background in either algorithms or programming.

Our project involves two programs. First, we will design an introductory bioinformatics course for undergraduates that requires no prerequisites and is suitable for all biology students. Second, we will develop a new course in collaborative research training in bioinformatics that brings together undergraduates, graduates, postdocs, and faculty working on the same research project.

The goals of this course are to teach undergraduates how to work as a team and how to write a research paper; to prepare undergraduate students and postdocs for roles as teachers; and to encourage interaction between bioinformatics students with different fields of study, for example, biology, physical sciences, engineering, and computational science and math. Undergraduates recruited to the program will also spend summers as research interns, rotating through participating labs. Our team will disseminate all course materials via a bioinformatics education website that will contribute to building a community of professors teaching bioinformatics courses and undergraduates involved in bioinformatics research.

Research in the Pevzner Lab

Our research focuses on combinatorial algorithms in computational molecular biology, including studies of genome rearrangements, protein identification by mass spectrometry, and DNA assembly. We developed the first efficient algorithm for studying genome rearrangements and released the first Web server for genome rearrangement analysis.

Our recent analysis of genome rearrangements in human and mouse genomes led to the rebuttal of the classical 30-year-old “random breakage” theory of chromosome evolution and the formulation of the new “fragile breakage” model of chromosome evolution. Other areas of research involve de novo peptide sequencing via mass spectrometry and “blind” identification of posttranslationally modified proteins. Recently, we proposed the first approach to the shotgun sequencing of entire proteins as opposed to the peptide sequencing that dominates mass spectrometry approaches today. In addition, we are developing algorithms for genome assembly and comparative analysis of multiple genomes.

As of May 2014

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