Steven Salzberg (Johns Hopkins University, School of Medicine)

The event will start on: Tue, Apr 15, 2014 | 4:00 pm
Location: Lewis Thomas Lab, 003 | Washington Road

steven salzbergSteven Salzberg
Johns Hopkins University School of Medicine

Steven Salzberg is Professor of Medicine, Biostatistics, and Computer Science at the McKusick-Nathans Institute of Genetic Medicine at Johns Hopkins School of Medicine, where he is also Director of the Center for Computational Biology. His group's research focuses on the development of new computational methods for analysis of DNA from the latest sequencing technologies. Over the years they have developed and applied software to many problems in gene finding, genome assembly, comparative genomics, evolutionary genomics, and sequencing technology itself.

Professor Salzberg's current work emphasizes analysis of DNA and RNA sequenced with next-generation technology. His blogs and other writing cover topics on the impact of science on society including the effects of pseudoscience, the problems of alternative medicine, the anti-vaccination movement, gene patents, and the influence of sports on higher education. See the links on his lab home page for his scientific publications, his opinion pieces, and other news.

Seminar Topic

Computational Challenges of High Throughput Genome Sequence Analysis

Next-generation sequencing technology allows us to peer inside the cell in exquisite detail, revealing new insights into biology, evolution, and disease that would have been impossible to discover just a few years ago. The enormous volumes of data produced by NGS experiments present many computational challenges that we are working to address. In this talk, I will discuss some of our algorithmic solutions to two key alignment problems: (1) mapping sequences onto the human genome at very high speed, and (2) mapping and assembling transcripts from RNA-seq experiments. I will also discuss some of the problems that can arise during analysis of exome data, in which the gene-containing portions of the genome are sequenced in an effort to identify mutations responsible for disease. My group has developed algorithms to solve each of these problems, including the widely-used Bowtie program for fast DNA sequence alignment, the TopHat and Cufflinks programs for assembly of genes from transcriptome sequencing (RNA-seq) experiments, and the new DIAMUND program for detecting de novo mutations. This talk describes joint work with current and former lab members including Ben Langmead, Cole Trapnell, Daehwan Kim, Mihaela Pertea, and Geo Pertea.

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