Steven Salzberg, Johns Hopkins University, 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 find 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 basic 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.
Location: Carl Icahn Lab 101
Date/Time: 02/03/14 at 4:15 pm - 02/03/14 at 5:15 pm
Category: Quantitative & Computational Biology
Department: Lewis-Sigler Institute