September – 2008

The initial sequencing and analysis of the human genome, completed in 2003, was a major biological breakthrough, leading to a better understanding of the evolution and function of many human genes. The key to translating this newly acquired knowledge into medical advances relies on the availability of the genomes of many individuals, and in the study of correlation between genomes and diseases. Because the initial human genome was sequenced over 8 years and at the cost of $3 billion, another technological leap was necessary in order to allow for the economical sequencing of the genomes of many humans. Today this leap has been accomplished: Next-Generation Sequencing (NGS) technologies are able to sequence a human genome in a few weeks, at a cost of $10,000 to $100,000. Using these technologies, scientists are hoping to sequence thousands of human genomes in the next few years, and eventually allow each individual to know his or her personal genome.

Some of the biggest remaining challenges on the path to the personal genome are algorithmic. The NGS technologies are only able to read many small fragments of a genomic sequence, and reconstructing the source genome from these fragments, as well as the analysis of the differences between the sets of fragments from various individuals are difficult computational problems. Furthermore, the challenges of using the NGS datasets are exacerbated by the errors and biases in the underlying sequencing technologies. In this talk I will give an overview of genome sequencing and NGS technologies, and discuss some of the computational methods used to address the challenges posed by NGS datasets.