Daniel H. Huson studied mathematics at Bielefeld University. He was a post-doc in Computer Science at the University of Pennsylvania and in Applied Math at Princeton University 1997-99. He then worked as a senior staff scientist at Celera Genomics in Gene Myers' group. Since 2002 he is Professor of Algorithms in Bioinformatics at the University of Tuebingen. In addition, he is currently a Visiting Professor at the National University of Singapore. He works on algorithms and software for phylogenetics, genomics and metagenomics.
The computational analysis of microbiome sequencing data
Environmental shotgun sequencing allows researchers to study microbes as they co-exist in the environment rather than only in pure culture. This is a very active area of research, for example, there is much interest in exploring the role of the human microbiome in the context of diseases. Typical projects involve tens or hundreds of samples, involving billions of DNA reads. Basic computational challenges are: how to efficiently align such datasets against DNA or protein reference databases. How to analyze the resulting alignments so as to identify the organisms and genes present in a sample, and how to correlate the changes seen across multiple samples with external parameters such as disease state. How to provide easy interactive access to such datasets. Our work in this area focuses on developing and implementing algorithms and data structures that address such questions. We will report on a number of algorithms and tools such as DIAMOND, MEGAN and MeganServer. We will demonstrate the use of these tools by describing some of the biological and bio-medical projects that we are involved in.