Motivated individuals may apply to conduct postdoctoral research with us in these or related areas. Send a complete application (with addresses of references) by e-mail to arend [at] stanford [dot] edu.

Ultra-high throughput sequencing (UHTS) technologies are revolutionizing functional genomics and population genetics. We generate data with both Solexa and SOLiD.

For an example of what we do with UHTS data see our QuEST web page.

Our goal is to construct genome regulatory networks of mammalian development, and understand how regulatory variation (in regulatory regions or in proteins) perturbs or extends such networks. This is a brand new project that was inspired by our work on Ciona gene regulation and population variation. We are using the mouse due to its significant advantages as an experimental model system. These studies will be informed by our evolutionary constraint analyses on genomes and proteomes.

Evolutionary Constraint in Genomes

Constraint-based identification of functional elements, many of which are likely involved in gene regulation, is a major goal of genome projects. In close collaboration with Serafim Batzoglou’s group, we have been developing methodology to make the most of comparative analyses of animal genomes. As part of this collaboration, we are currently building a new whole-genome aligner that will generate multiple alignments of mammalian genomes with high specificity and sensitivity. GERP, our method for alignment-based constraint detection, just received a major update to a new version (see downloads link on left).

Evolutionary Constraint in Proteins

We are building a high-quality and intuitive web resource to put alignments, trees, and analyses of constraint in proteins at the fingertips of the researcher. ProPhylER's focus is on providing analyses of constraint and predictions of deleteriousness using orthologs and closely related paralogs, distinguishing ProPhylER from superficially similar tree and evolution resources. Click the link on the left to learn more about ProPhylER.