This is joint work with Ken Simpson, Mark Robinson, and Terry Speed.
This is joint work with Alfred Spormann and Joey McMurdie from Chemical Engineering at Stanford.
Two types of in-vitro experiments designed to determine the affinities of a transcription factor to DNA sequences are considered. The first experiment is the systematic evolution of ligands by exponential enrichment (SELEX) experiment. In this experiment, one begins with a large random pool of DNA sequences and, after many rounds, selects for the highest affinity sequence. The second experiment is a multiplex assay experiment. This experiment is done on a select group of DNA sequences and provides much more precise measurements of affinities. The data presented are for Bicoid, a transcription factor in Drosophila melanogaster.
In this talk, I will begin with a simple biochemical explanation of both experiments. I will then discuss our analysis of the data thus far, stressing the statistical methodology, and also addressing issues in the design of these experiments. As this work is ongoing, I will finish by mentioning current and future research regarding these experiments.
This is joint work with Peter Bickel's group and Mark Biggin's lab.
This is joint work with Pratyaksha Wirapati and Mauro Delorenzi of the Swiss Institute of Bioinformatics.
Firstly, I will present a fast method that allows for quick estimation of relative evolutionary rates of proteins, an important component in accurate phylogenetic estimation. The DistR approach to estimate gene/protein evolutionary rates based is on pairwise distances between pairs of taxa derived from gene/protein sequence data is presented. Simulation studies indicate that this algorithm accurately estimates rates and is robust to missing data. Secondly, I will discuss two different approaches to incorporating gene rates into phylogenetic inference: i) Allowing each gene/protein to have a single gene-wide rate of evolution; ii) integrating out over the possible rates of evolution of a gene using the Gamma distribution. Finally, time-permitting I will present current work on using a mixture model to account for the rates of evolution of sites in multi-gene data sets.
With a putative gene-interaction network, the problem of producing viable models of the cell remains. While systems biology approaches that attempt to develop quantitative, predictive models of cellular processes have received great attention, it is surprising to note that the starting point for all cellular gene expression, the transcription of RNA, has not been described and measured in a population of living cells. To address this problem, we propose a simple model for transcript levels based on Poisson statistics and provide supporting experimental evidence for genes known to be expressed at high, moderate, and low levels. Not only do these data confirm our model, but this general strategy opens up a potential new approach, Mesoscopic Biology, that can be used to assess the natural variability of processes occurring at the cellular level in biological systems.
The field of biodefense exists in an interesting and turbulent intersection of technology, politics, mission-space, economics, and ethics. Far from being isolated from the chaos, bioinformatics all too frequently finds itself right in the middle of the controversy. This talk will draw upon LLNL's experiences over the past 8 years to present one viewpoint of some of the key challenges facing bioinformatics in the biodefense field. From inadequate algorithms and inappropriate computer architectures, to impotent bureaucracy and pork-barrel politics, to the inability to get genomic data from countries with dangerous epidemics (or from colleagues at federal agencies in the US), to the ethical problems raised by trying to defend against malicious genetic engineering; all of these impact researchers working in bioinformatics applied to biodefense. Efforts underway at LLNL to deal with many of these challenges will be discussed.
A Bayesian approach can be used to calculate a probability of assignment to each taxonomic unit represented in a sequence database. The probability of assignment to each taxa serves as a measure of confidence in the assignment. In this talk I will introduce the assignment problem and a tool that tool that implements the Bayesian approach. At the end I may have time to touch on other current research.
This is joint work with Wouter Boomsma, John Huelsenbeck and Rasmus Nielsen.