Post-doc Positions in Computational Biology at University of Arizona
The laboratory of Dr. Xiangfeng Wang invites applications for post-doc positions in School of Plant Sciences at University of Arizona. The research interests in the Lab focus on developing computational models and bioinformatic tools to interpret and integrate various epigenome and transcriptome next-generation sequencing data in plants, mainly in maize and rice. The successful candidate will be expected to utilize bioinformatic tools and develop original algorithms for a better understanding of the epigenetic mechanisms underlying the plant seed and endosperm development. The recruited postdocs will participate in two ongoing projects:
We are currently using Illumina RNA-seq to profile the transcriptome in maize seeds. The goal is to identify the important transcription factors controlling the endosperm development. The significance of this project is to fundamentally improve the seed yield, human nutrition, biomass and bioenergy production. From the methodological perspective, the candidate will design algorithms to module the regulatory network by integrating the gene expression data and epigenomic data. We will use nucleosome dynamics to improve the discovery of novel TF and regulatory motifs.
We are also producing ChIP-Seq data for a combination of activating and silencing epigenetic marks in maize endosperm development. In addition to the biological aim of understanding the epigenetic mechanism underlying the endosperm development, we are also interested on developing new computational approaches to: 1) improve gene prediction using activating epigenetic marks; 2) genome-wide search for the epigenetically imprinted genes by an innovative strategy. The original algorithms will be finally implemented as bioinformatic tools and database resources for the gene imprinting research in other plant organisms. For details, please check “Research” in Wang Lab website.
Have experiences in analyzing microarray, ChIP-Chip, ChIP-Seq and RNA-Seq data or relevant computational genomics experiences.