TGA2 dual activity: N-terminus regulation of reversible DNA binding influenced by NPRI and CK2

TGA2 is a dual-function Systemic Acquired Resistance (SAR) transcription factor involved in the activation and repression of pathogenesis-related (PR) genes. Recent studies have shown that TGA2 is able to switch from a basal repressor to activator, likely, through regulatory control from its N-terminus. The N-terminus has also been shown to affect DNA binding of the TGA2 bZIP domain when phosphorylated by Casein Kinase II (CK2). The mechanisms involved for directing a switch from basal repressor to activator, and the role of kinase activity, have not previously been looked at in detail. This study provides evidence for the involvement of a CK2-like kinase in the switch of TGA2 activity from repressor to activator, by regulating the DNA-binding activity of TGA2 by phosphorylating residues in the N terminus of the protein.

Improving Short DNA Sequence Alignment with Parallel Computing

Variations in different types of genomes have been found to be responsible for a large degree of physical diversity such as appearance and susceptibility to disease. Identification of genomic variations is difficult and can be facilitated through computational analysis of DNA sequences. Newly available technologies are able to sequence billions of DNA base pairs relatively quickly. These sequences can be used to identify variations within their specific genome but must be mapped to a reference sequence first. In order to align these sequences to a reference sequence, we require mapping algorithms that make use of approximate string matching and string indexing methods. To date, few mapping algorithms have been tailored to handle the massive amounts of output generated by newly available sequencing technologies. In otrder to handle this large amount of data, we modified the popular mapping software BWA to run in parallel using OpenMPI. Parallel BWA matches the efficiency of multithreaded BWA functions while providing efficient parallelism for BWA functions that do not currently support multithreading. Parallel BWA shows significant wall time speedup in comparison to multithreaded BWA on high-performance computing clusters, and will thus facilitate the analysis of genome sequencing data.