An overview of bioinformatics is presented with an emphasis on describing a set of tools, databases, and ontologies useful to the weed science community. These tools can be used to identify genes whose product may be the target site of a herbicide or in the degradation pathway of a xenobiotic. They may identify receptors responsible for pathogen recognition or enzymes in the metabolic pathway for allelopathic compounds. Whatever the gene of interest, bioinformatics allows researchers to assemble complete or partial genes from expressed sequence tags (ESTs), complete cDNAs, or complete genomes and then translate them into their corresponding amino acid sequences. Similarity searches can be used to find other proteins with homology to the gene of interest, which can provide clues to its function from the annotation of these database hits. The use of protein domain databases can also provide insight into the functional capabilities of the protein in question and delineate those portions essential for activity. Enzyme Commission (EZ) numbers or gene ontology (GO) descriptors allow placement of the protein within the larger network context of a biological system. These capabilities allow the scientist to probe deeper into the function of their protein of interest to gain a novel understanding of the biology.

This article analyses temperature data for Seoul based on a well defined daily average temperature (DAT) derived from records dating from 1954 to 2009, and considers related weather derivatives using a previous methodology. The temperature data exhibit some quite distinctive features, compared to other cities that have been considered before. Thus Seoul has: (i) four clear seasons; (ii) a significant seasonal range, with high temperature and humidity in the summer but low temperature and very dry weather in winter; and (iii) cycles of three cold days and four warmer days in winter. Due to these characteristics, seasonal variance and oscillation in Seoul is more apparent in winter and less evident in summer than in the other cities. We construct a deterministic model for the average temperature and then simulate future weather patterns, before pricing various weather derivative options and calculating the market price of risk (MPR).