The objectives of this study were to identify the population structure and to assess the genetic diversity of maize inbreds. We genotyped 81 microsatellite loci of 90 maize inbreds that were derived from tropical hybrids and populations. The population structure analysis was based on a Bayesian approach. Each subpopulation was characterized for the effective number of alleles, gene diversity, and number of private alleles. We also performed an analysis of molecular variance and computed a measure of population differentiation (FST).
Maize landraces derived from tropical germplasm represent an important source of genetic variability, which is currently poorly understood and under-exploited by Brazilian crop breeding programs.
Jatropha curcas, internationally and locally known, respectively, as physic nut and pinhão manso, is a highly promising species for biodiesel production in Brazil and other countries in the tropics. It is rustic, grows in warm regions and is easily cultivated. These characteristics and high-quality oil yields from the seeds have made this plant a priority for biodiesel programs in Brazil. Consequently, this species merits genetic investigations aimed at improving yields. Some studies have detected genetic variability in accessions in Africa and Asia.
Following sequence alignment, clustering algorithms are among the most utilized techniques in gene expression data analysis. Clustering gene expression patterns allows researchers to determine which gene expression patterns are alike and most likely to participate in the same biological process being investigated. Gene expression data also allow the clustering of whole samples of data, which makes it possible to find which samples are similar and, consequently, which sampled biological conditions are alike.
SpotWhatR is a user-friendly microarray data analysis tool that runs under a widely and freely available R statistical language (http://www.r-project.org) for Windows and Linux operational systems. The aim of SpotWhatR is to help the researcher to analyze microarray data by providing basic tools for data visualization, normalization, determination of differentially expressed genes, summarization by Gene Ontology terms, and clustering analysis.
We show here an example of the application of a novel method, MUTIC (model utilization-based clustering), used for identifying complex interactions between genes or gene categories based on gene expression data. The method deals with binary categorical data which consist of a set of gene expression profiles divided into two biologically meaningful categories. It does not require data from multiple time points.