Extracting Molecular Diversity Between Populations Through Sequence Alignments

The use of sequence alignments for establishing protein homology relationships has an extensive tradition in the field of bioinformatics, and there is an increasing desire for more statistical methods in the data analysis. We present statistical methods and algorithms that are useful when the protein alignments can be divided into two populations based on known features or traits. The algorithms are considered valuable for discovering differences between populations at a molecular level. The approach is illustrated with examples from real biological data sets, and we present experimental results in applying our work on bacterial populations of Vibrio, where the populations are defined by optimal growth temperature, Topt.