title: Analysis of poly(A) site choice using a Java-based clustering algorithm.
abstract: Modern high-throughput DNA sequencing has the potential to generate large volumes of data for analysis by investigators-including poly(A) site data. Here I describe a computational method to compare poly(A) site choice differences between two large data sets based on the relative abundance and position of tags within each reference sequence to which they are aligned. This method provides rapid quantification and visualization of differences and similarities in poly(A) site choice between the two datasets.
authors.affiliation: Department of Plant and Soil Sciences, University of Kentucky, Lexington, KY, 40506, USA, patrick.thomas@simpson.kyschools.us.
authors.forename: Patrick E
authors.initials: PE
authors.lastname: Thomas
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D066264:Datasets as Topic; D011061:Poly A / Q000235:genetics*; D017423:Sequence Analysis, RNA; D012984:Software
chemical_list: D011061:Poly A