Abstract
Background Piwi-interacting RNAs (piRNAs) are sRNAs that have a distinct biogenesis and molecular function from siRNAs and miRNAs. The piRNA pathway is well-conserved and shown to play an important role in the regulatory capacity of germline cells in Metazoans. Significant subsets of piRNAs are generated from discrete genomic loci referred to as piRNA clusters. Given that the contents of piRNA clusters dictate the target specificity of primary piRNAs, and therefore the generation of secondary piRNAs, they are of great significance when considering transcriptional and post-transcriptional regulation on a genomic scale. A quantitative comparison of top piRNA cluster composition can provide further insight into piRNA cluster biogenesis and function.
Results We have developed software for general use, piClusterBusteR, which performs nested annotation of piRNA cluster contents to ensure high-quality characterization, provides a quantitative representation of piRNA cluster composition by feature, and makes available annotated and unannotated piRNA cluster sequences that can be utilized for downstream analysis. The data necessary to run piClusterBusteR and the skills necessary to execute this software on any species of interest are not overly burdensome for biological researchers.
piClusterBusteR has been utilized to compare the composition of top piRNA generating loci amongst 13 Metazoan species. Characterization and quantification of cluster composition allows for comparison within piRNA clusters of the same species and between piRNA clusters of different species.
Conclusions We have developed a tool that accurately, automatically, and efficiently describes the contents of piRNA clusters in any biological system that utilizes the piRNA pathway. The results from piClusterBusteR have provided an in-depth description and comparison of the architecture of top piRNA clusters within and between 13 species, as well as a description of annotated and unannotated sequences from top piRNA cluster loci in these Metazoans.
piClusterBusteR is available for download on GitHub: https://github.com/pschreiner/piClusterBuster
