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Schreiner, P. A.

Publications and source records attributed to Schreiner, P. A..

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TruePaiR: Software for the Accurate Identification of Complementary piRNA Read Pairs in High-Throughput Sequencing Data

piRNAs and their biogenesis pathways are well-conserved in Metazoans (Grimson et al. 2008). piRNAs have been implicated in transcriptional, post-transcriptional, and translational regulation (Grivna et al. 2006; Lin & Yin 2008; Brennecke et al. 2008; Brennecke et al. 2007; Aravin et al. 2007). We analyze the signatures of a critical process in the primary and secondary mechanism of piRNA biogenesis, referred to as the amplification loop.\n\nThe presence of U-1 and A-10 bias within piRNA populations is an indicator, but not an absolute measure of piRNA amplification. By further considering imperfect and perfect sequence complementarity within the first ten base pairs of piRNAs, the active site promoting secondary piRNA biogenesis, we developed practical and statistically powerful metrics to observe relative piRNA amplification. TruePaiR is a fast and effective general software tool to assess the relative utilization of piRNA amplification in high throughput sRNA sequencing data.\n\nThe results of TruePaiR runs in seven species and five tissues serve as a benchmark for meaningful context of piRNA amplification. The TruePaiR metrics provide foundational data regarding the in terms of species specificity, tissue specificity, as well as the relative participation based upon origin-based piRNA subsets regarding piRNA amplification. The low degree of variability of same sample TruePaiR runs allows for metric reliability, reproducibility, as well as the ability to detect subtle differences in piRNA amplification within and between species and tissues. Given that TruePaiR serves as an effective and consistent metric of piRNA amplification across species, it can represent a new, meaningful standard in the degree of piRNA amplification in a specific organism and tissue that is or is not expected to undergo piRNA amplification.

bioinformatics

piClusterBusteR: Software For Automated Classification And Characterization Of piRNA Cluster Loci

BackgroundPiwi-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.\n\nResultsWe 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.\n\npiClusterBusteR 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.\n\nConclusionsWe 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.\n\npiClusterBusteR is available for download on GitHub: https://github.com/pschreiner/piClusterBuster

bioinformatics