piRNA analysis framework from small RNA-Seq data by a novel cluster prediction tool - PILFER.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• PIWI-interacting RNAs (piRNAs) are crucial for germline genome stability and gene regulation.
• The growing volume of piRNA research necessitates advanced computational tools for accurate analysis.
• Identifying piRNA clusters is fundamental for understanding piRNA biogenesis and function.

Purpose of the Study:

• To introduce PILFER (PIrna cLuster FindER), a novel and efficient tool for piRNA cluster prediction.
• To develop and validate a comprehensive pipeline for piRNA detection and annotation using PILFER.
• To compare PILFER's performance against existing piRNA cluster prediction methods.

Main Methods:

• PILFER employs a sliding-window approach, integrating read expression and spatial information for cluster identification.
• A piRNA analysis pipeline was established, incorporating PILFER for processing raw small RNA sequencing data.
• The pipeline was applied to publicly available sequencing data from healthy human germline and somatic tissues.

Main Results:

• PILFER demonstrated statistically superior accuracy in piRNA cluster prediction compared to existing tools.
• The tool exhibited enhanced robustness in defining piRNA clusters.
• PILFER offers significant memory efficiency, making it accessible for use on personal computers.

Conclusions:

• PILFER provides a fast, accurate, and resource-efficient solution for piRNA cluster prediction.
• The developed pipeline enables effective detection and annotation of piRNAs and their clusters.
• PILFER represents a valuable advancement for researchers in the field of small RNA biology.