Manatee: detection and quantification of small non-coding RNAs from next-generation sequencing data

Abstract

Small non-coding RNAs (sncRNAs) play important roles in health and disease. Next Generation Sequencing technologies are considered as the most powerful and versatile methodologies to explore small RNA (sRNA) transcriptomes in diverse experimental and clinical studies. Small RNA-Seq data analysis proved to be challenging due to non-unique genomic origin, short length and abundant post-transcriptional modifications of sRNA species. Here we present Manatee, an algorithm for quantification of sRNA classes and detection of uncharacterized expressed non-coding loci. Manatee adopts a novel approach for abundance estimation of genomic reads that combines sRNA annotation with reliable alignment density information and extensive reads salvation. Comparison of Manatee against state-of-the-art implementations using real/simulated data sets demonstrates its superior accuracy in quantification of diverse sRNA classes providing at the same time insights about unannotated expressed loci. It is user-friendly, easily embeddable in pipelines and provides a simplified output suitable for direct usage in downstream analyses and functional studies.