ShortStack

Small RNA sequencing allows genome-wide discovery, categorization, and quantification of genes producing regulatory small RNAs. Many tools have been described for annotation and quantification of microRNA loci (MIRNAs) from small RNA-seq data. However, in many organisms and tissue types, MIRNA genes comprise only a small fraction of all small RNA-producing genes. ShortStack is a stand-alone application that analyzes reference-aligned small RNA-seq data and performs comprehensive de novo annotation and quantification of the inferred small RNA genes. ShortStack's output reports multiple parameters of direct relevance to small RNA gene annotation, including RNA size distributions, repetitiveness, strandedness, hairpin-association, MIRNA annotation, and phasing. In this study, ShortStack is demonstrated to perform accurate annotations and useful descriptions of diverse small RNA genes from four plants (Arabidopsis, tomato, rice, and maize) and three animals (Drosophila, mice, and humans). ShortStack efficiently processes very large small RNA-seq data sets using modest computational resources, and its performance compares favorably to previously described tools. Annotation of MIRNA loci by ShortStack is highly specific in both plants and animals. ShortStack is freely available under a GNU General Public License.[1]

High-throughput sequencing of small RNAs (sRNA-seq) is a popular method used to discover and annotate microRNAs (miRNAs), endogenous short interfering RNAs (siRNAs), and Piwi-associated RNAs (piRNAs). One of the key steps in sRNA-seq data analysis is alignment to a reference genome. sRNA-seq libraries often have a high proportion of reads that align to multiple genomic locations, which makes determining their true origins difficult. Commonly used sRNA-seq alignment methods result in either very low precision (choosing an alignment at random), or sensitivity (ignoring multi-mapping reads). Here, we describe and test an sRNA-seq alignment strategy that uses local genomic context to guide decisions on proper placements of multi-mapped sRNA-seq reads. Tests using simulated sRNA-seq data demonstrated that this local-weighting method outperforms other alignment strategies using three different plant genomes. Experimental analyses with real sRNA-seq data also indicate superior performance of local-weighting methods for both plant miRNAs and heterochromatic siRNAs. The local-weighting methods we have developed are implemented as part of the sRNA-seq analysis program ShortStack, which is freely available under a general public license. Improved genome alignments of sRNA-seq data should increase the quality of downstream analyses and genome annotation efforts.[2]

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