Nathan R Johnson | Search bioRxiv

bioRxiv2016

Genome-Wide Analysis of Single Non-Templated Nucleotides in Plant Endogenous siRNAs and miRNAs

Plant small RNAs are subject to various modifications. Previous reports revealed widespread 3' modifications (truncations and non-templated tailing) of plant miRNAs when the 2'-C-methyltransferase HEN1 is absent. However, non-templated nucleotides in plant heterochromatic siRNAs have not been deeply studied, especially in wild-type plants. We systematically studied non-templated nucleotide patterns in plant small RNAs by analyzing small RNA sequencing libraries from Arabidopsis. tomato, Medicago, rice, maize, and Physcomitrella. Elevated rates of non-templated nucleotides were observed at the 3' ends of both miRNAs and endogenous siRNAs from wild-type specimens of all species. 'Off-sized' small RNAs, such as 25 and 23 nt siRNAs arising from loci dominated by 24 nt siRNAs, often had very high rates of 3'-non-templated nucleotides. The same pattern was observed in all species that we studied. Further analysis of 24 nt siRNA clusters in Arabidopsis revealed distinct patterns of 3'-non-templated nucleotides of 23 nt siRNAs arising from heterochromatic siRNA loci. This pattern of non-templated 3' nucleotides on 23 nt siRNAs is not affected by loss of known small RNA 3'-end modifying enzymes, and may result from modifications added to longer heterochromatic siRNA precursors.

Plant Biology↗

bioRxiv2016

Improved Placement of Multi-Mapping Small RNAs

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 which align to multiple genomic locations, which makes determining their true loci of origin 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.\n\nArticle SummaryHigh-throughput sequencing of small RNAs (sRNA-seq) is a frequently used technique in the study of small RNAs. Alignment to a reference genome is a key step in processing sRNA-seq libraries, but suffers from enormous rates of multi-mapping reads. Current methods for sRNA-seq alignment either place these reads randomly or ignore them, both of which distort downstream analyses. Here, we describe a locality-based weighting approach to make better decisions of placement of multi-mapped sRNA-seq data, and test our implementation of this method. We find that our method gives superior performance in terms of placing multi-mapped sRNA-seq data. An implementation of our method is freely available within the ShortStack small RNA analysis program. Use of this method may dramatically improve genome-wide analyses of small RNAs.

Bioinformatics↗