Caister Academic Press

Processing Large-scale Small RNA Datasets in Silico

Daniel Mapleson, Irina Mohorianu, Helio Pais, Matthew Stocks, Leighton Folkes and Vincent Moulton
from: Next-generation Sequencing: Current Technologies and Applications (Edited by: Jianping Xu). Caister Academic Press, U.K. (2014)


The latest advances in next generation sequencing technologies have resulted in a dramatic increase in the total number of sequences that can be produced per experiment as well as a significant decrease in sequencing error and bias. These improvements have driven forward both in silico and in vivo analyses in small RNA (sRNA) research. Until recently, the majority of existing sRNA computational methods focused on the analysis of a particular class of sRNAs, the micro RNAs. However there are several less well characterised classes of sRNAs present in plants, animals and other organisms that may have important biological function. This has prompted the development of novel data-driven approaches for sRNA analysis that are designed to cope with the increase in both the number of sequences and the diversity of information that is extracted. This chapter reviews these approaches and consists of three main sections. First, we consider the steps required to produce sRNA libraries. After this, a typical workflow for pre-processing the output from sequencing machines is presented. This includes an outline of the state of the art for adaptor removal, read filtering and selection, read mapping, and various approaches to normalise the read abundances. We then present the main computational techniques for sRNA analysis. More specifically, we discuss qualitative statistics for sample checking, biogenesis driven approaches for identification of known and novel sRNAs, and methods for predicting their function. We also give an overview of how correlation tools, developed to predict the types of interactions between sRNAs and their target genes, can refine information from target prediction tools. The chapter concludes with some remarks on how in silico sRNA research might evolve in the near future read more ...
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