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Methods Mol Biol. 2017;1580:193-224. doi: 10.1007/978-1-4939-6866-4_14.

The UEA Small RNA Workbench: A Suite of Computational Tools for Small RNA Analysis.

Author information

1
School of Biological Sciences, University of East Anglia, Norwich, UK.
2
School of Computing Sciences, University of East Anglia, Norwich, UK.
3
The Earlham Institute, Norwich, UK.
4
School of Computing Sciences, University of East Anglia, Norwich, UK. vincent.moulton@uea.ac.uk.

Abstract

RNA silencing (RNA interference, RNAi) is a complex, highly conserved mechanism mediated by short, typically 20-24 nt in length, noncoding RNAs known as small RNAs (sRNAs). They act as guides for the sequence-specific transcriptional and posttranscriptional regulation of target mRNAs and play a key role in the fine-tuning of biological processes such as growth, response to stresses, or defense mechanism.High-throughput sequencing (HTS) technologies are employed to capture the expression levels of sRNA populations. The processing of the resulting big data sets facilitated the computational analysis of the sRNA patterns of variation within biological samples such as time point experiments, tissue series or various treatments. Rapid technological advances enable larger experiments, often with biological replicates leading to a vast amount of raw data. As a result, in this fast-evolving field, the existing methods for sequence characterization and prediction of interaction (regulatory) networks periodically require adapting or in extreme cases, a complete redesign to cope with the data deluge. In addition, the presence of numerous tools focused only on particular steps of HTS analysis hinders the systematic parsing of the results and their interpretation.The UEA small RNA Workbench (v1-4), described in this chapter, provides a user-friendly, modular, interactive analysis in the form of a suite of computational tools designed to process and mine sRNA datasets for interesting characteristics that can be linked back to the observed phenotypes. First, we show how to preprocess the raw sequencing output and prepare it for downstream analysis. Then we review some quality checks that can be used as a first indication of sources of variability between samples. Next we show how the Workbench can provide a comparison of the effects of different normalization approaches on the distributions of expression, enhanced methods for the identification of differentially expressed transcripts and a summary of their corresponding patterns. Finally we describe individual analysis tools such as PAREsnip, for the analysis of PARE (degradome) data or CoLIde for the identification of sRNA loci based on their expression patterns and the visualization of the results using the software. We illustrate the features of the UEA sRNA Workbench on Arabidopsis thaliana and Homo sapiens datasets.

KEYWORDS:

Degradome analysis; Differential expression; High throughput sequencing; Normalization; Small RNA (sRNA); UEA sRNA Workbench; microRNA (miRNA); sRNA loci

PMID:
28439835
DOI:
10.1007/978-1-4939-6866-4_14
[Indexed for MEDLINE]

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