Quality checking and expression analysis of high-throughput small RNA sequencing data

Beckers, Matthew (2015) Quality checking and expression analysis of high-throughput small RNA sequencing data. Doctoral thesis, University of East Anglia.

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The advent of high-throughput RNA sequencing (RNA-seq) methods
have made it possible to sequence transcriptomes for the cell-wide
identi�cation of small non-coding RNAs (sRNAs) and to assess their
regulation using di�erential expression analysis by comparing two or
more di�erent conditions. During an analysis of a typical set of sRNA
sequencing (sRNA-seq) libraries, a large variety of tools and methods
are used on the dataset in order to understand the data's quality, content,
and to summarise the knowledge gained from the entire analysis.
Many of the tools available to do this were created for mRNA sequencing
(mRNA-seq) datasets. In this thesis, we present and implement
a processing pipeline that can be used to assess the quality and the
di�erential expression of sRNA-seq datasets over two or more di�erent
conditions. We then utilise aspects of this pipeline in various
sRNA-seq experiments. Firstly, we combine our pipeline with current
tools for miRNA identi�cation to assess the regulation of miRNAs
during larval caste di�erentiation in a novel genome; the European
bumblebee (Bombus terrestris). Secondly, we explore the di�erential
expression during cell stress of all classes of sRNAs using two cell lines
in humans. We also �nd that a speci�c protein, Ro60, is required for
the expression of mRNA-derived sRNAs during stress, similar to the
way in which sRNAs derived from Y RNAs are regulated. Finally,
we utilise our understanding of sRNA mapping patterns, alongside
current tools for miRNA identi�cation, to search for functional miRNAs
and other sRNAs in the novel genomes of two diatoms. The
lack of canonical miRNA predictions in this study has repercussions
for the evolutionary theory behind miRNAs. The implementation of
our pipeline for sRNA-seq data provides an interactive and quality
controlled work
ow that can be used to process a dataset from raw
sequences to the results of several di�erential expression experiments
for all identi�ed sRNA classes within a sequenced transcriptome.

Item Type: Thesis (Doctoral)
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Users 2259 not found.
Date Deposited: 05 May 2016 10:45
Last Modified: 01 Oct 2016 00:38
URI: https://ueaeprints.uea.ac.uk/id/eprint/58581


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