Enright Group

Our Research

Complete genome sequencing projects have generated enormous amounts of data. Although progress has been rapid, a significant proportion of the genes in any given genome are either unannotated or possess a poorly characterised function. Our group aims to detect, predict and describe the functions of genes, proteins and regulatory non-coding RNAs as well as their interactions in living organisms and their implications for disease. We are a multi-disciplinary team using computational biology, bioinformatics and high-throughput genomics to solve these problems. Our work encompasses a number of areas:

The epitranscriptome - RNA modification and methylation

Gene regulation is a fundamental process that underpins cell development, differentiation, function and homeostasis. The discovery of epigenetic marks on both DNA and histones heralded a new era in our understanding of gene regulation. We believe that we are on the cusp of another wave of critical biological discoveries regarding similar marks placed on mRNAs and regulatory RNAs. These events sculpt the transcriptome altering how mRNAs are processed, spliced, translated, degraded and how they interact with proteins and other RNAs. Understanding this fundamental process is of critical importance to further our understanding of biological regulation and understanding mechanisms that underlie disease aetiology and susceptibility.

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Methylated Nucleosides in RNA.

To fully explore this growing world of epitranscriptomic modifications and their effects, we are combining cutting-edge computational genomics research with Illumina high-throughput sequencing, Oxford Nanopore Direct RNA sequencing and single nucleotide mass-spectrometry. Over the coming years we will be developing novel algorithms, methods, tools and protocols for detecting and describing RNA modifications and their impact in living systems.

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Algorithms and methods for non-coding RNA research

We have been working on how small RNAs such as microRNAs (miRNAs) regulate transcription and mRNA stability for over 15 years. We developed one of the first algorithms for miRNA target detection (miRanda) and working with miRBase to make the first computational miRNA targets available to the community. The advent of next-generation sequencing made the detection of miRNAs and their global, transcriptome-wide effects, far easier to detect. Recently we have developed a range of algorithms and web-based resources for the community involved in this field.

Additionally, we worked extensively on other classes of non-coding RNA molecules such as piwi-RNAs and long non-coding RNAs. Recently, we developed chimiRa a web-based tool for processing of small RNA sequencing datasets and the detection of 3' modification events such as uridylation.

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Modification profiles for microRNAs present in human cancer datasets detected using ChimiRa.

Elucidating the functions of non-coding RNAs in biological processes and disease

We have worked on the roles of miRNAs and lncRNAs in a variety of biological systems and disease models. Our algorithm Sylamer has been widely used to assess the effects of miRNAs on transcriptome level data obtained from sequencing. This has allowed us to establish roles for a number of miRNAs with our collaborators including: Maternal-Zygotic transition, Mouse models of deafness, Ichtyosis and a range of cancers. We work closely with Matthew Murray and Nicholas Coleman on the roles of miRNAs in paediatric germ cell tumours and on HPV pathogenesis.

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Sylamer motif enrichment profiles showing microRNA motifs present in T-helper cells from mice deficient in miR-155

Machine Learning and Data Visualisation in biology

We have an ongoing interest in the analysis of large datasets using machine learning techniques such as SVMs, RVMs, unsupervised clustering (e.g. Markov Clustering), Random Forests and Logistic Regression. Recently, we released miRNovo, a machine learning microRNA classifier for accurate prediction of miRNAs from large-scale sequencing projects. We have developed a number of tools for large-scale visualisation of biological networks including the BioLayout tool developed with BBSRC funding in collaboration with Prof. Tom Freeman (University of Edinburgh).

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BioLayout Express 3D visualisation of samples from a human cell atlas (Tom Freeman) based on Markov Clustering (MCL)

We welcome visitors to the laboratory who wish to undertake their own computational analyses under our guidance. We are always happy to talk to prospective students, visitors and lab members.