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Department of Pathology

Areas of Interest
- Computational Genomics
- Non-coding RNAs and disease
- Epitranscriptomics
- RNA modification


Anton read Natural Sciences at Trinity College Dublin, specialising in Genetics before coming to the University of Cambridge for his Ph.D. studies in 1998, with  Prof. Christos Ouzounis, working on protein families and biological network analysis. His postdoctoral work was with Prof. Chris Sander at Memorial Sloan-Kettering Cancer Center, New York working on small RNA target prediction. In 2004 he started his own independent laboratory at the Wellcome Trust Sanger Institute, exploring the functions of small non-coding RNAs in various biological processes and diseases. In 2004 he moved to the EMBL - European Bioinformatics Institute, as a Group Leader where he continued to work on non-coding RNAs, including piwi-associated RNAs, lncRNAs and RNA modifications. His laboratory moved to the Department of Pathology in 2018. Anton is also academic lead for the genomics facility at the department which specialises in large-scale sequencing, microarrays and genotyping projects. Anton oversees the Oxford Nanopore facility that hosts the School of Biological Sciences PromethION device.


PhD studentship available

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. 


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.

Nanopore direct RNA sequencing of methylated nucleotides in RNA

Oxford Nanopore direct RNA sequencing of modified nucleotides in RNA.


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.

Uridylation profiles of miRNAs in cancer datasets

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.

Sylamer nucleotide motif enrichment profiles in T-helper cells (mouse) for a mouse deficient in miR-155

 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).

BioLayout Network

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.




Key publications: 

Recent Selected Publications

  • Vitsios DM, Kentepozidou E, Quintais L, Benito-Gutiérrez E, van Dongen S, Davis MP*, Enright AJ*. Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forestsNucleic Acids Res., 2017 gkx836
  • Ivanova I, Much C, Di Giacomo M, Azzi C, Morgan M, Moreira PN, Monahan J, Carrieri C, Enright AJ*, O'Carroll D* The RNA m6A Reader YTHDF2 Is Essential for the Post-transcriptional Regulation of the Maternal Transcriptome and Oocyte Competence,  Mol Cell. 2017 Sep 21;67(6):1059-1067.
  • Morgan M, Much C, DiGiacomo M, Azzi C, Ivanova I, Vitsios DM, Pistolic J, Collier P, Moreira PN, Benes V, Enright AJ, O'Carroll D. mRNA 3' uridylation and poly(A) tail length sculpt the mammalian maternal transcriptomeNature. 2017 Aug 17;548(7667):347-351
  • Davis MP, Carrieri C, Saini HK, van Dongen S, Leonardi T, Bussotti G, Monahan JM, Auchynnikava T, Bitetti A, Rappsilber J, Allshire RC, Shkumatava A, O'Carroll D, Enright AJ. Transposon-driven transcription is a conserved feature of vertebrate spermatogenesis and transcript evolution. EMBO Rep. 2017 Jul;18(7):1231-1247
  • Vitsios DM, Davis MP, van Dongen S, Enright AJ. Large-scale analysis of microRNA expression, epi-transcriptomic features and biogenesis. Nucleic Acids Res. 2017 Feb 17;45(3):1079-1090
  • Bussotti G, Leonardi T, Clark MB, Mercer TR, Crawford J, Malquori L, Notredame C, Dinger ME, Mattick JS, Enright AJ. Improved definition of the mouse transcriptome via targeted RNA sequencingGenome Res. 2016 May;26(5):705-16. doi: 10.1101/gr.199760.115.
  • Vitsios DM, Enright AJChimira: analysis of small RNA sequencing data and microRNA modifications. Bioinformatics. 2015 Oct 15;31(20):3365-7. doi: 10.1093/bioinformatics/btv380. Epub 2015 Jun 20.

Enright lab members underlined

* denotes co-corresponding or co-first authors


All Publications

Teaching and Supervisions

  • Part II Pathology - Genetics and Genomics of Disease - Option Organiser and Lecturer
  • Part 1B - Mathematical and Computational Biology - Bioinformatics Module Organiser
  • Part 1A - Mathematical Biology
  • Part II - Bioinformatics BBS

Supervisions: Part II Pathology, Part II BBS Bioinformatics, Part 1B Mathematical Biology.

Research supervision: 

Lab Members

We are a multi-disciplinary team of both wet and dry researchers. 

Ph.D. Students

  • Albert Kang
  • Stephanie Wenlock
  • Melanie Maranian
  • Sethlina Aryee

Masters Students and Undergraduates

  • Adam Agbamu
  • Levia Yee



University Assistant Professor and Group Leader- Division of Cellular and Molecular Pathology
Academic Lead - Cambridge Genomic Services
Staff Fellow, Director of Studies, Deputy Graduate Tutor - Trinity Hall
Anton Enright

Contact Details

Department of Pathology
Tennis Court Road
+44 1223 339871 / 33709
Takes PhD students
Not available for consultancy