Dr Irene Nobeli

Overview

Web profiles

• Research group homepage
• Twitter (@irileniaN)

Administrative responsibilities

• Director, MSc Bioinformatics
• Assistant Dean (strategic) for Retention and Recruitment, School of Science
• Admissions Tutor, MSc/MRes Bioinformatics
• Module Organiser for the MSc Bioinformatics modules: Sequence Analysis and Genomics, Omics, MSc Project
• Departmental representative for Retention and Rectruitnent

ORCID

0000-0001-8616-170X

Research

Research interests

• Bioinformatics
• Transcriptomics
• Regulatory RNAs
• Chemoinformatics
• Mycobacteria
• Autism

Research overview

The lab's expertise is in computational biology and chemoinformatics. Current interests include regulatory RNAs, transcriptomics in health and disease, disorders of the brain and the role of small molecules in Biology.

Research Centres and Institutes

• Birkbeck Institute for Data Analytics (BIDA)
• Institute of Structural Molecular Biology (ISMB)

Supervision and teaching

Supervision

Supervisions within Birkbeck:

6

Current doctoral researchers

• CHARALAMPOS BOUTSELAKIS

  Nature of supervision:

  Principal Supervisor, Oct 2018 onwards

• JENNIFER STIENS

  Nature of supervision:

  Principal Supervisor, Oct 2020 onwards

  Title of thesis:

  Discovery and characterisation of the non-coding genome of pathogenic mycobacteria

• BOLAJI COKER

  Nature of supervision:

  Principal Supervisor, Oct 2021 onwards

• JUSTIN BARTON

  Nature of supervision:

  Principal Supervisor, Mar 2022 onwards

  Title of thesis:

  Functional profiling of immune repertoire sequencing data using deep learning

  Description:

  Immune receptors – found on the surface of B cells and T cells – play a central role in the adaptive immune system and are characterised by astonishing levels of sequence diversity. In T cells, the observed diversity is underpinned by three key factors: the large number of T cell germline gene segments (e.g. around 50 β and 70 α Variable segments in humans); the stochastic combinatorial process known as V(D)J recombination that generates T cell receptor (TCR) chains from those germline segments; and the clonal selection process associated with the expansion of antigen-specific TCRs [1]. Matured B cells achieve even greater diversity due to somatic hypermutation.

  Recent advances in high throughput immunosequencing have made immune repertoire sequencing affordable and practical for a wide range of clinical applications. However, the function of individual B cells and T cells – i.e. their antigenic target (whether defined in terms of a single protein or specific epitope) – is unknown in the vast majority of cases. The ability to identify the likely antigenic target of B cells and T cells would open up new diagnostic possibilities in the context of infection, cancer, and autoimmunity [2].

  The aim of the research outlined in this proposal is to develop deep-learning algorithms to predict in silico the antigenic targets of the T cell receptors found in immune repertoires. This would enable functional profiling of immune repertoire sequencing data, which would in turn support a broad range of clinical uses from diagnostics to disease progression tracking in longitudinal data.

• TRUPTI GORE

  Nature of supervision:

  Principal Supervisor, Mar 2022 onwards

Doctoral alumni since 2013-14

• KRZYSZTOF SZKOP

  Nature of supervision:

  Principal Supervisor

  Title of thesis:

  Computational studies of the role of untranslated regions in regulating gene expression

  Awarded:

  2019

Teaching

Teaching modules

• Specialist Laboratory Research Project (SCBS036D6)
• Sequence Analysis and Omics (SCBS088S7)
• The Molecular Basis of Life (SCBS095S7)

Publications

External Repositories