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Advances in statistical genomics

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A series of three online talks to highlight current advances in statistical genomics.

About this Event

Talks from three speakers will cover new advances in statistical genomics.

This is a meeting of the British and Irish Region of the International Biometric Society.

Registration is free but places are limited.

Programme

Wednesday 28th October (all UK BST)

13:00 - 13:35 John Marioni, EMBL-EBI and University of Cambridge

Challenges in modelling spatially-resolved single-cell genomics data

13:35 - 14:10 Catalina Vallejos, MRC Human Genetics Unit, Edinburgh & The Alan Turing Institute

scMET: Bayesian modelling of DNA methylation heterogeneity at single-cell resolution

14:10 - 14:45 Maria Secrier, UCL Genetics Institute

Reconstructing the mutational histories of oesophageal cancer

Abstracts

John Marioni, EMBL-EBI and University of Cambridge

Challenges in modelling spatially-resolved single-cell genomics data

I will describe how we have combined cutting-edge experimental approaches with novel computational approaches in order to generate a comprehensive map of how gene expression varies in space across an entire mouse embryo at the 8-12 somite stage of development. Previous studies using scRNA-seq have reconstructed molecular trajectories computationally but, in the absence of cell-specific spatial information, it has been impossible to understand where particular populations of cells are located in the embryo, to understand the signaling environment to which they are exposed and how this might impact their molecular make up and their ultimate fate. I will provide a blueprint for how such studies can be tackled from a computational perspective, highlighting ongoing work as well as future challenges.

Catalina Vallejos, MRC Human Genetics Unit, Edinburgh & The Alan Turing Institute

scMET: Bayesian modelling of DNA methylation heterogeneity at single-cell resolution

High throughput measurements of DNA methylomes at single-cell resolution are a promising resource to quantify the heterogeneity of DNA methylation and uncover its role in gene regulation. However, limitations of the technology result in sparse CpG coverage, effectively posing challenges to robustly quantify genuine DNA methylation heterogeneity. Here we tackle these issues by introducing scMET, a hierarchical Bayesian model which overcomes data sparsity by sharing information across cells and genomic features, resulting in a robust and biologically interpretable quantification of variability. scMET can be used to both identify highly variable features that drive epigenetic heterogeneity and perform differential methylation and differential variability analysis between pre-specified groups of cells. We demonstrate scMET’s effectiveness on some recent large scale single cell methylation datasets, showing that the scMET feature selection approach facilitates the characterisation epigenetically distinct cell populations. Moreover, we illustrate how scMET variability estimates enable the formulation of novel biological hypotheses on the epigenetic regulation of gene expression in early development. An R package implementation of scMET is publicly available at https://github.com/andreaskapou/scMET.

Maria Secrier, UCL Genetics Institute

Reconstructing the mutational histories of oesophageal cancer

Mutational processes contributing to the development of cancer emerge from various risk factors of the disease and impose specific imprints of somatic alterations in the genomes of cancer patients. These mutational footprints, called “signatures”, can be read from the tumour sequencing data and are the result of the interplay between DNA damage and repair driving neoplastic progression. In this sense, they can be considered a form of evidence for historical mutational events that have acted during tumour evolution. I will discuss some of the insights we have obtained into the development and progression of oesophageal adenocarcinoma, an aggressive disease with limited treatment options, by tracking mutational signatures in large cohorts of whole-genome sequenced human cancer tissues. Tracing mutational signature trajectories from early to later stages of cancer development in primary tumours unveils a refined picture of evolution in this cancer, with frequent bottlenecks (~51% of cases) where mutational pressures shift. These bottleneck events were found to be correlated with oncogenic and immunologic hallmarks. We suggest that the observed genomic signatures and their specific temporal dynamics could be further exploited for early detection of this cancer.

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