Annual Scientific Report 2015

Marioni Group
Computational and
Evolutionary Genomics
Our research focuses on developing the computational and statistical
tools necessary to exploit high-throughput genomics data, in order to
understand the regulation of gene expression and model developmental and
evolutionary processes.
We develop the computational and statistical tools
necessary to exploit high-throughput genomics data, in
order to understand the regulation of gene expression
and model developmental and evolutionary processes.
Within this context, we focus on three specific areas:
gene expression regulation, evolution of cell types
and modelling variability in expression levels. To
understand how the divergence of gene expression
levels is regulated, we associate changes in expression
with a specific regulatory mechanism. In so doing, we
gain critical insights into speciation and differences in
phenotypes between individuals. We study the evolution
of cell types by using gene expression as a definition of
the molecular fingerprint of individual cells. Comparing
the molecular fingerprint associated with a particular
tissue across species allows us to decipher whether
specific cell types arise de novo during speciation, or
whether they have a common evolutionary ancestor.
We model spatial variability in gene-expression levels
within a tissue or organism to identify heterogeneous
patterns of expression within a cell type. This potentially
allows us to uncover new cell types, perhaps with novel
functions. We use similar approaches to study the extent
of heterogeneity present throughout a tumour.
These strands of research are brought together
by single-cell sequencing technologies. Studying
variability in gene expression (and other genome-wide
characteristics) at a single-cell level is revolutionising
our ability to assay regulatory variation, molecular
fingerprints and spatial patterns of expression. As
founding members of the Sanger Institute – EMBL-EBI
Single Cell Genomics Centre, and with group leader
John Marioni as co-ordinator, we are closely involved
in the centre’s efforts to improve data generation
and analysis methods, especially single-cell RNAsequencing,
and in using them to answer numerous
exciting biological questions. We see the development
of appropriate statistical and computational tools as
critical to the full exploitation of these data, and will
focus on these challenges over the next few years.
Major achievements
In 2015 we built on our previous work in the field of
single-cell transcriptomics, collaborating with the
Richardson group at the Medical Research Council
(MRC) Biostatistics Unit to develop a hierarchical
Bayesian approach for identifying highly variable genes
(Vallejos et al., 2015). This approach builds on our
earlier work (Brennecke
et al., 2013) by jointly
inferring normalisation and
noise parameters for both
technical and biological
genes. We recently extended
this model to identify genes
that show different noise
profiles between conditions.
Moreover, we developed
approaches for robustly
identifying stochastic allelespecific
expression (Kim
et al., 2015), providing an
important tool for dissecting
A new method for analysing RNA
sequence data allows researchers
to identify new subtypes of cells,
creating order out of seeming chaos
This novel technique represents a
major step forward for single-cell
genomics.
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2015 EMBL-EBI Annual Scientific Report