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Title: Computational methods for genomics data analysis: Cancer detection using circulating tumor DNA and miRNA expression inference in single cells
Abstract: I will present examples of projects involving statistical modelling within two areas of ongoing interest to my group.
Tumors continuously shed DNA into the bloodstream, though typically in minute amounts. Sequencing technology can capture low-frequency circulating tumor DNA (ctDNA) fragments. In principle, we may therefore be able to detect cancer based on a blood sample, which can be collected with ease, low risk, and low costs. I will give an introduction to the clinical opportunities offered by ctDNA, the technological advances, the current data types, the main statistical challenges, and the currently applied statistical approaches. I will further present the clinical translational setting at my home department (Aarhus, Denmark) and some of our efforts to improve ctDNA detection, which include development of improved null models for DNA sequencing errors and models for mutational signal aggregation across select cancer genes or genome-wide.
MicroRNAs (miRNAs) are short RNA molecules (~22 nucleotides long). They show highly tissue- and cell-type specific expression patterns. Each miRNA regulates a specific set of genes by destabilising their mRNAs. Recent progress in single-cell sequencing allows mRNA expression profiles to be routinely obtained. However, single-cell miRNA expression cannot be quantified in high throughput settings. We have developed a method for inferring miRNA expression from mRNA expression profiles, by modelling the regulatory effect of miRNAs on target mRNAs . This approach has allowed us to infer miRNA expression at the single cell level . I will briefly outline the core ideas of the approach and show some results from its application on large single cells data sets.
I’m visiting UBC and the Department of Statistics until August 2022 and hope to interact with many of you during this time.
: Nielsen MM, Tataru P, Madsen T, Hobolth A & Pedersen JS. Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments. Algorithms Mol. Biol. 13, 17 (2018).
: Nielsen, M. M. & Pedersen, J. S. miRNA activity inferred from single cell mRNA expression. Sci. Rep. 11, 9170 (2021).
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