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Computational methods for analysis of single-cell RNA-seq and cancer mutability

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In this talk I will be presenting some of the work in my group focusing on quantitative models of gene regulation. The first part will cover computational analysis of single-cell RNA -seq data, while the second part will cover the role of non-canonical secondary structures of DNA on mutability in cancer patients.

Compared to bulk RNA -seq, single-cell RNA -seq presents many opportunities for addressing biological questions that were previously inaccessible. To realize this potential, however, novel computational methods are required. We have developed a new method for unsupervised feature selection. Without any prior assumptions of cell-types, the method is able to identify a set of informative genes. We demonstrate that these genes correspond to differentially expressed genes and that they are more informative than highly variable genes for clustering and batch correction. Moreover, we use feature selection for scmap, a fast and accurate method for comparing two different samples.

Although DNA for the most part is in the canonical B-DNA configuration, there are more than 20 other configurations known. Here, we demonstrate that the non-canonical secondary structures are predictive of mutability in cancer. The increased mutability of sites overlapping non B-DNA motifs suggests that one must take this aspect into consideration when characterizing driver mutations.

In addition to presenting recent results from my group’s research, I will also discuss some of my thoughts on how people from a physics background can make contributions to molecular biology and genomics.

This talk is part of the Biological and Statistical Physics discussion group (BSDG) series.

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