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Matteo Cereda, MEng, PhD - "Less is More: going discrete to detect the altered biological processes in heterogeneous high-volume RNA-sequencing data. A prostate cancer story"

Young Group Leader, Cancer Genomics and Bioinformatics lab, Italian Institute for Genomic Medicine (IIGM), Turin, IT
When Mar 26, 2019
from 12:00 PM to 01:30 PM
Where Tigem, Vesuvius Auditorium
Contact Name
Contact Phone 081-19230659
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Abstract
With the growth of high-throughput sequencing projects modern biology is facing novel bottlenecks due to Big Data issues. One of the challenges is to extract relevant information from this high-volume data while accounting for their intrinsic heterogeneity. In my talk I will show you how we addressed this challenge applied to RNA-seq data and introduced a novel concept of discretization of gene expression levels, which we derived from machine learning theory and shaped upon knowledge of RNA biology. I will discuss how we employed our method to: (1) detect cellular programs that are altered upon the somatic loss of PTEN and (2) identify the altered expression of splicing factors driven by FOXA1 in prostate cancer. Overall, I will present you how “going discrete”, or using machine learning approaches derived from Big Data analysis combined with RNA biology, can help in disentangling the noise of heterogeneity from biological processes potentially relevant for complex phenotypes. 


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