Slides from 23rd January PyData Salamanca meetup!

Understanding the control of gene expression is critical for us in the relationship between genotype and phenotype. The need for reliable assessment of transcript abundance in biological samples has driven to develop novel technologies such as DNA microarray and RNA-Seq.

RNA-Seq technology has emerged as an attractive alternative to traditional microarray platforms but both techniques are used to determine a transcriptome gene expression profiling. Our meeting focuses on comparing these two methods analysing the similarities and differences as well analysing in detail each step of both techniques and determining the efficiency and the best use depending on our data.

With the development and advancement of nanotechnologies for DNA and RNA sequencing, new and huge databases have emerged that can no longer be analyzed with classical statistical techniques. The new form and amount of such data require new tools to describe, merge, filter, normalize and analyze them.

The omic data allow us to relate phenotypic variables with cellular and molecular information about the biological processes involved in the onset and development of diseases that may have a genetic nature. A basic tool in the study of these relationships is the differential expression analysis that consists in the comparison between groups of the expression of our genome or our transcriptome.

Why all humans do not age at the same pace? Recent research in the field of human aging is beginning to understand that aging is a complex biological process, which is strongly affected by genetics, but also by the environment. In the era of precision and personalized medicine, the idea of a personal bio-age related to our transcriptomic profile is trying to explain why humans do not always age equally.

Here, we present a Deep Neural Network model, capable of inferring the brain bio-age of a given individual from its transcriptomic profile.