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Yasser Perera

Publications and source records attributed to Yasser Perera.

4 recordsLinked to original sources
arXiv2020

On the gene expression landscape of cancer

A principal component analysis of the TCGA data for 15 cancer localizations unveils the following qualitative facts about tumors: 1) The state of a tissue in gene expression space may be described by a few variables. In particular, there is a single variable describing the progression from a normal tissue to a tumor. 2) Each cancer localization is characterized by a gene expression profile, in which genes have specific weights in the definition of the cancer state. There are no less than 2500 differentially-expressed genes, which lead to power-like tails in the expression distribution functions. 3) Tumors in different localizations share hundreds or even thousands of differentially expressed genes. There are 6 genes common to the 15 studied tumor localizations. 4) The tumor region is a kind of attractor. Tumors in advanced stages converge to this region independently of patient age or genetic variability. 5) There is a landscape of cancer in gene expression space with an approximate border separating normal tissues from tumors.

q-bio.TO↗
arXiv2018

Genetic interactions from first principles

We derive a general statistical model of interactions, starting from probabilistic principles and elementary requirements. Prevailing interaction models in biomedical researches diverge both mathematically and practically. In particular, genetic interaction inquiries are formulated without an obvious mathematical unity. Our model reveals theoretical properties unnoticed so far, particularly valuable for genetic interaction mapping, where mechanistic details are mostly unknown, distribution of gene variants differ between populations, and genetic susceptibilities are spuriously propagated by linkage disequilibrium. When applied to data of the largest interaction mapping experiment on Saccharomyces Cerevisiae to date, our results imply less aversion to positive interactions, detection of well-documented hubs and partial remapping of functional regions of the currently known genetic interaction landscape. Assessment of divergent annotations across functional categories further suggests that positive interactions have a more important role on ribosome biogenesis than previously realized. The unity of arguments elaborated here enables the analysis of dissimilar interaction models and experimental data with a common framework.

stat.ME↗
arXiv2016

Impact of germline susceptibility variants in cancer genetic studies

Although somatic mutations are the main contributor to cancer, underlying germline alterations may increase the risk of cancer, mold the somatic alteration landscape and cooperate with acquired mutations to promote the tumor onset and/or maintenance. Therefore, both tumor genome and germline sequence data have to be analyzed to have a more complete picture of the overall genetic foundation of the disease. To reinforce such notion we quantitatively assess the bias of restricting the analysis to somatic mutation data using mutational data from well-known cancer genes which displays both types of alterations, inherited and somatically acquired mutations.

q-bio.QM↗