Entromics

Genome sequence contains information which — when used as input into an exact thermodynamic formula — is recalculated into the energy needed to incorporate a variant base into any specific position in the DNA strand. This led to the discovery of coherence for these incorporation entropies along the complete genome sequence. A short explanation of this phenomenon and how it is quantitatively characterized can be found here. For more details see our paper “Entromics — thermodynamics of sequence dependent base incorporation into DNA reveals novel long-distance genome organization”.

With personal genotype weighted by incorporation entropy (IE) and IE coherence (IEC) differences between the personal and consensus genome or gene, it is possible to find “intuitive” relationships to function and model them quantitatively. These models of relationships between the personal genotype-dependent IE and IEC difference patterns then provide personalized predictors of the gene-related function or phenotype. The example below shows that genetic variants of serotonin receptor HTR2A are related to depression not by the presence or absence of some SNP alleles and their combinations. Instead, the prediction of the psychological impact of the personal variants of this gene is a “second order”. The role of personal variants in promoting depression is derived from the deviation of the personal change of the consensus IEC from the general dependence of IEC difference on the allele number for an individual.

Importantly, representation of the personal genome by incorporation entropy and entromic coherence is the only approach allowing direct quantitative characterization of the personal genome status in the individual clinical, behavioral, psychological, socio-economical or lifestyle contexts.

Entromics is an integral component of the Personalized Network Medicine platform. Connected by the optimized information-entropy background, the PNM approach guarantees improvement in project results and new hypothesis generation, and allows for more analyses from the same data. More results are therefore obtained for the same project cost. All those advantages minimize the probability of project failure.