Non-commutativef-divergence functional

Positive selection drives a correlation between non-synonymous/synonymous divergence and functional divergence

MOTIVATION Functional divergence among proteins is often assumed to be strongly influenced by natural selection, as inferred from the ratio of non-synonymous nucleotide divergence (d(N)) to synonymous nucleotide divergence (d(S)). That is, the more a mutation changes protein function, the more likely it is to be either selected against or selectively favored, and because the d(N)/d(S) ratio is ...

Gene Duplication and Functional Divergence

Non-parametric Jensen-Shannon Divergence

Quantifying the difference between two distributions is a common problem in many machine learning and data mining tasks. What is also common in many tasks is that we only have empirical data. That is, we do not know the true distributions nor their form, and hence, before we can measure their divergence we first need to assume a distribution or perform estimation. For exploratory purposes this ...

Functional divergence of proteins through frameshift mutations.

Frameshift mutations are generally considered to be deleterious and of little importance for the evolution of novel gene functions. However, by screening an exhaustive set of vertebrate gene families, we found that, when a second transcript encoding the original gene product compensates for this mutation, frameshift mutations can be retained for millions of years and enable new gene functions t...

Ltter Functional Divergence for Every Paralog

Because genes can be constrained by selection at more than one phenotypic level, the relaxation of constraints following gene duplication allows for functional divergence (FD) along multiple phenotypic axes. Many studies have generated individual measures of FD, but the profile of FD between paralogs across levels of phenotypic space remains largely uncharted. We evaluate paralog pairs that ori...