Autoregressive Modeling of Coding Sequence Lengths in Bacterial Genome

TL;DR

This paper demonstrates that bacterial coding sequence lengths can be effectively modeled using first order autoregressive processes, revealing local interactions and offering insights into biological organization.

Contribution

It introduces the application of autoregressive modeling to bacterial CDS lengths, highlighting the potential for broader biological and physical process modeling.

Findings

CDS lengths exhibit short-range correlations

First order autoregressive models fit the data well

Potential applications in diverse biological and physical systems

Abstract

Previous investigation of coding sequence lengths (CDS) in the bacterial circular chromosome revealed short range correlation in the series of these data. We have further analyzed the averaged periodograms of these series and we found that the organization of CDS can be well described by first order autoregressive processes. This involves interaction between the neighboring terms. The autoregressive analysis may have great potential in modeling various physical and biological processes like light emission of galaxies, protein organization, cell flickering, cognitive processes and perhaps others.