Genome characterization through dichotomic classes: An analysis of the whole chromosome 1 of A. thaliana

Enrico Properzi; Simone Giannerini; Diego Luis Gonzalez; Rodolfo Rosa

doi:10.3934/mbe.2013.10.199

Article Contents

2013, Volume 10, Issue 1: 199-219. Doi: 10.3934/mbe.2013.10.199

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Genome characterization through dichotomic classes: An analysis of the whole chromosome 1 of A. thaliana

1.
Dipartimento di Scienze Statistiche, Università di Bologna, Via delle Belle Arti 41, 40126, Bologna
2.
CNR-IMM, UOS di Bologna, Via Gobetti 101, 40129 Bologna
3.
Dipartimento di Scienze Statistiche, Università di Bologna, Via delle Belle Arti 41, 40126 Bologna

Received: May 2012

Revised: September 2012

Published: December 2012

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Abstract

In this article we show how dichotomic classes, binary variables naturally derived from a new mathematical model of the genetic code, can be used in order to characterize different parts of the genome. In particular, we analyze and compare different parts of whole chromosome 1 of Arabidopsis thaliana: genes, exons, introns, coding sequences (CDS), intergenes, untranslated regions (UTR) and regulatory sequences. In order to accomplish the task we encode each sequence in the 3 possible reading frames according to the definitions of the dichotomic classes (parity, Rumer and hidden). Then, we perform a statistical analysis on the binary sequences. Interestingly, the results show that coding and non-coding sequences have different patterns and proportions of dichotomic classes. This suggests that the frame is important only for coding sequences and that dichotomic classes can be useful to recognize them. Moreover, such patterns seem to be more enhanced in CDS than in exons. Also, we derive an independence test in order to assess whether the percentages observed could be considered as an expression of independent random processes. The results confirm that only genes, exons and CDS seem to possess a dependence structure that distinguishes them from i.i.d sequences. Such informational content is independent from the global proportion of nucleotides of a sequence. The present work confirms that the recent mathematical model of the genetic code is a new paradigm for understanding the management and the organization of genetic information and is an innovative tool for investigating informational aspects of error detection/correction mechanisms acting at the level of DNA replication.

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Mathematics Subject Classification: 92B05, 92D20, 62P10.

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