Study of triplet periodicity differences inside and between genomes

Suvorova Yulia M. () and Korotkov Eugene V.
Additional contact information
Suvorova Yulia M.: Bioinformatics Laboratory, Centre of Bioengineering of the Russian Academy of Sciences, 117312, Prospect 60-tya Oktyabrya, Moscow, Russian Federation
Korotkov Eugene V.: Bioinformatics Laboratory, Centre of Bioengineering of the Russian Academy of Sciences, 117312, Prospect 60-tya Oktyabrya, Moscow, Russian Federation Department of Applied Mathematics, National Nuclear Investigational University (MIFI), 115522, Kashirskoe Shosse, 31, Moscow, Russian Federation

Statistical Applications in Genetics and Molecular Biology, 2015, vol. 14, issue 2, 113-123

Abstract: Triplet periodicity (TP) is a distinctive feature of the protein coding sequences of both prokaryotic and eukaryotic genomes. In this work, we explored the TP difference inside and between 45 prokaryotic genomes. We constructed two hypotheses of TP distribution on a set of coding sequences and generated artificial datasets that correspond to the hypotheses. We found that TP is more similar inside a genome than between genomes and that TP distribution inside a real genome dataset corresponds to the hypothesis which implies that a common TP pattern exists for the majority of sequences inside a genome. Additionally, we performed gene classification based on TP matrixes. This classification showed that TP allows identification of the genome to which a given gene belongs with more than 85% accuracy.

Keywords: gene classification; genomes comparison; protein coding genes; triplet periodicity (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2013-0063 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:14:y:2015:i:2:p:113-123:n:1

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/sagmb-2013-0063

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().