Sequence complexity profiles of prokaryotic genomic sequences: A fast algorithm for calculating linguistic complexity

Olga G. Troyanskaya; Ora Arbell; Yair Koren; Gad M. Landau; Alexander Bolshoy

doi:10.1093/bioinformatics/18.5.679

Sequence complexity profiles of prokaryotic genomic sequences: A fast algorithm for calculating linguistic complexity

Olga G. Troyanskaya, Ora Arbell, Yair Koren, Gad M. Landau, Alexander Bolshoy

Research output: Contribution to journal › Article › peer-review

Abstract

Motivation: One of the major features of genomic DNA sequences, distinguishing them from texts in most spoken or artificial languages, is their high repetitiveness. Variation in the repetitiveness of genomic texts reflects the presence and density of different biologically important messages. Thus, deviation from an expected number of repeats in both directions indicates a possible presence of a biological signal. Linguistic complexity corresponds to repetitiveness of a genomic text, and potential regulatory sites may be discovered through construction of typical patterns of complexity distribution. Results: We developed software for fast calculation of linguistic sequence complexity of DNA sequences. Our program utilizes suffix trees to compute the number of subwords present in genomic sequences, thereby allowing calculation of linguistic complexity in time linear in genome size. The measure of linguistic complexity was applied to the complete genome of Haemophilus influenzae. Maps of complexity along the entire genome were obtained using sliding windows of 40, 100, and 2000 nucleotides. This approach provided an efficient way to detect simple sequence repeats in this genome. In addition, local profiles of complexity distribution around the starts of translation were constructed for 21 complete prokaryotic genomes. We hypothesize that complexity profiles correspond to evolutionary relationships between organisms. We found principal differences in profiles of the GC-rich and other (non-GC-rich) genomes. We also found characteristic differences in profiles of AT genomes, which probably reflect individual species variations in translational regulation.

Original language	English
Pages (from-to)	679-688
Number of pages	10
Journal	Bioinformatics
Volume	18
Issue number	5
DOIs	https://doi.org/10.1093/bioinformatics/18.5.679
State	Published - 2002

Bibliographical note

Funding Information:
We wish to thank Ed Trifonov and Valery Kirzhner for helpful discussions. We are grateful to Dr Larsson who permitted to use his code in our software. A.B. is a Guastaella Fellow, and is partially supported by the Fondation Sacta-Rachi, the Ancell Teicher Research Foundation for Molecular Genetics and Evolution, and by the FIRST Foundation of the Israel Academy of Science and Humanities. O.G.T. and O.A. are partially supported by the Israel Science Foundation, founded by the Israeli Academy of Sciences and Humanities. G.L. is partially supported by NSF grants CCR-9610238 and CCR-0104307, by NATO Science Programme grant PST.CLG.977017, by the Israel Science Foundation grants 173/98 and 282/01, by the FIRST Foundation of the Israel

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1093/bioinformatics/18.5.679

Cite this

@article{34d80b1e8b9e4fe48d9d6e4f98800927,

title = "Sequence complexity profiles of prokaryotic genomic sequences: A fast algorithm for calculating linguistic complexity",

abstract = "Motivation: One of the major features of genomic DNA sequences, distinguishing them from texts in most spoken or artificial languages, is their high repetitiveness. Variation in the repetitiveness of genomic texts reflects the presence and density of different biologically important messages. Thus, deviation from an expected number of repeats in both directions indicates a possible presence of a biological signal. Linguistic complexity corresponds to repetitiveness of a genomic text, and potential regulatory sites may be discovered through construction of typical patterns of complexity distribution. Results: We developed software for fast calculation of linguistic sequence complexity of DNA sequences. Our program utilizes suffix trees to compute the number of subwords present in genomic sequences, thereby allowing calculation of linguistic complexity in time linear in genome size. The measure of linguistic complexity was applied to the complete genome of Haemophilus influenzae. Maps of complexity along the entire genome were obtained using sliding windows of 40, 100, and 2000 nucleotides. This approach provided an efficient way to detect simple sequence repeats in this genome. In addition, local profiles of complexity distribution around the starts of translation were constructed for 21 complete prokaryotic genomes. We hypothesize that complexity profiles correspond to evolutionary relationships between organisms. We found principal differences in profiles of the GC-rich and other (non-GC-rich) genomes. We also found characteristic differences in profiles of AT genomes, which probably reflect individual species variations in translational regulation.",

author = "Troyanskaya, {Olga G.} and Ora Arbell and Yair Koren and Landau, {Gad M.} and Alexander Bolshoy",

note = "Funding Information: We wish to thank Ed Trifonov and Valery Kirzhner for helpful discussions. We are grateful to Dr Larsson who permitted to use his code in our software. A.B. is a Guastaella Fellow, and is partially supported by the Fondation Sacta-Rachi, the Ancell Teicher Research Foundation for Molecular Genetics and Evolution, and by the FIRST Foundation of the Israel Academy of Science and Humanities. O.G.T. and O.A. are partially supported by the Israel Science Foundation, founded by the Israeli Academy of Sciences and Humanities. G.L. is partially supported by NSF grants CCR-9610238 and CCR-0104307, by NATO Science Programme grant PST.CLG.977017, by the Israel Science Foundation grants 173/98 and 282/01, by the FIRST Foundation of the Israel",

year = "2002",

doi = "10.1093/bioinformatics/18.5.679",

language = "English",

volume = "18",

pages = "679--688",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "5",

}

TY - JOUR

T1 - Sequence complexity profiles of prokaryotic genomic sequences

T2 - A fast algorithm for calculating linguistic complexity

AU - Troyanskaya, Olga G.

AU - Arbell, Ora

AU - Koren, Yair

AU - Landau, Gad M.

AU - Bolshoy, Alexander

N1 - Funding Information: We wish to thank Ed Trifonov and Valery Kirzhner for helpful discussions. We are grateful to Dr Larsson who permitted to use his code in our software. A.B. is a Guastaella Fellow, and is partially supported by the Fondation Sacta-Rachi, the Ancell Teicher Research Foundation for Molecular Genetics and Evolution, and by the FIRST Foundation of the Israel Academy of Science and Humanities. O.G.T. and O.A. are partially supported by the Israel Science Foundation, founded by the Israeli Academy of Sciences and Humanities. G.L. is partially supported by NSF grants CCR-9610238 and CCR-0104307, by NATO Science Programme grant PST.CLG.977017, by the Israel Science Foundation grants 173/98 and 282/01, by the FIRST Foundation of the Israel

PY - 2002

Y1 - 2002

N2 - Motivation: One of the major features of genomic DNA sequences, distinguishing them from texts in most spoken or artificial languages, is their high repetitiveness. Variation in the repetitiveness of genomic texts reflects the presence and density of different biologically important messages. Thus, deviation from an expected number of repeats in both directions indicates a possible presence of a biological signal. Linguistic complexity corresponds to repetitiveness of a genomic text, and potential regulatory sites may be discovered through construction of typical patterns of complexity distribution. Results: We developed software for fast calculation of linguistic sequence complexity of DNA sequences. Our program utilizes suffix trees to compute the number of subwords present in genomic sequences, thereby allowing calculation of linguistic complexity in time linear in genome size. The measure of linguistic complexity was applied to the complete genome of Haemophilus influenzae. Maps of complexity along the entire genome were obtained using sliding windows of 40, 100, and 2000 nucleotides. This approach provided an efficient way to detect simple sequence repeats in this genome. In addition, local profiles of complexity distribution around the starts of translation were constructed for 21 complete prokaryotic genomes. We hypothesize that complexity profiles correspond to evolutionary relationships between organisms. We found principal differences in profiles of the GC-rich and other (non-GC-rich) genomes. We also found characteristic differences in profiles of AT genomes, which probably reflect individual species variations in translational regulation.

AB - Motivation: One of the major features of genomic DNA sequences, distinguishing them from texts in most spoken or artificial languages, is their high repetitiveness. Variation in the repetitiveness of genomic texts reflects the presence and density of different biologically important messages. Thus, deviation from an expected number of repeats in both directions indicates a possible presence of a biological signal. Linguistic complexity corresponds to repetitiveness of a genomic text, and potential regulatory sites may be discovered through construction of typical patterns of complexity distribution. Results: We developed software for fast calculation of linguistic sequence complexity of DNA sequences. Our program utilizes suffix trees to compute the number of subwords present in genomic sequences, thereby allowing calculation of linguistic complexity in time linear in genome size. The measure of linguistic complexity was applied to the complete genome of Haemophilus influenzae. Maps of complexity along the entire genome were obtained using sliding windows of 40, 100, and 2000 nucleotides. This approach provided an efficient way to detect simple sequence repeats in this genome. In addition, local profiles of complexity distribution around the starts of translation were constructed for 21 complete prokaryotic genomes. We hypothesize that complexity profiles correspond to evolutionary relationships between organisms. We found principal differences in profiles of the GC-rich and other (non-GC-rich) genomes. We also found characteristic differences in profiles of AT genomes, which probably reflect individual species variations in translational regulation.

UR - http://www.scopus.com/inward/record.url?scp=0036083579&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/18.5.679

DO - 10.1093/bioinformatics/18.5.679

M3 - Article

C2 - 12050064

AN - SCOPUS:0036083579

SN - 1367-4803

VL - 18

SP - 679

EP - 688

JO - Bioinformatics

JF - Bioinformatics

IS - 5

ER -

Sequence complexity profiles of prokaryotic genomic sequences: A fast algorithm for calculating linguistic complexity

Abstract

Bibliographical note

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this