Publication detail

Word entropy-based approach to detect highly variable genetic markers for bacterial genotyping

NYKRÝNOVÁ, M. BARTOŇ, V. SEDLÁŘ, K. BEZDÍČEK, M. LENGEROVÁ, M. ŠKUTKOVÁ, H.

Original Title

Word entropy-based approach to detect highly variable genetic markers for bacterial genotyping

English Title

Word entropy-based approach to detect highly variable genetic markers for bacterial genotyping

Type

journal article in Web of Science

Language

en

Original Abstract

Genotyping methods are used to distinguish bacterial strains from one species. Thus, distinguishing bacterial strains on a global scale, between countries or local districts in one country is possible. However, the highly selected bacterial populations (e.g. local populations in hospital) are typically closely related and low diversified. Therefore, currently used typing methods are not able to distinguish individual strains from each other. Here, we present a novel pipeline to detect highly variable genetic segments for genotyping a closely related bacterial population. The method is based on a degree of disorder in analyzed sequences that can be represented by sequence entropy. With the identified variable sequences, it is possible to find out transmission routes and sources of highly virulent and multiresistant strains. The proposed method can be used for any bacterial population, and due to its whole genome range, also noncoding regions are examined.

English abstract

Genotyping methods are used to distinguish bacterial strains from one species. Thus, distinguishing bacterial strains on a global scale, between countries or local districts in one country is possible. However, the highly selected bacterial populations (e.g. local populations in hospital) are typically closely related and low diversified. Therefore, currently used typing methods are not able to distinguish individual strains from each other. Here, we present a novel pipeline to detect highly variable genetic segments for genotyping a closely related bacterial population. The method is based on a degree of disorder in analyzed sequences that can be represented by sequence entropy. With the identified variable sequences, it is possible to find out transmission routes and sources of highly virulent and multiresistant strains. The proposed method can be used for any bacterial population, and due to its whole genome range, also noncoding regions are examined.

Keywords

genotyping; entropy; genetic markers; closely related bacteria; MLST

Released

03.02.2021

Publisher

Frontiers Media SA

ISBN

1664-302X

Periodical

Frontiers in Microbiology

Year of study

12

Number

1

State

CH

Pages from

1

Pages to

8

Pages count

8

URL

Full text in the Digital Library

Documents

BibTex


@article{BUT167886,
  author="Markéta {Nykrýnová} and Vojtěch {Bartoň} and Karel {Sedlář} and Matěj {Bezdíček} and Martina {Lengerová} and Helena {Škutková}",
  title="Word entropy-based approach to detect highly variable genetic markers for bacterial genotyping",
  annote="Genotyping methods are used to distinguish bacterial strains from one species. Thus, distinguishing bacterial strains on a global scale, between countries or local districts in one country is possible. However, the highly selected bacterial populations (e.g. local populations in hospital) are typically closely related and low diversified. Therefore, currently used typing methods are not able to distinguish individual strains from each other. Here, we present a novel pipeline to detect highly variable genetic segments for genotyping a closely related bacterial population. The method is based on a degree of disorder in analyzed sequences that can be represented by sequence entropy. With the identified variable sequences, it is possible to find out transmission routes and sources of highly virulent and multiresistant strains. The proposed method can be used for any bacterial population, and due to its whole genome range, also noncoding regions are examined.",
  address="Frontiers Media SA",
  chapter="167886",
  doi="10.3389/fmicb.2021.631605",
  howpublished="online",
  institution="Frontiers Media SA",
  number="1",
  volume="12",
  year="2021",
  month="february",
  pages="1--8",
  publisher="Frontiers Media SA",
  type="journal article in Web of Science"
}