Publication detail

Advanced DNA fingerprint genotyping based on a model developed from real chip electrophoresis data

ŠKUTKOVÁ, H. VÍTEK, M. BEZDÍČEK, M. BRHELOVÁ, E. LENGEROVÁ, M.

Original Title

Advanced DNA fingerprint genotyping based on a model developed from real chip electrophoresis data

English Title

Advanced DNA fingerprint genotyping based on a model developed from real chip electrophoresis data

Type

journal article in Scopus

Language

en

Original Abstract

Large-scale comparative studies of DNA fingerprints prefer automated chip capillary electrophoresis over conventional gel planar electrophoresis due to the higher precision of the digitalization process. However, the determination of band sizes is still limited by the device resolution and sizing accuracy. Band matching, therefore, remains the key step in DNA fingerprint analysis. Most current methods evaluate only the pairwise similarity of the samples, using heuristically determined constant thresholds to evaluate the maximum allowed band size deviation; unfortunately, that approach significantly reduces the ability to distinguish between closely related samples. This study presents a new approach based on global multiple alignments of bands of all samples, with an adaptive threshold derived from the detailed migration analysis of a large number of real samples. The proposed approach allows the accurate automated analysis of DNA fingerprint similarities for extensive epidemiological studies of bacterial strains, thereby helping to prevent the spread of dangerous microbial infections.

English abstract

Large-scale comparative studies of DNA fingerprints prefer automated chip capillary electrophoresis over conventional gel planar electrophoresis due to the higher precision of the digitalization process. However, the determination of band sizes is still limited by the device resolution and sizing accuracy. Band matching, therefore, remains the key step in DNA fingerprint analysis. Most current methods evaluate only the pairwise similarity of the samples, using heuristically determined constant thresholds to evaluate the maximum allowed band size deviation; unfortunately, that approach significantly reduces the ability to distinguish between closely related samples. This study presents a new approach based on global multiple alignments of bands of all samples, with an adaptive threshold derived from the detailed migration analysis of a large number of real samples. The proposed approach allows the accurate automated analysis of DNA fingerprint similarities for extensive epidemiological studies of bacterial strains, thereby helping to prevent the spread of dangerous microbial infections.

Keywords

DNA fingerprintingautomated chip capillary electrophoresisgenotypingband matchinggel sample distortionpattern recognition

Released

25.01.2019

Publisher

Elsevier

Pages from

9

Pages to

18

Pages count

10

URL

BibTex


@article{BUT155605,
  author="Helena {Škutková} and Martin {Vítek} and Matěj {Bezdíček} and Eva {Brhelová} and Martina {Lengerová}",
  title="Advanced DNA fingerprint genotyping based on a model developed from real chip electrophoresis data",
  annote="Large-scale comparative studies of DNA fingerprints prefer automated chip capillary electrophoresis over conventional gel planar electrophoresis due to the higher precision of the digitalization process. However, the determination of band sizes is still limited by the device resolution and sizing accuracy. Band matching, therefore, remains the key step in DNA fingerprint analysis. Most current methods evaluate only the pairwise similarity of the samples, using heuristically determined constant thresholds to evaluate the maximum allowed band size deviation; unfortunately, that approach significantly reduces the ability to distinguish between closely related samples. This study presents a new approach based on global multiple alignments of bands of all samples, with an adaptive threshold derived from the detailed migration analysis of a large number of real samples. The proposed approach allows the accurate automated analysis of DNA fingerprint similarities for extensive epidemiological studies of bacterial strains, thereby helping to prevent the spread of dangerous microbial infections.",
  address="Elsevier",
  chapter="155605",
  doi="10.1016/j.jare.2019.01.005",
  howpublished="online",
  institution="Elsevier",
  number="18",
  volume="18",
  year="2019",
  month="january",
  pages="9--18",
  publisher="Elsevier",
  type="journal article in Scopus"
}