J 2019

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

SKUTKOVA, Helena, Martin VITEK, Matěj BEZDÍČEK, Eva BRHELOVÁ, Martina LENGEROVÁ et. al.

Basic information

Original name

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

Authors

SKUTKOVA, Helena (203 Czech Republic, guarantor), Martin VITEK (203 Czech Republic), Matěj BEZDÍČEK (203 Czech Republic, belonging to the institution), Eva BRHELOVÁ (203 Czech Republic, belonging to the institution) and Martina LENGEROVÁ (203 Czech Republic, belonging to the institution)

Edition

JOURNAL OF ADVANCED RESEARCH, AMSTERDAM, ELSEVIER SCIENCE BV, 2019, 2090-1232

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

21100 2.11 Other engineering and technologies

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 6.992

RIV identification code

RIV/00216224:14110/19:00110574

Organization unit

Faculty of Medicine

UT WoS

000471218900002

Keywords in English

DNA fingerprinting; Automated chip capillary electrophores; Genotyping; Band matching; Gel sample distortion; Pattern recognition

Tags

Tags

International impact, Reviewed
Změněno: 4/9/2019 13:57, Mgr. Tereza Miškechová

Abstract

V originále

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. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of Cairo University.