Detailed Information on Publication Record
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
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.