Skutkova Helena, Vitek Martin, Bezdicek Matej, Brhelova Eva, Lengerova Martina
Department of Biomedical Engineering, Brno University of Technology, Technicka 12, 616 00 Brno, Czech Republic.
Department of Internal Medicine, Hematology and Oncology, Masaryk University and University Hospital Brno, Cernopolni 212/9, 662 63 Brno, Czech Republic.
J Adv Res. 2019 Jan 25;18:9-18. doi: 10.1016/j.jare.2019.01.005. eCollection 2019 Jul.
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.
由于数字化过程具有更高的精度，DNA指纹的大规模比较研究更倾向于使用自动化芯片毛细管电泳而非传统的凝胶平板电泳。然而，条带大小的测定仍然受到设备分辨率和尺寸准确性的限制。因此，条带匹配仍然是DNA指纹分析中的关键步骤。目前大多数方法仅评估样本之间的成对相似性，使用启发式确定的恒定阈值来评估允许的最大条带大小偏差；不幸的是，这种方法显著降低了区分密切相关样本的能力。本研究提出了一种基于所有样本条带全局多重比对的新方法，其自适应阈值源自对大量真实样本的详细迁移分析。所提出的方法能够对细菌菌株进行广泛的流行病学研究，准确地自动分析DNA指纹相似性，从而有助于防止危险微生物感染的传播。
