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Molecular Analysis of Plant DNA Genomes: Conserved and Diverged DNA Sequences
Published in S. K. Dutta, DNA Systematics, 2019
Although the higher plant genotypes are alike in complexity (i.e., formed by a similar number of genes), DNA hybridization reactions, particularly in the case of large “C-value” genomes, involve mainly those genome fractions whose functions have not been specified in full. Since transcriptionally active gene sequences in plant genomes comprise no more than a few percent of the entire nDNA,28,39–42 estimates necessary for genome-genotype comparisons are only approximations. The situation is even more complicated because some temporarily nonexpressed nDNA sequences (pseudogenes, for instance) may be involved in genotype evolution. Moreover, the matrix function may not be the only nDNA function in eukaryotes; a part of nDNA may have skeletal functions3 and be involved in the formation of supramolecular genotype structure and in chromosomal interactions.5 The interdependence between a plant “nucleotype” and morphological characters has received special scrutiny. In view of DNA hybridization method restrictions,30 nucleotide and amino acid sequence comparisons have become very popular.
Introduction
Published in Victor A. Bernstam, Pocket Guide to GENE LEVEL DIAGNOSTICS in Clinical Practice, 2019
Quantitation of DNA hybridization in routine clinical laboratories and even in field studies uses, for example, the slide immunoenzymatic assay (SIA-DNA). In this method, a cloned probe p17h8 (D17Z1) for the highly repetitive, primate-specific α satellite DNA quantitates minute amounts of human genomic DNA in forensic specimen extracts within less than 4 h with subnanogram sensitivity.
Rotavirus
Published in Dongyou Liu, Handbook of Foodborne Diseases, 2018
Lijuan Yuan, Tammy Bui, Ashwin Ramesh
Real-time reverse-transcription quantitative polymerase chain reaction is even more sensitive, capable of broad detection of various genotypes of RVA and discrimination of mixed RV infections.168–171 Oligonucleotide microarray hybridization technology combines the high sensitivity of RT-PCR with the selectivity of DNA-DNA hybridization.172 It is useful for RVA genotyping and also capable of discriminating mixed rotavirus infections from nonspecific cross-reactivity, which is the inherent shortcoming of traditional multiplex RT-PCR genotyping.173 Whole genome sequencing, especially with the next-generation sequencing technology, is able to further characterize the genome segment combinations in mixed rotavirus infections.174,175
Nucleic acid-based electrochemical biosensors for rapid clinical diagnosis: advances, challenges, and opportunities
Published in Critical Reviews in Clinical Laboratory Sciences, 2022
Abu Hashem, M. A. Motalib Hossain, Ab Rahman Marlinda, Mohammad Al Mamun, Suresh Sagadevan, Zohreh Shahnavaz, Khanom Simarani, Mohd Rafie Johan
Potentiometry is currently the main method used in clinical diagnostic applications. In a potentiometric biosensor, a biological recognition element is integrated into an EC transducer [63–65]. In response to the analyte, the detection element produces a biochemical signal which is transformed by the transducer to a measurable potential [66]. The signal is measured as the potential difference between the working electrode and the reference electrode, which relates to the amount of analyte present in the working electrode. An advantage of potentiometric biosensors is that a redox probe is not essential to assess the interaction between the target analyte and biorecognition element [67]. Additionally, such biosensors are very sensitive and selective in the presence of a stable and accurate reference electrode [68]. Goda et al. [69] reported on a PNA-based label-free potentiometric biosensor for detecting DNA hybridization. Although it is not based on NA, the glucose meter used for the quantification of blood glucose levels is a good example of an EC biosensor based on potentiometry. A potentiometric biosensor is described in Figure 4; details of a DNA-based multi-spotlight-addressable potentiometric sensor setup with layer structure and measuring setup are described [70].
Beyond the Usual Suspects: Expanding on Mutations and Detection for Familial Hypercholesterolemia
Published in Expert Review of Molecular Diagnostics, 2021
Shirin Ibrahim, Joep C. Defesche, John J.P. Kastelein
To date, different methods have been used for the analysis of underlying genetic defects in patients with an FH phenotype. One method is by using DNA hybridization assays, which only test for the presence of a limited number of specific known disease-causing mutations. This means that the reach of such a test is limited by the selected mutations and is fixed based on the knowledge at the time of design and manufacturing. These arrays are efficient, inexpensive, and require very little bioinformatics processing and interpretation. They typically contain the most common mutations detected within a specific geographical location. Consequently, if new FH variants are introduced or identified, the array design needs to be updated to decrease the chance of obtaining false-negative results. Furthermore, this method is not useful in the discovery of new mutations.
A 2020 update on the use of genetic testing for patients with primary immunodeficiency
Published in Expert Review of Clinical Immunology, 2020
Ivan K. Chinn, Jordan S. Orange
Two methods are commonly used for identification of CNVs. In array comparative genomic hybridization (CGH), patient DNA hybridization to a microarray of DNA oligonucleotide probes is compared to competitive reference DNA hybridization to the probes within the same assay. The probes are often designed specifically for the detection of CNVs. For single nucleotide polymorphism (SNP) arrays, the presence of common single base-pair changes across the genome is examined relative to controls also by means of microarray hybridization to probes. The best approach consists of a combination of both methods [15]. These techniques are still considered the gold standard for CNV testing compared to the use of bioinformatic tools. Factors that can affect the performance of the tests include probe design for array CGH and probe density for SNP arrays.