scholarly journals Comparison of Pyrosequencing, Sanger Sequencing, and Melting Curve Analysis for Detection of Low-Frequency Macrolide-Resistant Mycoplasma pneumoniae Quasispecies in Respiratory Specimens

2013 ◽  
Vol 51 (8) ◽  
pp. 2592-2598 ◽  
Author(s):  
K.-H. Chan ◽  
K. K. W. To ◽  
B. W. K. Chan ◽  
C. P. Y. Li ◽  
S. S. Chiu ◽  
...  
2019 ◽  
Vol 6 (2) ◽  
pp. 48-54
Author(s):  
L. A. Kesaeva ◽  
A. Yu. Bulanov ◽  
Yu. P. Finashutina ◽  
V. V. Tikhonova ◽  
O. N. Solopova ◽  
...  

Molecular genetic detection of CALR gene somatic mutations is required for myeloproliferative neoplasms diagnosis and treatment according to the novel WHO clinical recommendations. CALR mutations are found in approximately 25–35 % cases of essential thrombocythemia and primary myelofibrosis and they are associated with benign clinical outcome. In this study we have compared sensitivity and selectivity of seve ral different options of CALR mutation molecular genetic detection in blood samples of 379 CMD patients and 17 healthy donors. Among methods compared in our study there have been conventional polymerase chain reaction with electrophoretic detection, real-time quantitative polymerase chain reaction, direct Sanger sequencing of polymerase chain reaction fragments and polymerase chain reaction high resolution melting curve analysis. By means of melting curve analysis CALR mutations have been found in 97 (25.5 %) patients, whereas in the cases of Sanger sequencing and polymerase chain reaction there have been 87 (23.0 %) and 84 (22.1 %) CALR mutation positive patients respectively.


Author(s):  
Qiuying Huang ◽  
Xudong Wang ◽  
Ning Tang ◽  
Chunjiang Zhu ◽  
Tizhen Yan ◽  
...  

AbstractThe assay consisted of one pair of primers specific for theThe assay had a reproducibility of 100%, could detect gDNA of different genotype as low as 1 ng per reaction, and had an overall accuracy of 100% when compared with RDB analysis and Sanger sequencing.The developed assay is rapid, robust, and cost-effective while maintaining high sensitivity, specificity, and throughput.


2010 ◽  
Vol 12 (4) ◽  
pp. 425-432 ◽  
Author(s):  
Athanasios C. Tsiatis ◽  
Alexis Norris-Kirby ◽  
Roy G. Rich ◽  
Michael J. Hafez ◽  
Christopher D. Gocke ◽  
...  

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Qunfeng Zhang ◽  
Yiqiao Du ◽  
Xinju Zhang ◽  
Zhihua Kang ◽  
Ming Guan ◽  
...  

Background. KRAS genotyping in tumor samples is a decisive clinical test for the anti-EGFR therapy management. However, the complexity of KRAS mutation landscape across different cancer types and the mosaic effect caused by cancer cellularity and heterogeneity make the choice of KRAS genotyping method a challenging topic in the clinical practice. Methods. We depicted the landscape of somatic KRAS mutation in 7,844 primary tumors and 10,336 metastatic tumors across over 30 types of cancer using the Cancer Genome Atlas (TCGA) and Integrated Mutation Profiling of Actionable Cancer Targets (MSKCC-IMPACT) databases, respectively. A snapback primer assay based on melting curve analysis was developed to detect the most common somatic mutations in KRAS codons 12 and 13. The sensitivity and accuracy of the method was validated by genotyping 100 colorectal cancer (CRC) samples, in comparison with Sanger sequencing and T-A cloning sequencing. Results. Pancreas adenocarcinoma (somatic mutation frequency 90.6%), colorectal adenocarcinoma (42.5%), and lung adenocarcinoma (32.6%) are the top three most KRAS mutant primary cancer types. The metastatic tumors showed a higher prevalence (90.99% versus 66.31%) and diversity of KRAS mutation compared with the primary tumors. Mutations in codons 12 and 13 are the predominant genetic alteration in KRAS (84.15% for TCGA and 86.13% for MSK-IMPACT). Moreover, KRAS mutation is highly correlated with the overall survival of patients with metastatic cancer. The snapback primer assay showed a more favorable performance in enriching and detecting the KRAS codon 12 and 13 mutation (1% mutation load) compared with Sanger sequencing (20% mutation load and 7% false-negative rate). Conclusions. KRAS mutation pattern is highly diverse among different cancer types and is associated with the survival of patients with metastatic cancers. The snapback primer assay is a reliable, sensitive method to detect the major mutant KRAS alleles, which might facilitate the effective cancer treatment decisions.


2005 ◽  
Vol 43 (2) ◽  
pp. 301-310 ◽  
Author(s):  
Kijeong Kim ◽  
Juwon Seo ◽  
Katherine Wheeler ◽  
Chulmin Park ◽  
Daewhan Kim ◽  
...  

2006 ◽  
Vol 52 (12) ◽  
pp. 2236-2242 ◽  
Author(s):  
Melissa R Snyder ◽  
Jerry A Katzmann ◽  
Malinda L Butz ◽  
Ping Yang ◽  
D Brian Dawson ◽  
...  

Abstract Background: Laboratory testing in suspected α-1-antitrypsin (A1AT) deficiency involves analysis of A1AT concentrations and identification of specific alleles by genotyping or phenotyping. The purpose of this study was to define and evaluate a strategy that provides reliable laboratory evaluation of A1AT deficiency. Methods: Samples from 512 individuals referred for A1AT phenotype analysis were analyzed by quantification, phenotype, and genotype. A1AT concentrations were measured by nephelometry. Phenotype analysis was performed by isoelectric focusing electrophoresis. The genotype assay detected the S and Z deficiency alleles by a melting curve analysis. Results: Of the 512 samples analyzed, 2% of the phenotype and genotype results were discordant. Among these 10 discordant results, 7 were attributed to phenotyping errors. On the basis of these data we formulated an algorithm, according to which we analyzed samples by genotyping and quantification assays, with a reflex to phenotyping when the genotype and quantification results were not concordant. Retrospective analyses demonstrated that 4% of samples submitted for genotype and quantitative analysis were reflexed to phenotyping. Of the reflexed samples, phenotyping confirmed the genotype result in 85% of cases. In the remaining 15%, phenotyping provided further information, including identifying rare deficiency alleles and suggesting the presence of a null allele, and allowed for a more definitive interpretation of the genotype result. Conclusions: The combination of genotyping and quantification, with a reflex to phenotyping, is the optimal strategy for the laboratory evaluation of A1AT deficiency.


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