Simulating multiplexed SNP discovery rates using base-specific cleavage and mass spectrometry

Correlation between thyroglobulin gene polymorphisms and autoimmune thyroid disease

The aim of the present study was to detect thyroglobulin (Tg) gene polymorphisms in a Han Chinese population from the Northern regions of Henan province, China, and to study the correlation between Tg gene polymorphisms and autoimmune thyroid disease (AITD). A total of 270 patients with AITD and 135 healthy controls were enrolled. Genomic DNA was extracted and fluorescence polymerase chain reaction analysis was performed; high‑resolution melting curve analysis (HRMA) was used to detect single‑nucleotide polymorphisms (SNPs) in exons 10, 12 and 33 of the Tg gene. SNPs were then correlated with AITD. Han people from the Northern regions of Henan displayed four Tg exon SNPs: E10SNP24 T/G, E10SNP158 T/C, E12SNP A/G and E33SNP C/T. Several allele and genotype frequencies differed between the AITD group and the healthy control group (Tg E10SNP: Allele T, P<0.01; allele G, P<0.01; and Tg genotype GG, P<0.01; genotype TG, P<0.01. Tg E12SNP: Allele A, P<0.01; allele G, P<0.01; Tg genotype GG, P<0.01; genotype AG, P<0.01). A statistically significant difference in the frequency of selected Tg SNPs haplotypes was also present between AITD patients and healthy controls (P<0.05). There was no significant difference in haplotypes between various types of AITD (hypothyroidism, hyperthyroidism and Hashimoto's disease). The Tg SNP frequency distribution was significantly different between Han populations of the Northern regions of Henan province and the Xi'an regions of Shaanxi province. The results of the present study suggested that specific Tg gene alleles or genotypes were correlated with AITD; specific Tg SNP haplotypes were associated with hypothyroidism, hyperthyroidism and Hashimoto's disease, and the Tg SNP frequency distribution differed depending on the geographical location of the Han Chinese populations.

Computational mass spectrometry for small molecules

: The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline.

Is high resolution melting analysis (HRMA) accurate for detection of human disease-associated mutations? A meta analysis

BACKGROUND

High Resolution Melting Analysis (HRMA) is becoming the preferred method for mutation detection. However, its accuracy in the individual clinical diagnostic setting is variable. To assess the diagnostic accuracy of HRMA for human mutations in comparison to DNA sequencing in different routine clinical settings, we have conducted a meta-analysis of published reports.

METHODOLOGY/PRINCIPAL FINDINGS

Out of 195 publications obtained from the initial search criteria, thirty-four studies assessing the accuracy of HRMA were included in the meta-analysis. We found that HRMA was a highly sensitive test for detecting disease-associated mutations in humans. Overall, the summary sensitivity was 97.5% (95% confidence interval (CI): 96.8-98.5; I(2) = 27.0%). Subgroup analysis showed even higher sensitivity for non-HR-1 instruments (sensitivity 98.7% (95%CI: 97.7-99.3; I(2) = 0.0%)) and an eligible sample size subgroup (sensitivity 99.3% (95%CI: 98.1-99.8; I(2) = 0.0%)). HRMA specificity showed considerable heterogeneity between studies. Sensitivity of the techniques was influenced by sample size and instrument type but by not sample source or dye type.

CONCLUSIONS/SIGNIFICANCE

These findings show that HRMA is a highly sensitive, simple and low-cost test to detect human disease-associated mutations, especially for samples with mutations of low incidence. The burden on DNA sequencing could be significantly reduced by the implementation of HRMA, but it should be recognized that its sensitivity varies according to the number of samples with/without mutations, and positive results require DNA sequencing for confirmation.

Mass spectrometry tools for the classification and identification of bacteria

Mass spectrometry has become an important analytical tool in biology in the past two decades. In principle, mass spectrometry offers high-throughput, sensitive and specific analysis for many applications in microbiology, including clinical diagnostics and environmental research. Recently, several mass spectrometry methods for the classification and identification of bacteria and other microorganisms, as well as new software analysis tools, have been developed. In this Review we discuss the application range of these mass spectrometry procedures and their potential for successful transfer into microbiology laboratories.

High-resolution DNA melting analysis for simple and efficient molecular diagnostics

High-resolution melting of DNA is a simple solution for genotyping, mutation scanning and sequence matching. The melting profile of a PCR product depends on its GC content, length, sequence and heterozygosity and is best monitored with saturating dyes that fluoresce in the presence of double-stranded DNA. Genotyping of most variants is possible by the melting temperature of the PCR products, while all variants can be genotyped with unlabeled probes. Mutation scanning and sequence matching depend on sequence differences that result in heteroduplexes that change the shape of the melting curve. High-resolution DNA melting has several advantages over other genotyping and scanning methods, including an inexpensive closed tube format that is homogenous, accurate and rapid. Owing to its simplicity and speed, the method is a good fit for personalized medicine as a rapid, inexpensive method to predict therapeutic response.