Pyrosequencing for SNP Genotyping
Pyrosequencing is a real-time DNA sequencing method. It is based on the transformation of pyrophosphates, released during DNA elongation by DNA polymerase, into measurable light. During DNA elongation, a single pyrophosphate molecule is released following incorporation of a single nucleotide. In the pyrosequencing reaction, released pyrophosphates are then rapidly converted by sulfurylase to adenosine triphosphate, which in turn is utilized by luciferase to produce light. Within standardized conditions, this reaction is accomplished in a few milliseconds and the light produced can be registered with a CCD camera. Therefore, it becomes possible to quantitatively measure the nucleotides incorporated. This approach has been automated in different platforms and can be used for a wide variety of applications, such as single-nucleotide polymorphism (SNP) genotyping, DNA sequencing, loss of heterozygosity analysis, and CpG methylation studies. Here we describe the entire process, focusing our attention on SNP genotyping, and giving examples of some other applications.
Available from: Arndt Vogel
- "To evaluate allele-specific expression of MEG3 and DLK1 in primary HCC samples and HCC cell lines, polymorphisms at exonic regions showing parent-specific expression in MEG3 (Rs8013873)  and DLK1 (Rs1802710)  were analysed using pyrosequencing technology as described . In heterozygous samples, further SNP analysis of the corresponding cDNA was carried out. "
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ABSTRACT: Deregulation of imprinted genes is an important molecular mechanism contributing to the development of cancer in humans. However, knowledge about imprinting defects in human hepatocellular carcinoma (HCC), the third leading cause of cancer mortality worldwide, is still limited. Therefore, a systematic meta-analysis of the expression of 223 imprinted loci in human HCC was initiated. This screen revealed that the DLK1-MEG3 locus is frequently deregulated in HCC. Deregulation of DLK1 and MEG3 expression accompanied by extensive aberrations in DNA methylation could be confirmed experimentally in an independent series of human HCC (n = 40) in more than 80% of cases. Loss of methylation at the DLK1-MEG3 locus correlates linearly with global loss of DNA methylation in HCC (r(2) = 0.63, p<0.0001). Inhibition of DNMT1 in HCC cells using siRNA led to a reduction in MEG3-DMR methylation and concomitant increase in MEG3 RNA expression. Allele-specific expression analysis identified loss of imprinting in 10 out of 31 informative samples (32%), rendering it one of the most frequent molecular defects in human HCC. In 2 cases unequivocal gain of bi-allelic expression accompanied by substantial loss of methylation at the IG-DMR could be demonstrated. In 8 cases the tumour cells displayed allelic switching by mono-allelic expression of the normally imprinted allele. Allelic switching was accompanied by gains or losses of DNA methylation primarily at IG-DMR1. Analysis of 10 hepatocellular adenomas (HCA) and 5 cases of focal nodular hyperplasia (FNH) confirmed that this epigenetic instability is specifically associated with the process of malignant transformation and not linked to increased proliferation per se. This widespread imprint instability in human HCC has to be considered in order to minimize unwanted side-effects of therapeutic approaches targeting the DNA methylation machinery. It might also serve in the future as predictive biomarker and for monitoring response to epigenetic therapy.
PLoS ONE 11/2012; 7(11):e49462. DOI:10.1371/journal.pone.0049462 · 3.23 Impact Factor
Available from: Zaved Khan
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ABSTRACT: Autism is a complex neurodevelopmental disorder with a significant genetic component.The prevalence of autism has been increasing globally, though exact statistics for India are not available. Several genetic markers for autism have been studied. These include chromosomal abnormalities, copy number variations, submicroscopic cytogenetic anomalies, single nucleotide polymorphisms and other point mutations. This review gives
details on the current data available on these genetic markers of autism, with a focus on single nucleotide polymorphisms. Studies on SNPs within candidate genes on each chromosome are dealt with, including some details on which populations show which variation. Methodology involved in analysis of SNPs, i.e. techniques in SNP genotyping are also reviewed, focusing on those techniques that are simple and economically
feasible in the Indian scenario.
International Journal of Pharma and Bio Sciences 01/2013; 4(1):31-41.
Available from: Hsueh-Wei Chang
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ABSTRACT: Many drug or single nucleotide polymorphism (SNP)-related resources and tools have been developed, but connecting and integrating them is still a challenge. Here, we describe a user-friendly web-based software package, named Drug-SNPing, which provides a platform for the integration of drug information (DrugBank and PharmGKB), protein-protein interactions (STRING), tagSNP selection (HapMap) and genotyping information (dbSNP, REBASE and SNP500Cancer). DrugBank-based inputs include the following: (i) common name of the drug, (ii) synonym or drug brand name, (iii) gene name (HUGO) and (iv) keywords. PharmGKB-based inputs include the following: (i) gene name (HUGO), (ii) drug name and (iii) disease-related keywords. The output provides drug-related information, metabolizing enzymes and drug targets, as well as protein-protein interaction data. Importantly, tagSNPs of the selected genes are retrieved for genotyping analyses. All drug-based and protein-protein interaction-based SNP genotyping information are provided with PCR-RFLP (PCR-restriction enzyme length polymorphism) and TaqMan probes. Thus, users can enter any drug keywords/brand names to obtain immediate information that is highly relevant to genotyping for pharmacogenomics research.Availability and implementation: Drug-SNPing and its user manual are freely available at http://bio.kuas.edu.tw/drug-snping/. CONTACT: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org.
Bioinformatics 02/2013; 29(6). DOI:10.1093/bioinformatics/btt037 · 4.98 Impact Factor
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