Drug-SNPing: an integrated drug-based, protein interaction-based tagSNP-based pharmacogenomics platform for SNP genotyping.
Bioinformatics. 2013 Feb 15;
Authors: Yang CH, Cheng YH, Chuang LY, Chang HW
SUMMARY: 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.
PMID: 23418190 [PubMed - as supplied by publisher]
Molecular Analysis of Spinal Muscular Atrophy: A genotyping protocol based on TaqMan(®) real-time PCR.
Genet Mol Biol. 2012 Dec;35(4 (suppl)):955-9
Authors: de Souza Godinho FM, Bock H, Gheno TC, Saraiva-Pereira ML
Spinal muscular atrophy (SMA) is an autosomal recessive inherited disorder caused by alterations in the survival motor neuron I (SMN1) gene. SMA patients are classified as type I-IV based on severity of symptoms and age of onset. About 95% of SMA cases are caused by the homozygous absence of SMN1 due to gene deletion or conversion into SMN2. PCR-based methods have been widely used in genetic testing for SMA. In this work, we introduce a new approach based on TaqMan(®)real-time PCR for research and diagnostic settings. DNA samples from 100 individuals with clinical signs and symptoms suggestive of SMA were analyzed. Mutant DNA samples as well as controls were confirmed by DNA sequencing. We detected 58 SMA cases (58.0%) by showing deletion of SMN1 exon 7. Considering clinical information available from 56 of them, the patient distribution was 26 (46.4%) SMA type I, 16 (28.6%) SMA type II and 14 (25.0%) SMA type III. Results generated by the new method was confirmed by PCR-RFLP and by DNA sequencing when required. In conclusion, a protocol based on real-time PCR was shown to be effective and specific for molecular analysis of SMA patients.
PMID: 23412967 [PubMed - in process]
BRAF gene polymorphism (rs10487888) assessment in chronic periodontitis and peri-implantitis in an Iranian population.
J Basic Clin Physiol Pharmacol. 2013 Feb 14;:1-5
Authors: Kadkhodazadeh M, Jafari AR, Khalighi HR, Ebadian AR, Vaziri S, Amid R
Abstract Background: Peri-implantitis (PI) and chronic periodontitis (CP) are multifactorial diseases of implant/tooth supporting tissue that are caused by bacterial infection and increased host immune response. T-cell proliferation plays a pivotal role in the orchestration of host response to bacterial infection. BRAF is a positive regulator of T-cell proliferation. The aim of this study was to evaluate for the first time the role of a functional single nucleotide polymorphism (SNP) of the BRAF gene in association to PI and CP. Methods: A total of 194 individuals referred to the Periodontology Department of Shahid Beheshti Dental School, Tehran, Iran, were divided into three groups: 74 patients in the CP group (39 men and 35 women, with mean age of 48.3 years), 38 patients in the PI group (20 men and 18 women, with mean age of 50.2 years), and 82 patients in the healthy periodontium group (39 men and 43 women, with mean age of 45.4 years). DNA was extracted from fresh blood samples collected from the arm vein of participants and was transferred to KBiosience institute (United Kingdom) for genotyping. χ2 and Kruskal-Wallis tests were conducted using SPSS software v. 19 for statistical analysis (p<0.05). Results: The allele (C/T) and genotype (CC, CT, TT) frequencies had insignificant differences among the three groups; however, the CC genotype was more prevalent in the healthy condition than in the disease conditions. Conclusions: The BRAF gene polymorphism (rs10487888) may not be a genetic determinant for increasing the risk of CP and PI among the Iranian population. More studies with more sample size in different populations are necessary for determining the effect of this SNP.
PMID: 23412871 [PubMed - as supplied by publisher]
Distinguishing somatic and germline copy number events in cancer patient DNA hybridized to whole-genome SNP genotyping arrays.
Methods Mol Biol. 2013;973:355-72
Authors: Ha G, Shah S
Chromosomal aneuploidy and segmental copy number changes are common genomic aberrations in -cancer. Copy number alterations (CNAs) arise from deletions, insertions, or duplications resulting in -chromosomal aberrations and aneuploidy. Genomes of normal cells also exhibit variable copy number called germline copy number variants (CNVs). CNVs in the general population tend to confound interpretation of predictions when attempting to extract relevant driver somatic events in cancer. In large studies of CNAs in cancer patients, it becomes necessary to accurately identify and separate CNAs and CNVs so as to prioritize candidate tumor suppressors and oncogenes. We have developed a probabilistic approach, HMM-Dosage, for segmenting and distinguishing CNAs and CNVs as separate, discrete events in cancer SNP genotyping array data. We outline the steps and computer code for the analysis of whole-genome cancer DNA hybridized to SNP genotyping arrays, focusing on distinguishing somatic CNA and germline CNVs, and describe the combined approach of HMM-Dosage for probabilistic inference and classification of somatic and germline copy number changes.
PMID: 23412801 [PubMed - in process]