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]
Genome Fusion Detection: A novel method to detect fusion genes from SNP-array data.
Bioinformatics. 2013 Jan 22;
Authors: Thieme S, Groth P
MOTIVATION: Fusion genes result from genomic rearrangements, such as deletions, amplifications, and translocations. Such rearrangements can also frequently be observed in cancer and have been postulated as driving event in cancer development. In order to detect them, one needs to analyze the transition region of two segments with different copy number, the location where fusions are known to occur. Finding fusion genes is essential to understanding cancer development and may lead to new therapeutic approaches. RESULTS: Here we present a novel method, the Genomic Fusion Detection algorithm, to predict fusion genes on a genomic level based on SNP-array data. This algorithm detects genes at the transition region of segments with copy number variation. With the application of defined constraints, certain properties of the detected genes are evaluated in order to predict whether they may be fused. We evaluated our prediction by calculating the observed frequency of known fusions in both primary cancers and cell lines. We tested a set of cell lines positive for the BCR-ABL1 fusion and prostate cancers positive for the TMPRSS2-ERG fusion. We could detect the fusions in all positive cell lines, but not in the negative controls. AVAILABILITY: The algorithm is available from the supplement CONTACT: email@example.com.
PMID: 23341502 [PubMed - as supplied by publisher]
Data Exploration, Quality Control and Testing in Single-Cell qPCR-Based Gene Expression Experiments.
Bioinformatics. 2012 Dec 24;
Authors: McDavid A, Finak G, Chattopadyay PK, Dominguez M, Lamoreaux L, Ma SS, Roederer M, Gottardo R
MOTIVATION: Cell populations are never truly homogeneous; individual cells exist in biochemical states that define functional differences between them. New technology based on microfluidic arrays combined with multiplexed quantitative polymerase chain reactions (qPCR) now enables high-throughput single-cell gene expression measurement, allowing assessment of cellular heterogeneity. However very little analytic tools have been developed specifically for the statistical and analytical challenges of single-cell qPCR data. RESULTS: We present a statistical framework for the exploration, quality control, and analysis of single-cell gene expression data from microfluidic arrays. We assess accuracy and within-sample heterogeneity of single-cell expression and develop quality control criteria to filter unreliable cell measurements. We propose a statistical model accounting for the fact that genes at the single-cell level can be on (and a continuous expression measure is recorded) or dichotomously off (and the recorded expression is zero). Based on this model, we derive a combined likelihood-ratio test for differential expression that incorporates both the discrete and continuous components. Using an experiment that examines treatment-specific changes in expression, we show that this combined test is more powerful than either the continuous or dichotomous component in isolation, or a t-test on the zero-inflated data. While developed for measurements from a specific platform (Fluidigm), these tools are generalizable to other multi-parametric measures over large numbers of events. AVAILABILITY: All results presented here were obtained using the SingleCellAssay R package available on GitHub (http://github.com/RGLab/SingleCellAssay). CONTACT: firstname.lastname@example.orgSupplementary Material: Supplementary data are available.
PMID: 23267174 [PubMed - as supplied by publisher]
PAIR: Paired Allelic log-Intensity-Ratio based normalization method for SNP-CGH arrays.
Bioinformatics. 2012 Nov 29;
Authors: Yang S, Pounds S, Zhang K, Fang Z
MOTIVATION: Normalization is critical in DNA copy number analysis. We propose a new method to correctly identify two-copy probes from the genome in order to obtain representative references for normalization in SNP arrays. The method is based on a two-state Hidden Markov Model. Unlike most currently available methods in the literature, the proposed method does not need to assume that the percentage of two-copy state probes is dominant in the genome, as long as there do exist two-copy probes. RESULTS: The real data analysis and simulation study show that the proposed algorithm is successful in that 1) it performs as well as the current methods (for example, CGHnormaliter and popLowess) for samples with dominant two-copy states and outperforms these methods for samples with less dominant two-copy states; 2) it can identify the Copy-neutral Loss of Heterozygosity; and 3) it is efficient in terms of the computational time used. AVAILABILITY: R scripts are available at http://publichealth.lsuhsc.edu/PAIR.html. CONTACT: email@example.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
PMID: 23196989 [PubMed - as supplied by publisher]
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