Dataset Information

A simple method using PyrosequencingTM to identify de novo SNPs in pooled DNA samples.

ABSTRACT: A practical way to reduce the cost of surveying single-nucleotide polymorphism (SNP) in a large number of individuals is to measure the allele frequencies in pooled DNA samples. Pyrosequencing(TM) has been frequently used for this application because signals generated by this approach are proportional to the amount of DNA templates. The Pyrosequencing(TM) pyrogram is determined by the dispensing order of dNTPs, which is usually designed based on the known SNPs to avoid asynchronistic extensions of heterozygous sequences. Therefore, utilizing the pyrogram signals to identify de novo SNPs in DNA pools has never been undertook. Here, in this study we developed an algorithm to address this issue. With the sequence and pyrogram of the wild-type allele known in advance, we could use the pyrogram obtained from the pooled DNA sample to predict the sequence of the unknown mutant allele (de novo SNP) and estimate its allele frequency. Both computational simulation and experimental Pyrosequencing(TM) test results suggested that our method performs well. The web interface of our method is available at http://life.nctu.edu.tw/?yslin/PSM/.

SUBMITTER: Lin YS

PROVIDER: S-EPMC3061071 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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A simple method using PyrosequencingTM to identify de novo SNPs in pooled DNA samples.

Lin Yeong-Shin YS Liu Fu-Guo Robert FG Wang Tzi-Yuan TY Pan Cheng-Tsung CT Chang Wei-Ting WT Li Wen-Hsiung WH Li Wen-Hsiung WH

Nucleic acids research 20101203 5

A practical way to reduce the cost of surveying single-nucleotide polymorphism (SNP) in a large number of individuals is to measure the allele frequencies in pooled DNA samples. Pyrosequencing(TM) has been frequently used for this application because signals generated by this approach are proportional to the amount of DNA templates. The Pyrosequencing(TM) pyrogram is determined by the dispensing order of dNTPs, which is usually designed based on the known SNPs to avoid asynchronistic extensions ...[more]

PMID: 21131285

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Project description:Background: As costs decline, the size and scope of microarray experiments have increased. In multi-centre studies there is a need to ensure consistency of data pre-processing across centres. Similarly, in smaller scale studies the evolution of microarray platforms means that there is often a need to compare data generated on earlier microarrays to that generated on newer ones. It is important in such studies to ensure that platform-dependent biases are removed so that meta-analysis of different datasets can be performed reliably. In both these cases the optimal scenario is to have a small subset of samples repeated at each site or on each platform. These replicates can then be used to learn a relationship between probe intensities on the two platforms. Results: I introduce here a simple, linear-modelling-based method for normalizing data from multiple-platforms by using replicate hybridizations. A dataset of 20 rat liver samples is used as a benchmark. Eight samples are hybridized to two separate versions of Affymetrix microarrays, while the other 12 are hybridized to one, for a total of 28 arrays. Our linear modelling method removes platform bias as assessed using both unsupervised machine-learning and two-group statistical analyses. The method is computationally efficient and works well for data pre-processed by the GCRMA, RMA and MAS5 algorithms and using either default or alternative probe-mappings. The method is very stable towards the number of replicate samples used, with even two replicates greatly reducing platform-specific bias. Conclusions: A simple linear-modelling method can remove platform-specific bias independent of the pre-processing algorithm and ProbeSet-mapping used. This technique can readily be extended to multi-site experiments, and suggests the benefits of including a small number of replicate hybridizations in each new study as a normalization control. Twenty rats livers were processed, eight on both RAE230-A and RAE230-2 arrays, 8 on only RAE230-A arrays, and 4 on RAE230-2 arrays only.

Dataset Information

A simple method using PyrosequencingTM to identify de novo SNPs in pooled DNA samples.

Publications

A simple method using PyrosequencingTM to identify de novo SNPs in pooled DNA samples.

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