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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Ehapp2: Estimate haplotype frequencies from pooled sequencing data with prior database information
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Ehapp2: Estimate haplotype frequencies from pooled sequencing data with prior database information

机译：ehapp2：根据合并的测序数据和先前的数据库信息估算单倍型频率

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摘要

To reduce the cost of large-scale re-sequencing, multiple individuals are pooled together and sequenced called pooled sequencing. Pooled sequencing could provide a cost-effective alternative to sequencing individuals separately. To facilitate the application of pooled sequencing in haplotype-based diseases association analysis, the critical procedure is to accurately estimate haplotype frequencies from pooled samples. Here we present Ehapp2 for estimating haplotype frequencies from pooled sequencing data by utilizing a database which provides prior information of known haplotypes. We first translate the problem of estimating frequency for each haplotype into finding a sparse solution for a system of linear equations, where the NNREG algorithm is employed to achieve the solution. Simulation experiments reveal that Ehapp2 is robust to sequencing errors and able to estimate the frequencies of haplotypes with less than 3% average relative difference for pooled sequencing of mixture of real Drosophila haplotypes with 50 x total coverage even when the sequencing error rate is as high as 0.05. Owing to the strategy that proportions for local haplotypes spanning multiple SNPs are accurately calculated first, Ehapp2 retains excellent estimation for recombinant haplotypes resulting from chromosomal crossover. Comparisons with present methods reveal that Ehapp2 is state-of-the-art for many sequencing study designs and more suitable for current massive parallel sequencing.
机译：为了降低大规模重测序的成本，将多个人汇集在一起​​并进行测序，称为汇集测序。合并测序可以提供一种经济有效的替代方法来对个体进行单独测序。为了促进合并测序在基于单体型的疾病关联分析中的应用，关键程序是从合并样品中准确估计单体型频率。在这里，我们介绍了Ehapp2，用于通过利用提供已知单倍型先验信息的数据库从合并的测序数据估算单倍型频率。我们首先将估计每个单元型的频率的问题转化为寻找线性方程组的稀疏解，其中使用NNREG算法来实现该解。仿真实验表明，Ehapp2对测序错误具有鲁棒性，并且即使对总果蝇的单倍型覆盖率为50倍，即使合并测序错误率高达10％，Ehapp2仍能够估计平均相对差异小于3％的单倍型频率。 0.05。由于首先要精确计算跨越多个SNP的局部单倍型的比例的策略，Ehapp2保留了对由染色体交叉产生的重组单倍型的出色估计。与现有方法的比较表明，Ehapp2是许多测序研究设计的最新技术，并且更适合当前的大规模并行测序。

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