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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Cloud-BS: A MapReduce-based bisulfite sequencing aligner on cloud
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Cloud-BS: A MapReduce-based bisulfite sequencing aligner on cloud

机译：Cloud-BS：云上的基于MapReduce的伯硫酸氢盐测序控制器

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摘要

In recent years, there have been many studies utilizing DNA methylome data to answer fundamental biological questions. Bisulfite sequencing (BS-seq) has enabled measurement of a genome-wide absolute level of DNA methylation at single-nucleotide resolution. However, due to the ambiguity introduced by bisulfite-treatment, the aligning process especially in large-scale epigenetic research is still considered a huge burden. We present Cloud-BS, an efficient BS-seq aligner designed for parallel execution on a distributed environment. Utilizing Apache Hadoop framework, Cloud-BS splits sequencing reads into multiple blocks and transfers them to distributed nodes. By designing each aligning procedure into separate map and reducing tasks while an internal key-value structure is optimized based on the MapReduce programming model, the algorithm significantly improves alignment performance without sacrificing mapping accuracy. In addition, Cloud-BS minimizes the innate burden of configuring a distributed environment by providing a pre-configured cloud image. Cloud-BS shows significantly improved bisulfite alignment performance compared to other existing BS-seq aligners. We believe our algorithm facilitates large-scale methylome data analysis. The algorithm is freely available at https://paryoja.github.io/Cloud-BS/.
机译：近年来，利用DNA甲基族数据有很多研究以回答基本的生物学问题。亚硫酸氢盐测序（BS-SEQ）能够在单核苷酸分辨率下测量DNA甲基化的基因组绝对水平。然而，由于亚硫酸氢盐处理引入的模糊性，尤其是大规模表观遗传研究的对准过程仍然被认为是一个巨大的负担。我们呈现云BS，一个有效的BS-SEQ对齐器，用于在分布式环境上并行执行。利用Apache Hadoop框架，Cloud-BS将排序读入多个块并将其传输到分布式节点。通过将每个对齐过程设计成单独的地图和减少任务时基于MapReduce编程模型优化内部键值结构，该算法显着提高了对准性能而不牺牲映射精度。此外，Cloud-BS通过提供预配置的云图像，最大限度地减少配置分布式环境的先天负担。与其他现有的BS-SEQ对齐器相比，云BS显示出明显改善的伯硫酸盐对准性能。我们认为我们的算法有助于大规模的甲虫数据分析。该算法在https://paryoja.github.io/cloud-bs/上自由使用。

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