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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/.