碱基硫酸盐测序是一种用于分析基因组甲基化的强大工具，甲基化是一种对于癌症、精神障碍等多种疾病理解至关重要的表观遗传修饰。整个基因组甲基化测序（WGBS）生成的原始数据在进行统计分析之前需要经过多个计算步骤的处理，并且需要特别注意以及时和内存高效的方式处理数据。在WGBS工作流程中，参考基因组的比对是最耗计算资源的步骤之一，常用的WGBS特定比对软件可能需要几个小时甚至几天的时间。这自然催生了可以利用基于GPU的比对软件来大大加速瓶颈步骤的计算工作流程的创建。此外，WGBS生成的原始数据往往庞大而难以处理；现有流程中对于数据的内存有效表示的缺乏使得WGBS对许多研究人员来说不现实甚至不可能。 我们提出了BiocMAP，这是一个包含两个模块的Bioconductor友好的甲基化分析流程，旨在解决上述问题。第一个模块使用Arioc，一种GPU加速的短读比对器，执行计算密集型的读取比对。由于GPU并不总是在传统的基于CPU的分析便利的计算环境中可用，第二个模块可以在无GPU的环境中运行。该模块提取和合并DNA甲基化比例 - 样本中给定基因组位点所有细胞的甲基化胞嘧啶的比例。基于R的Bioconductor输出对象利用磁盘数据表示方式，大大减少所需的主内存，使得WGBS项目对更多研究人员而言计算可行。 BiocMAP使用Nextflow实现，可在http://research.libd.org/BiocMAP/获取。为了在各种典型计算环境下实现可重复分析，BiocMAP可以与Docker或Singularity容器化，并在本地或使用SLURM或SGE调度引擎上执行。通过提供Bioconductor对象，BiocMAP的输出可以与强大的开源分析软件集成，用于分析甲基化数据。© 2023年。 BioMed Central Ltd.，Springer Nature的一部分。

Bisulfite sequencing is a powerful tool for profiling genomic methylation, an epigenetic modification critical in the understanding of cancer, psychiatric disorders, and many other conditions. Raw data generated by whole genome bisulfite sequencing (WGBS) requires several computational steps before it is ready for statistical analysis, and particular care is required to process data in a timely and memory-efficient manner. Alignment to a reference genome is one of the most computationally demanding steps in a WGBS workflow, taking several hours or even days with commonly used WGBS-specific alignment software. This naturally motivates the creation of computational workflows that can utilize GPU-based alignment software to greatly speed up the bottleneck step. In addition, WGBS produces raw data that is large and often unwieldy; a lack of memory-efficient representation of data by existing pipelines renders WGBS impractical or impossible to many researchers.We present BiocMAP, a Bioconductor-friendly methylation analysis pipeline consisting of two modules, to address the above concerns. The first module performs computationally-intensive read alignment using Arioc, a GPU-accelerated short-read aligner. Since GPUs are not always available on the same computing environments where traditional CPU-based analyses are convenient, the second module may be run in a GPU-free environment. This module extracts and merges DNA methylation proportions-the fractions of methylated cytosines across all cells in a sample at a given genomic site. Bioconductor-based output objects in R utilize an on-disk data representation to drastically reduce required main memory and make WGBS projects computationally feasible to more researchers.BiocMAP is implemented using Nextflow and available at http://research.libd.org/BiocMAP/ . To enable reproducible analysis across a variety of typical computing environments, BiocMAP can be containerized with Docker or Singularity, and executed locally or with the SLURM or SGE scheduling engines. By providing Bioconductor objects, BiocMAP's output can be integrated with powerful analytical open source software for analyzing methylation data.© 2023. BioMed Central Ltd., part of Springer Nature.