BACKGROUND Next-generation sequencing allows genome-wide analysis of changes in chromatin states and gene expression. Data analysis of these increasingly used methods either requires multiple analysis steps, or extensive computational time. We sought to develop a tool for rapid quantification of sequencing peaks from diverse experimental sources and an efficient method to produce coverage tracks for accurate visualization that can be intuitively displayed and interpreted by experimentalists with minimal bioinformatics background. We demonstrate its strength and usability by integrating data from several types of sequencing approaches. RESULTS We have developed BAMscale, a one-step tool that processes a wide set of sequencing datasets. To demonstrate the usefulness of BAMscale, we analyzed multiple sequencing datasets from chromatin immunoprecipitation sequencing data (ChIP-seq), chromatin state change data (assay for transposase-accessible chromatin using sequencing: ATAC-seq, DNA double-strand break mapping sequencing: END-seq), DNA replication data (Okazaki fragments sequencing: OK-seq, nascent-strand sequencing: NS-seq, single-cell replication timing sequencing: scRepli-seq) and RNA-seq data. The outputs consist of raw and normalized peak scores (multiple normalizations) in text format and scaled bigWig coverage tracks that are directly accessible to data visualization programs. BAMScale also includes a visualization module facilitating direct, on-demand quantitative peak comparisons that can be used by experimentalists. Our tool can effectively analyze large sequencing datasets (~ 100 Gb size) in minutes, outperforming currently available tools. CONCLUSIONS BAMscale accurately quantifies and normalizes identified peaks directly from BAM files, and creates coverage tracks for visualization in genome browsers. BAMScale can be implemented for a wide set of methods for calculating coverage tracks, including ChIP-seq and ATAC-seq, as well as methods that currently require specialized, separate tools for analyses, such as splice-aware RNA-seq, END-seq and OK-seq for which no dedicated software is available. BAMscale is freely available on github (https://github.com/ncbi/BAMscale).

中文翻译：

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BAMscale：量化下一代测序峰并生成扩展的覆盖范围。

背景技术下一代测序允许染色质状态和基因表达变化的全基因组分析。这些越来越常用的方法的数据分析要么需要多个分析步骤，要么需要大量的计算时间。我们寻求开发一种工具，用于对来自各种实验来源的测序峰进行快速定量，以及一种有效的方法来生成覆盖轨迹，以进行准确的可视化，可以由实验人员以最少的生物信息学背景直观地显示和解释。我们通过整合几种测序方法的数据来证明其强度和可用性。结果我们开发了BAMscale，这是一个一步工具，可以处理各种测序数据集。为了证明BAMscale的有用性，我们从染色质免疫沉淀测序数据（ChIP-seq），染色质状态变化数据（使用测序测定转座酶可及的染色质的方法：ATAC-seq，DNA双链断裂定位测序：END-seq），DNA复制数据中分析了多个测序数据集（Okazaki片段测序：OK-seq，新生链测序：NS-seq，单细胞复制计时序列：scRepli-seq）和RNA-seq数据。输出包括文本格式的原始峰和归一化峰得分（多次归一化），以及可通过数据可视化程序直接访问的缩放过的bigWig覆盖范围。BAMScale还包括一个可视化模块，可帮助按需进行直接，按需的定量峰比较，供实验人员使用。我们的工具可以在几分钟内有效地分析大型测序数据集（〜100 Gb大小），胜过当前可用的工具。结论BAMscale直接从BAM文件中准确量化和归一化鉴定出的峰，并创建覆盖轨迹以在基因组浏览器中可视化。BAMScale可以用于多种计算覆盖范围的方法，包括ChIP-seq和ATAC-seq，以及目前需要专门的单独工具进行分析的方法，例如剪接感知RNA-seq，END-seq。和没有专用软件的OK-seq。BAMscale可在github（https://github.com/ncbi/BAMscale）上免费获得。BAMScale可以用于多种计算覆盖范围的方法，包括ChIP-seq和ATAC-seq，以及目前需要专门的单独工具进行分析的方法，例如剪接感知RNA-seq，END-seq。和没有专用软件的OK-seq。BAMscale可在github（https://github.com/ncbi/BAMscale）上免费获得。BAMScale可以用于多种计算覆盖范围的方法，包括ChIP-seq和ATAC-seq，以及目前需要专门的单独工具进行分析的方法，例如剪接感知RNA-seq，END-seq。和没有专用软件的OK-seq。BAMscale可在github（https://github.com/ncbi/BAMscale）上免费获得。