CloudATAC:基于云的 ATAC-Seq 数据分析框架。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Avinash M Veerappa, M Jordan Rowley, Angela Maggio, Laura Beaudry, Dale Hawkins, Allen Kim, Sahil Sethi, Paul L Sorgen, Chittibabu Guda
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引用次数: 0

摘要

利用高通量测序(ATAC-seq)检测转座酶可及染色质可生成全基因组染色质可及性图谱,为在集合细胞和单细胞群体水平上了解表观遗传基因调控提供了宝贵的信息。ATAC-seq 数据的综合分析需要使用各种相互依存的程序。学习处理数据所需的正确步骤顺序是一大障碍。在每个阶段(包括预分析、核心分析和高级下游分析)选择适当的参数对于确保准确分析和解读 ATAC-seq 数据非常重要。此外,获得有限的计算环境并在其中工作对非生物信息学研究人员来说也是一个巨大的挑战。因此,我们提出了云 ATAC,这是一个开源、基于云的交互式框架,具有可扩展、灵活、精简的分析框架,基于池细胞和单细胞 ATAC-seq 数据的最佳实践方法。这些框架使用按需计算能力和内存、可扩展性以及谷歌云提供的安全、合规的环境。此外,我们还利用 Jupyter Notebook 的交互式计算平台,该平台结合了实时代码、教程、叙述性文本、闪存卡、测验和自定义可视化,以加强学习和分析。此外,利用 GPU 实例大大提高了单细胞框架的运行时间。源代码和数据可通过美国国立卫生研究院云实验室 https://github.com/NIGMS/ATAC-Seq-and-Single-Cell-ATAC-Seq-Analysis 公开获取。本手稿介绍了资源模块的开发情况,该模块是名为 "NIGMS 云学习沙盒 "的学习平台的一部分,https://github.com/NIGMS/NIGMS-Sandbox。本补编开头的社论 "NIGMS 沙盒"[1] 介绍了沙盒的整体起源。该模块以交互式格式提供有关批量和单细胞 ATAC-seq 数据分析的学习材料,并使用适当的云资源进行数据访问和分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CloudATAC: a cloud-based framework for ATAC-Seq data analysis.

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) generates genome-wide chromatin accessibility profiles, providing valuable insights into epigenetic gene regulation at both pooled-cell and single-cell population levels. Comprehensive analysis of ATAC-seq data involves the use of various interdependent programs. Learning the correct sequence of steps needed to process the data can represent a major hurdle. Selecting appropriate parameters at each stage, including pre-analysis, core analysis, and advanced downstream analysis, is important to ensure accurate analysis and interpretation of ATAC-seq data. Additionally, obtaining and working within a limited computational environment presents a significant challenge to non-bioinformatic researchers. Therefore, we present Cloud ATAC, an open-source, cloud-based interactive framework with a scalable, flexible, and streamlined analysis framework based on the best practices approach for pooled-cell and single-cell ATAC-seq data. These frameworks use on-demand computational power and memory, scalability, and a secure and compliant environment provided by the Google Cloud. Additionally, we leverage Jupyter Notebook's interactive computing platform that combines live code, tutorials, narrative text, flashcards, quizzes, and custom visualizations to enhance learning and analysis. Further, leveraging GPU instances has significantly improved the run-time of the single-cell framework. The source codes and data are publicly available through NIH Cloud lab https://github.com/NIGMS/ATAC-Seq-and-Single-Cell-ATAC-Seq-Analysis. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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