谷歌云平台上的全基因组亚硫酸氢盐测序数据分析学习模块。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-07-23 DOI:10.1093/bib/bbae236
Yujia Qin, Angela Maggio, Dale Hawkins, Laura Beaudry, Allen Kim, Daniel Pan, Ting Gong, Yuanyuan Fu, Hua Yang, Youping Deng
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引用次数: 0

摘要

本研究介绍了一个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 https://github.com/NIGMS/NIGMS-Sandbox 的一部分。本增刊开头的社论 "NIGMS 沙盒 "介绍了沙盒的总体起源。该模块旨在促进全基因组亚硫酸氢盐测序(WGBS)数据分析的互动学习,利用谷歌云平台中基于云的工具,如云存储、顶点人工智能笔记本和谷歌批处理。WGBS 是一种功能强大的技术,可以全面了解单胞嘧啶分辨率的 DNA 甲基化模式,对于了解整个基因组的表观遗传调控至关重要。设计的学习模块首先提供循序渐进的教程,指导学习者完成 WGBS 数据分析的两个主要阶段,即预处理和识别差异甲基化区域。然后,它提供了一个简化的工作流程,并演示了如何利用云基础设施的强大功能有效地将其用于大型数据集。这些相互关联的子模块的整合逐步加深了用户对 WGBS 分析流程和云资源使用的理解。通过这个模块,我们可以提高云计算在表观基因组研究中的可及性和采用率,加快相关领域及其他领域的进步。本手稿介绍了一个资源模块的开发过程,该模块是名为 "NIGMS 云学习沙盒 "的学习平台 https://github.com/NIGMS/NIGMS-Sandbox 的一部分。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。
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Whole-genome bisulfite sequencing data analysis learning module on Google Cloud Platform.

This study 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 at the beginning of this Supplement. This module is designed to facilitate interactive learning of whole-genome bisulfite sequencing (WGBS) data analysis utilizing cloud-based tools in Google Cloud Platform, such as Cloud Storage, Vertex AI notebooks and Google Batch. WGBS is a powerful technique that can provide comprehensive insights into DNA methylation patterns at single cytosine resolution, essential for understanding epigenetic regulation across the genome. The designed learning module first provides step-by-step tutorials that guide learners through two main stages of WGBS data analysis, preprocessing and the identification of differentially methylated regions. And then, it provides a streamlined workflow and demonstrates how to effectively use it for large datasets given the power of cloud infrastructure. The integration of these interconnected submodules progressively deepens the user's understanding of the WGBS analysis process along with the use of cloud resources. Through this module, we can enhance the accessibility and adoption of cloud computing in epigenomic research, speeding up the advancements in the related field and beyond. 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.
期刊最新文献
TUnA: an uncertainty-aware transformer model for sequence-based protein-protein interaction prediction. scLEGA: an attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data. CatLearning: highly accurate gene expression prediction from histone mark. Detecting novel cell type in single-cell chromatin accessibility data via open-set domain adaptation. Explorer: efficient DNA coding by De Bruijn graph toward arbitrary local and global biochemical constraints.
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