Understanding proteome quantification in an interactive learning module on Google Cloud Platform.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-07-23 DOI:10.1093/bib/bbae235
Kyle A O'Connell, Benjamin Kopchick, Thad Carlson, David Belardo, Stephanie D Byrum
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Abstract

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 at the beginning of this Supplement. This module delivers learning materials on protein quantification in an interactive format that uses appropriate cloud resources for data access and analyses. Quantitative proteomics is a rapidly growing discipline due to the cutting-edge technologies of high resolution mass spectrometry. There are many data types to consider for proteome quantification including data dependent acquisition, data independent acquisition, multiplexing with Tandem Mass Tag reporter ions, spectral counts, and more. As part of the NIH NIGMS Sandbox effort, we developed a learning module to introduce students to mass spectrometry terminology, normalization methods, statistical designs, and basics of R programming. By utilizing the Google Cloud environment, the learning module is easily accessible without the need for complex installation procedures. The proteome quantification module demonstrates the analysis using a provided TMT10plex data set using MS3 reporter ion intensity quantitative values in a Jupyter notebook with an R kernel. The learning module begins with the raw intensities, performs normalization, and differential abundance analysis using limma models, and is designed for researchers with a basic understanding of mass spectrometry and R programming language. Learners walk away with a better understanding of how to navigate Google Cloud Platform for proteomic research, and with the basics of mass spectrometry data analysis at the command line. 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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在谷歌云平台的互动学习模块中了解蛋白质组定量。
本手稿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。该模块以互动形式提供有关蛋白质定量的学习材料,并使用适当的云资源进行数据访问和分析。由于高分辨率质谱仪的尖端技术,定量蛋白质组学是一门快速发展的学科。蛋白质组定量分析需要考虑许多数据类型,包括依赖数据的采集、独立数据的采集、使用串联质谱标签报告离子的多路复用、光谱计数等。作为美国国立卫生研究院 NIGMS 沙盒项目的一部分,我们开发了一个学习模块,向学生介绍质谱术语、归一化方法、统计设计和 R 编程基础。通过利用谷歌云环境,该学习模块无需复杂的安装程序即可轻松访问。蛋白质组定量模块演示了如何使用提供的 TMT10plex 数据集,在带有 R 内核的 Jupyter 笔记本中使用 MS3 报告离子强度定量值进行分析。该学习模块从原始强度开始,使用 limma 模型执行归一化和差异丰度分析,专为对质谱和 R 编程语言有基本了解的研究人员设计。通过学习,学习者可以更好地了解如何利用谷歌云平台开展蛋白质组学研究,并掌握命令行质谱数据分析的基础知识。本手稿介绍了一个资源模块的开发过程,该模块是名为 "NIGMS 基于云的学习沙盒 "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》[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。
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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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