Cloud-based introduction to BASH programming for biologists.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-07-23 DOI:10.1093/bib/bbae244
Owen M Wilkins, Ross Campbell, Zelaikha Yosufzai, Valena Doe, Shannon M Soucy
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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 authored by National Institute of General Medical Sciences: NIGMS Sandbox: A Learning Platform toward Democratizing Cloud Computing for Biomedical Research at the beginning of this supplement. This module delivers learning materials introducing the utility of the BASH (Bourne Again Shell) programming language for genomic data analysis in an interactive format that uses appropriate cloud resources for data access and analyses. The next-generation sequencing revolution has generated massive amounts of novel biological data from a multitude of platforms that survey an ever-growing list of genomic modalities. These data require significant downstream computational and statistical analyses to glean meaningful biological insights. However, the skill sets required to generate these data are vastly different from the skills required to analyze these data. Bench scientists that generate next-generation data often lack the training required to perform analysis of these datasets and require support from bioinformatics specialists. Dedicated computational training is required to empower biologists in the area of genomic data analysis, however, learning to efficiently leverage a command line interface is a significant barrier in learning how to leverage common analytical tools. Cloud platforms have the potential to democratize access to the technical tools and computational resources necessary to work with modern sequencing data, providing an effective framework for bioinformatics education. This module aims to provide an interactive platform that slowly builds technical skills and knowledge needed to interact with genomics data on the command line in the Cloud. The sandbox format of this module enables users to move through the material at their own pace and test their grasp of the material with knowledge self-checks before building on that material in the next sub-module. 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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基于云的生物学家 BASH 编程入门。
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。国家普通医学研究所》撰写的社论介绍了沙盒的总体起源:NIGMS Sandbox:本补编开头的 "NIGMS 沙盒:生物医学研究云计算民主化的学习平台 "中介绍了沙盒的总体起源。该模块提供的学习材料介绍了 BASH(Bourne Again Shell)编程语言在基因组数据分析中的实用性,该语言采用交互式格式,使用适当的云资源进行数据访问和分析。新一代测序革命产生了海量的新型生物数据,这些数据来自多个平台,调查的基因组模式不断增加。这些数据需要进行大量的下游计算和统计分析,才能获得有意义的生物学见解。然而,生成这些数据所需的技能与分析这些数据所需的技能大相径庭。生成下一代数据的基层科学家往往缺乏对这些数据集进行分析所需的培训,需要生物信息学专家的支持。要提高生物学家在基因组数据分析领域的能力,需要专门的计算培训,然而,学习如何有效利用命令行界面是学习如何利用常用分析工具的一大障碍。云平台有可能使获取现代测序数据所需的技术工具和计算资源的途径民主化,从而为生物信息学教育提供一个有效的框架。本模块旨在提供一个互动平台,慢慢培养在云平台上通过命令行与基因组学数据交互所需的技术技能和知识。本模块的沙盒形式使用户能够按照自己的节奏学习材料,并在下一个子模块中继续学习材料之前,通过知识自检测试自己对材料的掌握程度。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。
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
Atomistic simulations reveal impacts of missense mutations on the structure and function of SynGAP1. COFFEE: consensus single cell-type specific inference for gene regulatory networks. DrugDoctor: enhancing drug recommendation in cold-start scenario via visit-level representation learning and training. 3t-seq: automatic gene expression analysis of single-copy genes, transposable elements, and tRNAs from RNA-seq data. AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease.
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