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Data_Sheet_3_Learning from feces: a case study teaching microbiome analysis.docx

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NIAID Data Ecosystem2026-05-02 收录
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https://figshare.com/articles/dataset/Data_Sheet_3_Learning_from_feces_a_case_study_teaching_microbiome_analysis_docx/28760918
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资源简介:
Microbiome data is increasingly important in health and environmental research. This data can be used to teach students both microbiology content as well as bioinformatics competencies. A narrative case study, Learning from Feces: the Effects of Antibiotics on Gut Microbiomes, was developed where publicly available human gut microbiome data from infants treated with antibiotics is analyzed using the student-accessible, user-friendly DNA Subway Purple line. Students, through this case study, learn about microbiome analysis including how to choose samples and process sequence data through a pipeline and finally interpret and explain the biological relevance of this data. This case study was successfully implemented in Microbiology courses for both majors and non-majors as a multi-step module on microbiomes. This manuscript presents the case development process that includes undergraduate authors, learning objectives, teaching materials and the results of implementation. Reflections on the implementation in two different courses are shared with notes for future users. It is hoped that this case will be effective in student learning and helpful for other instructors, especially those with limited bioinformatics training and the time and financial resources required for a full wet lab.

微生物组(Microbiome)数据在健康与环境研究中的重要性与日俱增。此类数据可用于向学生传授微生物学知识与生物信息学(bioinformatics)实操技能。本研究开发了一则名为《从粪便中学习:抗生素对肠道微生物组的影响》(Learning from Feces: the Effects of Antibiotics on Gut Microbiomes)的叙事性案例研究,该案例依托公开获取的经抗生素处理的婴儿人类肠道微生物组数据,借助面向学生开放、操作友好的DNA Subway紫色分析管线(DNA Subway Purple line)开展分析。学生通过本案例可学习微生物组分析相关技能,包括如何筛选样本、通过分析流程处理测序数据,最终解读并阐释该数据的生物学相关性。本案例已作为微生物组相关的多步骤教学模块,成功应用于微生物学专业学生与非专业学生的课程教学中。本文详述了该案例的开发流程,涵盖本科作者、教学目标、教学材料以及实施效果等内容。此外,本文还分享了在两门不同课程中开展该案例教学的反思,并为后续使用者提供了实用指引。本研究期望该案例能够有效助力学生学习,并为其他教师提供参考,尤其是那些生物信息学培训不足、且缺乏完整湿实验(wet lab)所需时间与经费资源的教师。
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2025-04-09
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