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Introduction to Genomic Analysis Workshop: A catalyst for engaging life-science researchers in high throughput analysis.基因组分析研讨会简介:促使生命科学研究人员参与高通量分析的催化剂。
F1000Res. 2019 Jul 30;8:1221. doi: 10.12688/f1000research.19320.1. eCollection 2019.
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Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.使用QIIME 2进行可重复、交互式、可扩展和可延伸的微生物组数据科学研究。
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Women are underrepresented in computational biology: An analysis of the scholarly literature in biology, computer science and computational biology.女性在计算生物学领域的代表性不足:对生物学、计算机科学和计算生物学学术文献的分析。
PLoS Comput Biol. 2017 Oct 12;13(10):e1005134. doi: 10.1371/journal.pcbi.1005134. eCollection 2017 Oct.
4
QIIME allows analysis of high-throughput community sequencing data.QIIME可用于分析高通量群落测序数据。
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通过教学与研究的整合,对新的高性能计算实践社区进行教育并使其参与其中。

Educating and engaging new communities of practice with high performance computing through the integration of teaching and research.

作者信息

Townsend-Nicholson Andrea

机构信息

Structural and Molecular Biology, Division of Biosciences, Faculty of Life Sciences, University College London, Darwin Building, Gower Street, London WC1E 6BT, UK.

出版信息

Interface Focus. 2020 Dec 6;10(6):20200003. doi: 10.1098/rsfs.2020.0003. Epub 2020 Oct 16.

DOI:10.1098/rsfs.2020.0003
PMID:33184587
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7653341/
Abstract

The identification of strategies by which to increase the representation of women and increase diversity in STEM fields (science, technology, engineering and mathematics), including medicine, has been a pressing matter for global agencies including the European Commission, UNESCO and numerous international scientific societies. In my role as UCL training lead for CompBioMed, a European Commission Horizon 2020-funded Centre of Excellence in Computational Biomedicine (compbiomed.eu), and as Head of Teaching for Molecular Biosciences at UCL from 2010 to 2019, I have integrated research and teaching to lead the development of high-performance computing (HPC)-based education targeting medical students and undergraduate students studying biosciences in a way that is explicitly integrated into the existing university curriculum as a credit-bearing module. One version of the credit-bearing module has been specifically designed for medical students in their pre-clinical years of study and one of the unique features of the course is the integration of clinical and computational aspects, with students obtaining and processing clinical samples and then interrogating the results computationally using code that was ported to HPC at CompBioMed's HPC Facility core partners (EPCC (UK), SURFsara (The Netherlands) and the Barcelona Supercomputing Centre (Spain)). Another version of the credit-bearing module has, over the course of this project, evolved into a replacement for the third year research project course for undergraduate biochemistry, biotechnology and molecular biology students, providing students with the opportunity to design and complete an entire specialist research project from the formulation of experimental hypotheses to the investigation of these hypotheses in a way that involves the integration of experimental and HPC-based computational methodologies. Since 2017-2018, these UCL modules have been successfully delivered to over 350 students-a cohort with a demographic of greater than 50% female. CompBioMed's experience with these two university modules has enabled us to distil our methodology into an educational template that can be delivered at other universities in Europe and worldwide. This educational approach to training enables new communities of practice to effectively engage with HPC and reveals a means by which to improve the underrepresentation of women in supercomputing.

摘要

确定增加女性代表性并提高包括医学在内的STEM领域(科学、技术、工程和数学)多样性的策略,一直是欧盟委员会、联合国教科文组织以及众多国际科学协会等全球机构的紧迫事务。作为伦敦大学学院(UCL)CompBioMed培训负责人(CompBioMed是欧盟委员会“地平线2020”资助的计算生物医学卓越中心,网址为compbiomed.eu),以及2010年至2019年期间UCL分子生物科学教学负责人,我将研究与教学相结合,主导了以高性能计算(HPC)为基础的教育发展,目标受众是医学生和学习生物科学的本科生,这种教育以一种明确作为学分模块融入现有大学课程的方式进行。其中一个学分模块版本是专门为医学预科阶段的学生设计的,该课程的一个独特之处在于将临床和计算方面相结合,学生获取并处理临床样本,然后使用在CompBioMed的HPC设施核心合作伙伴(英国的EPCC、荷兰的SURFsara和西班牙的巴塞罗那超级计算中心)移植到HPC上的代码对结果进行计算分析。在本项目过程中,另一个学分模块版本已演变为本科生物化学、生物技术和分子生物学专业学生三年级研究项目课程的替代课程,为学生提供了设计并完成一个完整专业研究项目的机会,从实验假设的形成到这些假设的研究,都涉及实验和基于HPC的计算方法的整合。自2017 - 2018年以来,这些UCL模块已成功交付给350多名学生,该群体中女性比例超过50%。CompBioMed在这两个大学模块方面的经验使我们能够将方法提炼成一个教育模板,可在欧洲和全球其他大学推广。这种培训教育方法使新的实践社区能够有效地接触HPC,并揭示了一种改善超级计算领域女性代表性不足问题的方法。