迎接基因组分析的挑战:合作开发的泛基因组学和拓扑数据分析研讨会。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2024-09-27 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae139
Haydeé Contreras-Peruyero, Shaday Guerrero-Flores, Claudia Zirión-Martínez, Paulina M Mejía-Ponce, Marisol Navarro-Miranda, J Abel Lovaco-Flores, José M Ibarra-Rodríguez, Anton Pashkov, Cuauhtémoc Licona-Cassani, Nelly Sélem-Mojica
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引用次数: 0

摘要

动机随着基因组学数据分析变得越来越复杂,研究人员面临着掌握各种软件工具的挑战。庞基因组学(Pangenomics)分析可以检查一组基因组中的全套基因,它的兴起对于理解遗传多样性尤其具有变革性意义。我们由生物学家和数学家组成的跨学科团队开发了一个简短的 Pangenomics 讲习班,内容包括 Bash、Python 脚本、Pangenome 和拓扑数据分析。这些技能有助于深入了解遗传变异及其对进化生物学的影响。该工作坊使用 Conda 环境,具有可重复性和可访问性。该讲习班是在 Carpentries 孵化器基础设施的基础上开发的,旨在让研究人员掌握庞基因组学研究的基本技能。通过强调实践社区的作用,这项工作强调了其在增强多学科专业人员合作开发符合最佳实践的培训方面的意义:我们的研讨会通过提高计算生物学专业人员的技能组合取得了切实的成果。学员们通过使用首次描述的泛基因组的真实数据获得了实践经验。我们分享了创建一个开源、多学科和公共资源的路径,学习者可以在此开发庞基因组分析方面的专业知识。这项计划不仅能提高个人能力,还能满足计算生物学教育需求的更广泛使命。可用性和实施:https://carpentries-incubator.github.io/pangenomics-workshop/。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Meeting the challenge of genomic analysis: a collaboratively developed workshop for pangenomics and topological data analysis.

Motivation: As genomics data analysis becomes increasingly intricate, researchers face the challenge of mastering various software tools. The rise of Pangenomics analysis, which examines the complete set of genes in a group of genomes, is particularly transformative in understanding genetic diversity. Our interdisciplinary team of biologists and mathematicians developed a short Pangenomics Workshop covering Bash, Python scripting, Pangenome, and Topological Data Analysis. These skills provide deeper insights into genetic variations and their implications in Evolutionary Biology. The workshop uses a Conda environment for reproducibility and accessibility. Developed in The Carpentries Incubator infrastructure, the workshop aims to equip researchers with essential skills for Pangenomics research. By emphasizing the role of a community of practice, this work underscores its significance in empowering multidisciplinary professionals to collaboratively develop training that adheres to best practices.

Results: Our workshop delivers tangible outcomes by enhancing the skill sets of Computational Biology professionals. Participants gain hands-on experience using real data from the first described pangenome. We share our paths toward creating an open-source, multidisciplinary, and public resource where learners can develop expertise in Pangenomic Analysis. This initiative goes beyond advancing individual capabilities, aligning with the broader mission of addressing educational needs in Computational Biology.

Availability and implementation: https://carpentries-incubator.github.io/pangenomics-workshop/.

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