通过团队合作提高生物信息学软件质量。

Katalin Ferenc, Ieva Rauluseviciute, Ladislav Hovan, Vipin Kumar, Marieke L Kuijjer, Anthony Mathelier
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引用次数: 0

摘要

自从高通量技术成为生物科学实验室的主流以来,计算算法和科学软件得到了蓬勃发展。然而,生物信息学软件的开发通常缺乏软件开发质量标准。由此产生的软件代码难以测试、重用和维护。我们认为,在学术环境中实施最佳软件开发实践效率低下的根本原因在于个人主义方法,这种方法历来是表彰科学成就的规范,进而也是开发专业软件的规范。在大多数软件繁重的工作中,软件开发都是一项集体工作。事实上,文献表明,通过知识共享、集体软件开发和既定编码标准,团队合作会直接影响代码质量。在我们的计算生物学研究小组中,我们让所有小组成员持续参与软件开发的学习、共享和讨论,同时保持研究项目和相关软件产品的个人所有权。我们发现,参与这项工作的小组成员提高了编码技能,成为了更高效的生物信息学家,并详细了解了同伴的工作,从而引发了新的合作项目。我们大力提倡在有三名或三名以上生物信息学家的计算生物学小组或研究所中,通过集体努力改善生物信息学领域的软件开发文化。
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Improving bioinformatics software quality through teamwork.

Summary: Since high-throughput techniques became a staple in biological science laboratories, computational algorithms, and scientific software have boomed. However, the development of bioinformatics software usually lacks software development quality standards. The resulting software code is hard to test, reuse, and maintain. We believe that the root of inefficiency in implementing the best software development practices in academic settings is the individualistic approach, which has traditionally been the norm for recognizing scientific achievements and, by extension, for developing specialized software. Software development is a collective effort in most software-heavy endeavors. Indeed, the literature suggests teamwork directly impacts code quality through knowledge sharing, collective software development, and established coding standards. In our computational biology research groups, we sustainably involve all group members in learning, sharing, and discussing software development while maintaining the personal ownership of research projects and related software products. We found that group members involved in this endeavor improved their coding skills, became more efficient bioinformaticians, and obtained detailed knowledge about their peers' work, triggering new collaborative projects. We strongly advocate for improving software development culture within bioinformatics through collective effort in computational biology groups or institutes with three or more bioinformaticians.

Availability and implementation: Additional information and guidance on how to get started is available at https://ferenckata.github.io/ImprovingSoftwareTogether.github.io/.

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