Zak Jaroslav, Newman Ian, Montiel Garcia Daniel J, Parisi Daniele, Joy Janet, Head Steven R, Ducom Jean-Christophe, Natarajan Padmaja, Cui Haissi, Ul-Hasan Sabah
Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, United States of America.
Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah, United States of America.
PLoS Comput Biol. 2025 Sep 10;21(9):e1013453. doi: 10.1371/journal.pcbi.1013453. eCollection 2025 Sep.
Biology has been transformed by the rapid development of computing and the concurrent rise of data-rich approaches such as, omics or high-resolution imaging. However, there is a persistent computational skills gap in the biomedical research workforce. Inherent limitations of classroom teaching and institutional core support highlight the need for accessible ways for researchers to explore developments in computational biology. An analysis of the Scripps Research Genomics Core revealed increases in the total number and diversity of experiments: the share of experiments other than bulk RNA- or DNA-sequencing increased from 34% to 60% within 10 years, requiring more tailored computational analyses. These challenges were tackled by forming a volunteer-led affinity group of approximately 300 academic biomedical researchers interested in computational biology, referred to as the Computational Biology and Bioinformatics (CBB) affinity group. This adaptive group has provided continuing education and networking opportunities through seminars, workshops, and coding sessions while evolving along with the needs of its members. A survey of CBB's impact confirmed the group's events increased the members' exposure to computational biology educational and research events (79% respondents) and networking opportunities (61% respondents). Thus, volunteer-led affinity groups may be a viable complement to traditional institutional resources for enhancing the application of computing in biomedical research.
计算技术的迅速发展以及诸如组学或高分辨率成像等数据丰富型方法的同步兴起,已经改变了生物学。然而,生物医学研究人员中一直存在计算技能差距。课堂教学和机构核心支持的固有局限性凸显了为研究人员提供便捷途径以探索计算生物学进展的必要性。对斯克里普斯研究所基因组学核心的一项分析显示,实验的总数和多样性有所增加:在10年内,除大量RNA或DNA测序之外的实验份额从34%增至60%,这需要更具针对性的计算分析。通过组建一个由志愿者主导的、约300名对计算生物学感兴趣的学术生物医学研究人员组成的亲和团体,即计算生物学与生物信息学(CBB)亲和团体,这些挑战得以解决。这个适应性团体通过研讨会、工作坊和编码课程提供继续教育和交流机会,同时随着成员需求的变化而发展。对CBB影响力的一项调查证实,该团体的活动增加了成员接触计算生物学教育和研究活动的机会(79%的受访者)以及交流机会(61%的受访者)。因此,由志愿者主导的亲和团体可能是传统机构资源的一个可行补充,有助于加强计算技术在生物医学研究中的应用。