在omics比较测试中的挑战和最佳实践

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Updated: Jul 5, 2025

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Thomas G Brooks¹, Nicholas F Lahens¹, Antonijo Mrčela¹

¹Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.

Nature reviews. Genetics

|January 12, 2024

概括

Omics数据分析需要强大的基准测试,以确保可靠的结果. 本综述解决了omics基准测试中的常见陷,并为透明和有意义的绩效评估提供了指导方针.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
数据科学数据科学数据科学

背景情况:

由微阵列和高通量测序等技术驱动的奥米克时代,产生了庞大的生物数据集.
奥米克斯基准测试,对转录组学,蛋白组学和代谢组学分析方法的评估,有25年的历史.
尽管成熟,但在产生有意义的基准数据和评估绩效方面仍然存在挑战.

研究的目的:

识别和突出显示omics基准测试中常见的错误和陷.
提出一种方法来解决并透明地报告基准测试挑战.
提供关于最近的发展和OMIC数据分析中的常见困难的指导.

主要方法:

审查现有的omics基准测试研究和方法.
开发一个全面的报告准则模板 (电子表格).
调查该领域的最新进展和共同挑战.

主要成果:

识别omics基准测试实践中的反复出现的问题和潜在偏差.
建立一个结构化的方法,以透明地报告基准测试结果.
为面对常见的数据分析挑战的研究人员提供实用指导.

结论:

标准化和透明的omics基准测试对于可靠的生物数据分析至关重要.
拟议的指导方针旨在提高omics比较研究的质量和可解释性.
解决常见的陷将提高omics数据在科学发现中的实用性.

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Genomics

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