复旦大学Leming Shi，Chen Ding，Yuanting Zheng，国家计量研究所Xiang Fang和中国医学科学院Jinming Li共同合作，近期取得重要工作进展。他们研究利用基于比率的定量分析与Quartet参考材料进行多组学数据整合。相关研究成果2023年9月7日在线发表于《自然—生物技术》杂志上。

据介绍，由于缺乏事实依据，不同数据集的基因组、表观基因组、转录组、蛋白质组和代谢组的表征和整合很困难。

研究人员开发并表征了一套公开可用的多组学参考材料，这些参考材料包括匹配的DNA、RNA、蛋白质和代谢产物，这些物质来源于父母和单卵双胞胎女儿家族的永生细胞系。这些参照提供了由家族成员之间的关系以及从DNA到RNA再到蛋白质的信息流定义的内在真理。研究人员演示了如何使用基于比率的分析方法来缩放研究样本相对于同时测量的通用参考样本的绝对特征值，从而生成适合跨批次、实验室、平台和组学类型集成的可重复且可比较的数据。

总之，这一研究将无参考的“绝对”特征量化确定为多组学测量和数据集成中不可重复性的根本原因，并建立了使用常见参考材料进行基于比率的多组学分析的优势。

附：英文原文

Title: Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials

Author: Zheng, Yuanting, Liu, Yaqing, Yang, Jingcheng, Dong, Lianhua, Zhang, Rui, Tian, Sha, Yu, Ying, Ren, Luyao, Hou, Wanwan, Zhu, Feng, Mai, Yuanbang, Han, Jinxiong, Zhang, Lijun, Jiang, Hui, Lin, Ling, Lou, Jingwei, Li, Ruiqiang, Lin, Jingchao, Liu, Huafen, Kong, Ziqing, Wang, Depeng, Dai, Fangping, Bao, Ding, Cao, Zehui, Chen, Qiaochu, Chen, Qingwang, Chen, Xingdong, Gao, Yuechen, Jiang, He, Li, Bin, Li, Bingying, Li, Jingjing, Liu, Ruimei, Qing, Tao, Shang, Erfei, Shang, Jun, Sun, Shanyue, Wang, Haiyan, Wang, Xiaolin, Zhang, Naixin, Zhang, Peipei, Zhang, Ruolan, Zhu, Sibo, Scherer, Andreas, Wang, Jiucun, Wang, Jing, Huo, Yinbo, Liu, Gang, Cao, Chengming, Shao, Li, Xu, Joshua, Hong, Huixiao, Xiao, Wenming, Liang, Xiaozhen, Lu, Daru, Jin, Li, Tong, Weida, Ding, Chen, Li, Jinming, Fang, Xiang, Shi, Leming

Issue&Volume: 2023-09-07

Abstract: Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free ‘absolute’ feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials. Multi-omics data are readily integrated using quartet reference materials.

DOI: 10.1038/s41587-023-01934-1

Source: https://www.nature.com/articles/s41587-023-01934-1