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

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.

Long-read sequencing of 945 Han individuals identifies novel structural variants associated with phenotypic diversity and disease susceptibility

“…The SV was associated with standing height in our cohort, individuals carrying this SV displayed a lower standing height (median height = 164 cm) than that of the noncarriers (median height = 165 cm), consistent with the reduced body length caused by knocking out of the Wwp2 gene in mice 74,75 . While the difference was not statistically significant due to the relatively small sample size (n = 1,016) in the present study ( P value = 0.28, one- way ANOVA ), we found that the SV is in complete linkage disequilibrium with rs8052428-T, which is ∼55 kb downstream to the SV, based on manual examination of the Pacbio HiFi data from a Chinese quartet family (http://chinese-quartet.org/) 76 . rs8052428-T is significantly associated with shorter sitting height ( Beta = -0.101, P value = 1.1E-29 ) and standing height ( Beta = -0.132, P value = 1.0E-17 ) in a prior genome-wide association study (GWAS) of height variation of 361,194 UK Biobank samples (http://www.nealelab.is/uk-biobank/).…”

Multi-omics Quality Assessment in Personalized Medicine through EATRIS

“…In a PT effort aimed to assess the quality of the omics technology platforms of EATRIS-Plus partners, the EATRIS-Plus project acquired Quartet multi-omics reference materials from Fudan University (Shanghai, China). The Quartet reference materials encompass DNA, RNA, protein, and metabolites, all derived from B-lymphoblastoid cell lines obtained from a familial quartet consisting of parents and monozygotic twin daughters (21)(22)(23)(24). The Quartet Project is a pivotal resource, providing "multi-omics ground truth", best practices, and computational methods for objective assessment of proficiency and reliability of data generation processes in participating laboratories (Figure 3) The Chinese Quartet reference materials and the quality assessment system.…”

“…For qualitative omics, the F1-score is a commonly used measure that takes into account both false positives and false negatives by computing a harmonic mean of precision and recall. Quality metrics for assessing reliability of DNA-seq, RNA-seq, proteomics, and metabolomics in terms of intra-batch proficiency and cross-batch reproducibility are assessed using ratio-based reference datasets (23). The Pearson correlation coefficient between the ratio-based expression levels of test datasets and reference datasets are used to describe the accuracy of quantitation.…”

“…The quality assessment system is embedded on the Quartet Data Portal (https://chinese-quartet.org/#/dashboard). A total quality score is calculated from several individual aspects of quality, for qualitative omics including Mendelian concordance rate and F1 score and for quantitative omics including signal-to-noise ratio (SNR) and relative correlation with reference dataset (RC) (23).…”

Toward Best Practice in Identifying Subtle Differential Expression with RNA-seq: A Real-World Multi-Center Benchmarking Study Using Quartet and MAQC Reference Materials

“…Recently, the Quartet project for quality control and data integration of multi-omics profiling, introduced multi-omics reference materials derived from immortalized B-lymphoblastoid cell lines from a Chinese quartet family of parents and monozygotic twin daughters, and developed ratio-based reference datasets 24 . These well-characterized, homogenous, and stable Quartet RNA reference materials with small inter-sample biological differences, provided a unique opportunity for the assessment and benchmarking of transcriptome profiling at subtle differential expression levels in a reference-based manner 23 .…”