Abstract
The low quantitative accuracy of conventional small noncoding RNA sequencing (sncRNA-seq) methods due to extensive ligation bias commonly limits functional investigation of microRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs). Here, we develop 4NBoost, a single-tube sncRNA-seq protocol designed to minimize bias in the estimated absolute quantification of miRNA and piRNA transcripts through the incorporation of quantitative exogenous RNA spike-ins. With 4NBoost, we profile sncRNA expression across 20 murine tissues, 18 macaque tissues, and 24 widely used cell lines, as well as 4 Arabidopsis tissues, to establish a comprehensive quantitative reference atlas. Compared with existing small RNA databases, our data reveal substantial biases in miRNA abundance, strand selection, and tissue-specific expression at both individual and family levels. To further extend its utility, we employ machine learning to model and correct biases in conventional datasets, effectively recovering ground truth transcript abundances. All 4NBoost data and the accompanying bias-correction model are freely available via SmRNAQuant (http://wulg-lab.sibcb.ac.cn/SmRNAQuant/), a web-based repository for exploring sncRNA expression. Together, the 4NBoost, bias-correction model, and SmRNAQuant provide powerful resources to advance sncRNA research.
Data availability
The deep-sequencing data have been deposited at the National Center for Biotechnology Information NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) database under accession number GSE279145. The following publicly available datasets were used: K562 AQ-seq data from GSE158025; K562 TruSeq data from GSE102497; HCT116 AQ-seq data from GSE230544; HCT116 TruSeq data from GSE180613 and GSE189908; HEK293T and Hela AQ-seq data from GSE123627; HEK293T and Hela TruSeq data from GSE57295; HEK293T IsoSeek data from PRJNA867189. Source data are provided with this paper.
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Acknowledgements
We are grateful to Jin Ren and Xinming Qi for providing Crab-eating monkey tissues and Jun Zhu for providing Arabidopsis thaliana tissues. We thank all members of L. Wu’s laboratory for their discussion and comments on this project; Y. Xu for his assistance with high-performance computing; and the staff at the HPC storage and network service platform of SIBCB for supplying the computing resources. Schematics in Figs. 2a and 6a were created using BioRender.com and are included under a paid publication license obtained from BioRender. We acknowledge BioRender for providing the platform and tools for scientific illustration. This work was supported by the National Key R&D Program of China (2021YFA1100201 and 2022YFA1303301), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0570000), and the National Natural Science Foundation of China (92581120) to Ligang Wu; the National Natural Science Foundation of China (32200696) and the Youth Innovation Promotion Association CAS to H.Z.
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L.W. and H.Z. conceived and designed the study. Y.Z. and H.Z. conceived and developed the methodology of 4NBoost. B.X., R.Z., W.X., and Y.Z. collected the cells and tissues. B.X., R.Z., and Y.Z. isolated all the RNAs. Y.Z. generated all sncRNA-seq libraries. W.X. designed spike-ins and performed computational analyses. W.X., H.Z., and Y.Z. interpreted the data. W.X., H.Z., L.W., and Y.Z. wrote the manuscript.
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Xiao, W., Zheng, Y., Zhang, H. et al. An absolute quantification atlas of small non-coding RNAs across diverse mammalian tissues and cell lines. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68812-7
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DOI: https://doi.org/10.1038/s41467-026-68812-7
