Article Contents
ARTICLE   Open Access     Cite

NaRaDa: A comprehensive nascent RNA database

    Show all affliationsShow less
More Information
  • Corresponding author: bao_xichen@gibh.ac.cn
  • DownLoad: Full size image
    1. NaRaDa compiles and organizes 3,664 nascent RNA-seq datasets from 415 studies across 22 species.

      558,574 transcriptional regulatory elements (TREs) across different species have been identified for browsing.

      NaRaDa also provides in-depth analysis of biological processes or events to study the transcriptional regulation.

  • Nascent RNA-seq is pivotal for elucidating the dynamics and transcriptional regulation of enhancer RNA and protein-coding genes, and for revealing the regulatory linkages between enhancer-gene pairs in diverse physiological and pathological contexts. However, despite the rapid growth of nascent RNA-seq datasets, a comprehensive database for the collection and analysis of these datasets remains absent. Here, we developed the Nascent RNA database (NaRaDa, http://www.narada.bio), which compiles and categorizes 3,664 global or precision run-on sequencing (GRO/PRO-seq) datasets from 415 studies across 22 species. NaRaDa provides a user-friendly interface for searching, visualizing, and assessing the quality of integrated GRO/PRO-seq datasets. Furthermore, NaRaDa identifies transcriptional regulatory elements (TREs) along with other annotation information from ENCODE, FANTOM5, and disease-associated risk SNPs, which are accessible by either “Gene-Centric” or “Project ID-Centric” modes. The database also furnishes researchers with web-based tools to identify differentially expressed genes, TREs, transcription pausing index changes, and more importantly, potential functional transcription factors, in response to specific treatments. These results can be readily downloaded for further independent analysis. NaRaDa serves as an invaluable resource for deepening our understanding of the regulatory mechanisms governing transcription and for identifying previously unrecognized correlations between genomic loci and diseases.
  • 加载中
  • [1] Wissink E. M., Vihervaara A., Tippens N. D., et al. (2019). Nascent RNA analyses: Tracking transcription and its regulation. Nat Rev Genet 20:705−723. DOI:10.1038/s41576-019-0159-6

    View in Article CrossRef Google Scholar

    [2] Spitz F. and Furlong E. E. (2012). Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13:613−626. DOI:10.1038/nrg3207

    View in Article CrossRef Google Scholar

    [3] Ozasa S., Kimura S., Ito K., et al. (2007). Efficient conversion of ES cells into myogenic lineage using the gene-inducible system. Biochem. Biophys. Res. Commun. 357:957−963. DOI:10.1016/j.bbrc.2007.04.032

    View in Article CrossRef Google Scholar

    [4] Asakura A., Hirai H., Kablar B., et al. (2007). Increased survival of muscle stem cells lacking the MyoD gene after transplantation into regenerating skeletal muscle. Proc. Natl. Acad. Sci. USA 104:16552−16557. DOI:10.1073/pnas.0708145104

    View in Article CrossRef Google Scholar

    [5] Takahashi K. and Yamanaka S. (2006). Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126:663−676. DOI:10.1016/j.cell.2006.07.024

    View in Article CrossRef Google Scholar

    [6] Tapscott S. J., Davis R. L., Thayer M. J., et al. (1988). MyoD1: A nuclear phosphoprotein requiring a Myc homology region to convert fibroblasts to myoblasts. Science 242:405−411. DOI:10.1126/science.3175662

    View in Article CrossRef Google Scholar

    [7] Yamanaka S. (2007). Strategies and new developments in the generation of patient-specific pluripotent stem cells. Cell Stem Cell 1:39−49. DOI:10.1016/j.stem.2007.05.012

    View in Article CrossRef Google Scholar

    [8] Lambert S. A., Jolma A., Campitelli L. F., et al. (2018). The human transcription factors. Cell 175:598−599. DOI:10.1016/j.cell.2018.09.045

    View in Article CrossRef Google Scholar

    [9] Feng C., Song C., Song S., et al. (2024). KnockTF 2.0: A comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species. Nucleic Acids Res. 52:D183-D193. DOI:10.1093/nar/gkad1016.

    View in Article Google Scholar

    [10] Guo Z., Zhu L., Cheng Z., et al. (2024). A midgut transcriptional regulatory loop favors an insect host to withstand a bacterial pathogen. The Innovation 5:100675. DOI:10.1016/j.xinn.2024.100675

    View in Article CrossRef Google Scholar

    [11] Stadhouders R., Vidal E., Serra F., et al. (2018). Transcription factors orchestrate dynamic interplay between genome topology and gene regulation during cell reprogramming. Nat. Genet. 50:238−249. DOI:10.1038/s41588-017-0030-7

    View in Article CrossRef Google Scholar

    [12] Core L. J., Waterfall J. J. and Lis J. T. (2008). Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 322:1845−1848. DOI:10.1126/science.1162228

    View in Article CrossRef Google Scholar

    [13] Kwak H., Fuda N. J., Core L. J., et al. (2013). Precise maps of RNA polymerase reveal how promoters direct initiation and pausing. Science 339:950−953. DOI:10.1126/science.1229386

    View in Article CrossRef Google Scholar

    [14] Ntini E., Jarvelin A. I., Bornholdt J., et al. (2013). Polyadenylation site-induced decay of upstream transcripts enforces promoter directionality. Nat. Struct. Mol. Biol. 20:923−928. DOI:10.1038/nsmb.2640

    View in Article CrossRef Google Scholar

    [15] Almada A. E., Wu X., Kriz A. J., et al. (2013). Promoter directionality is controlled by U1 snRNP and polyadenylation signals. Nature 499:360−363. DOI:10.1038/nature12349

    View in Article CrossRef Google Scholar

    [16] Kwak H. and Lis J. T. (2013). Control of transcriptional elongation. Annu. Rev. Genet. 47:483−508. DOI:10.1146/annurev-genet-110711-155440

    View in Article CrossRef Google Scholar

    [17] Adelman K. and Lis J. T. (2012). Promoter-proximal pausing of RNA polymerase II: emerging roles in metazoans. Nat. Rev. Genet. 13:720−731. DOI:10.1038/nrg3293

    View in Article CrossRef Google Scholar

    [18] Hu S., Peng L., Song A., et al. (2023). INTAC endonuclease and phosphatase modules differentially regulate transcription by RNA polymerase II. Mol. Cell 83:1588-1604 e1585. DOI:10.1016/j.molcel.2023.03.022

    View in Article Google Scholar

    [19] Muse G. W., Gilchrist D. A., Nechaev S., et al. (2007). RNA polymerase is poised for activation across the genome. Nat. Genet. 39:1507−1511. DOI:10.1038/ng.2007.21

    View in Article CrossRef Google Scholar

    [20] Rahl P. B., Lin C. Y., Seila A. C., et al. (2010). c-Myc regulates transcriptional pause release. Cell 141:432−445. DOI:10.1016/j.cell.2010.03.030

    View in Article CrossRef Google Scholar

    [21] Hu S., Song A., Peng L., et al. (2023). H3K4me2/3 modulate the stability of RNA polymerase II pausing. Cell Res. 33:403−406. DOI:10.1038/s41422-023-00794-3

    View in Article CrossRef Google Scholar

    [22] Chen F. X., Woodfin A. R., Gardini A., et al. (2015). PAF1, a Molecular Regulator of Promoter-Proximal Pausing by RNA Polymerase II. Cell 162:1003−1015. DOI:10.1016/j.cell.2015.07.042

    View in Article CrossRef Google Scholar

    [23] Liu L., Xu Y., He M., et al. (2014). Transcriptional pause release is a rate-limiting step for somatic cell reprogramming. Cell Stem Cell 15:574−588. DOI:10.1016/j.stem.2014.09.018

    View in Article CrossRef Google Scholar

    [24] Gorbovytska V., Kim S. K., Kuybu F., et al. (2022). Enhancer RNAs stimulate Pol II pause release by harnessing multivalent interactions to NELF. Nat. Commun. 13:2429. DOI:10.1038/s41467-022-29934-w

    View in Article CrossRef Google Scholar

    [25] Wang D., Garcia-Bassets I., Benner C., et al. (2011). Reprogramming transcription by distinct classes of enhancers functionally defined by eRNA. Nature 474:390−394. DOI:10.1038/nature10006

    View in Article CrossRef Google Scholar

    [26] Core L. J., Martins A. L., Danko C. G., et al. (2014). Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers. Nat. Genet. 46:1311−1320. DOI:10.1038/ng.3142

    View in Article CrossRef Google Scholar

    [27] Andersson R., Sandelin A. and Danko C. G. (2015). A unified architecture of transcriptional regulatory elements. Trends Genet. 31:426−433. DOI:10.1016/j.tig.2015.05.007

    View in Article CrossRef Google Scholar

    [28] Henriques T., Scruggs B. S., Inouye M. O., et al. (2018). Widespread transcriptional pausing and elongation control at enhancers. Genes Dev. 32:26−41. DOI:10.1101/gad.309351.117

    View in Article CrossRef Google Scholar

    [29] Lam M. T., Li W., Rosenfeld M. G., et al. (2014). Enhancer RNAs and regulated transcriptional programs. Trends Biochem. Sci. 39:170−182. DOI:10.1016/j.tibs.2014.02.007

    View in Article CrossRef Google Scholar

    [30] Melo C. A., Drost J., Wijchers P. J., et al. (2013). eRNAs are required for p53-dependent enhancer activity and gene transcription. Mol. Cell 49:524−535. DOI:10.1016/j.molcel.2012.11.021

    View in Article CrossRef Google Scholar

    [31] Mousavi K., Zare H., Dell'orso S., et al. (2013). eRNAs promote transcription by establishing chromatin accessibility at defined genomic loci. Mol. Cell 51:606−617. DOI:10.1016/j.molcel.2013.07.022

    View in Article CrossRef Google Scholar

    [32] Onoguchi M., Hirabayashi Y., Koseki H., et al. (2012). A noncoding RNA regulates the neurogenin1 gene locus during mouse neocortical development. Proc. Natl. Acad. Sci. USA 109:16939−16944. DOI:10.1073/pnas.1202956109

    View in Article CrossRef Google Scholar

    [33] Kong Y., Yu J., Ge S., et al. (2023). Novel insight into RNA modifications in tumor immunity: Promising targets to prevent tumor immune escape. The Innovation 4:100452. DOI:10.1016/j.xinn.2023.100452

    View in Article CrossRef Google Scholar

    [34] Lam M. T., Cho H., Lesch H. P., et al. (2013). Rev-Erbs repress macrophage gene expression by inhibiting enhancer-directed transcription. Nature 498:511−515. DOI:10.1038/nature12209

    View in Article CrossRef Google Scholar

    [35] Duttke S. H., Guzman C., Chang M., et al. (2024). Position-dependent function of human sequence-specific transcription factors. Nature 631:891−898. DOI:10.1038/s41586-024-07662-z

    View in Article CrossRef Google Scholar

    [36] Heinz S., Benner C., Spann N., et al. (2010). Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38:576−589. DOI:10.1016/j.molcel.2010.05.004

    View in Article CrossRef Google Scholar

    [37] Chae M., Danko C. G. and Kraus W. L. (2015). groHMM: a computational tool for identifying unannotated and cell type-specific transcription units from global run-on sequencing data. BMC Bioinformatics 16:222. DOI:10.1186/s12859-015-0656-3

    View in Article CrossRef Google Scholar

    [38] Danko C. G., Hyland S. L., Core L. J., et al. (2015). Identification of active transcriptional regulatory elements from GRO-seq data. Nat. Methods 12:433−438. DOI:10.1038/nmeth.3329

    View in Article CrossRef Google Scholar

    [39] Wang Z., Chu T., Choate L. A., et al. (2019). Identification of regulatory elements from nascent transcription using dREG. Genome Res. 29:293−303. DOI:10.1101/gr.238279.118

    View in Article CrossRef Google Scholar

    [40] Qian F. C., Zhou L. W., Li Y. Y., et al. (2023). SEanalysis 2.0: a comprehensive super-enhancer regulatory network analysis tool for human and mouse. Nucleic Acids Res. 51:W520-W527. DOI:10.1093/nar/gkad408

    View in Article Google Scholar

    [41] Song C., Zhang G., Mu X., et al. (2024). eRNAbase: a comprehensive database for decoding the regulatory eRNAs in human and mouse. Nucleic Acids Res. 52:D81−D91. DOI:10.1093/nar/gkad925

    View in Article CrossRef Google Scholar

    [42] Wang Y., Song C., Zhao J., et al. (2023). SEdb 2.0: a comprehensive super-enhancer database of human and mouse. Nucleic Acids Res. 51:D280-D290. DOI:10.1093/nar/gkac968.

    View in Article Google Scholar

    [43] Andersson R., Gebhard C., Miguel-Escalada I., et al. (2014). An atlas of active enhancers across human cell types and tissues. Nature 507:455−461. DOI:10.1038/nature12787

    View in Article CrossRef Google Scholar

    [44] Heintzman N. D., Stuart R. K., Hon G., et al. (2007). Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat. Genet. 39:311−318. DOI:10.1038/ng1966

    View in Article CrossRef Google Scholar

    [45] Garieri M., Delaneau O., Santoni F., et al. (2017). The effect of genetic variation on promoter usage and enhancer activity. Nat. Commun. 8:1358. DOI:10.1038/s41467-017-01467-7

    View in Article CrossRef Google Scholar

    [46] Kubo N., Chen P. B., Hu R., et al. (2024). H3K4me1 facilitates promoter-enhancer interactions and gene activation during embryonic stem cell differentiation. Mol. Cell 84:1742-1752 e1745. DOI:10.1016/j.molcel.2024.02.030

    View in Article Google Scholar

    [47] Shen Y., Yue F., McCleary D. F., et al. (2012). A map of the cis-regulatory sequences in the mouse genome. Nature 488:116−120. DOI:10.1038/nature11243

    View in Article CrossRef Google Scholar

    [48] Dong P., Hoffman G. E., Apontes P., et al. (2022). Population-level variation in enhancer expression identifies disease mechanisms in the human brain. Nat. Genet. 54:1493−1503. DOI:10.1038/s41588-022-01170-4

    View in Article CrossRef Google Scholar

    [49] Rada-Iglesias A., Bajpai R., Swigut T., et al. (2011). A unique chromatin signature uncovers early developmental enhancers in humans. Nature 470:279−283. DOI:10.1038/nature09692

    View in Article CrossRef Google Scholar

    [50] Barrett T., Suzek T. O., Troup D. B., et al. (2005). NCBI GEO: mining millions of expression profiles--database and tools. Nucleic Acids Res. 33:D562-566. DOI:10.1093/nar/gki022

    View in Article Google Scholar

    [51] Barrett T., Troup D. B., Wilhite S. E., et al. (2007). NCBI GEO: mining tens of millions of expression profiles--database and tools update. Nucleic Acids Res. 35:D760-765. DOI:10.1093/nar/gkl887

    View in Article Google Scholar

    [52] Kodama Y., Shumway M., Leinonen R., et al. (2012). The Sequence Read Archive: explosive growth of sequencing data. Nucleic Acids Res. 40:D54−56. DOI:10.1093/nar/gkr854

    View in Article CrossRef Google Scholar

    [53] Members C.-N. and Partners (2024). Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024. Nucleic Acids Res. 52:D18-D32. DOI:10.1093/nar/gkad1078.

    View in Article Google Scholar

    [54] Chen T., Chen X., Zhang S., et al. (2021). The Genome Sequence Archive Family: Toward Explosive Data Growth and Diverse Data Types. Genomics Proteomics Bioinformatics 19:578−583. DOI:10.1016/j.gpb.2021.08.001

    View in Article CrossRef Google Scholar

    [55] Smith J. P., Dutta A. B., Sathyan K. M., et al. (2021). PEPPRO: quality control and processing of nascent RNA profiling data. Genome Biol. 22:155. DOI:10.1186/s13059-021-02349-4

    View in Article CrossRef Google Scholar

    [56] Fant C. B., Levandowski C. B., Gupta K., et al. (2020). TFIID Enables RNA Polymerase II Promoter-Proximal Pausing. Molecular Cell 78:785−793. DOI:10.1016/j.molcel.2020.03.008

    View in Article CrossRef Google Scholar

    [57] Mortazavi A., Williams B. A., McCue K., et al. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5:621−628. DOI:10.1038/nmeth.1226

    View in Article CrossRef Google Scholar

    [58] Yao L., Liang J., Ozer A., et al. (2022). A comparison of experimental assays and analytical methods for genome-wide identification of active enhancers. Nat. Biotechnol. 40:1056−1065. DOI:10.1038/s41587-022-01211-7

    View in Article CrossRef Google Scholar

    [59] Lidschreiber K., Jung L. A., von der Emde H., et al. (2021). Transcriptionally active enhancers in human cancer cells. Mol. Syst. Biol. 17:e9873. DOI:10.15252/msb.20209873

    View in Article CrossRef Google Scholar

    [60] Michel M., Demel C., Zacher B., et al. (2017). TT-seq captures enhancer landscapes immediately after T-cell stimulation. Mol. Syst. Biol. 13:920. DOI:10.15252/msb.20167507

    View in Article CrossRef Google Scholar

    [61] Cao Q., Anyansi C., Hu X. H., et al. (2017). Reconstruction of enhancer-target networks in 935 samples of human primary cells, tissues and cell lines. Nat. Genet. 49:1428−1436. DOI:10.1038/ng.3950

    View in Article CrossRef Google Scholar

    [62] Furlong E. E. M. and Levine M. (2018). Developmental enhancers and chromosome topology. Science 361:1341−1345. DOI:10.1126/science.aau0320

    View in Article CrossRef Google Scholar

    [63] He B., Chen C. Y., Teng L., et al. (2014). Global view of enhancer-promoter interactome in human cells. Proc. Natl. Acad. Sci. USA 111:E2191−E2199. DOI:10.1073/pnas.1320308111

    View in Article CrossRef Google Scholar

    [64] Karpinska M. A. and Oudelaar A. M. (2023). The role of loop extrusion in enhancer-mediated gene activation. Curr. Opin. Genet. Dev. 79:102022. DOI:10.1016/j.gde.2023.102022

    View in Article CrossRef Google Scholar

    [65] Gao T. S. and Qian J. (2019). EAGLE: An algorithm that utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interactions. Plos Comput. Biol. 15. DOI:ARTN e100743610.1371/journal.pcbi.1007436

    View in Article Google Scholar

    [66] Wagner G. P., Kin K. and Lynch V. J. (2012). Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 131:281−285. DOI:10.1007/s12064-012-0162-3

    View in Article CrossRef Google Scholar

    [67] Li B. and Dewey C. N. (2011). RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323. DOI:10.1186/1471-2105-12-323

    View in Article CrossRef Google Scholar

    [68] Robinson M. D., McCarthy D. J. and Smyth G. K. (2010). edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139−140. DOI:10.1093/bioinformatics/btp616

    View in Article CrossRef Google Scholar

    [69] Buniello A., MacArthur J. A. L., Cerezo M., et al. (2019). The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47:D1005−D1012. DOI:10.1093/nar/gky1120

    View in Article CrossRef Google Scholar

    [70] Hinrichs A. S., Karolchik D., Baertsch R., et al. (2006). The UCSC Genome Browser Database: update 2006. Nucleic Acids Res. 34:D590−D598. DOI:10.1093/nar/gkj144

    View in Article CrossRef Google Scholar

    [71] Xiang J. F., Yin Q. F., Chen T., et al. (2014). Human colorectal cancer-specific CCAT1-L lncRNA regulates long-range chromatin interactions at the MYC locus. Cell Res. 24:513−531. DOI:10.1038/cr.2014.35

    View in Article CrossRef Google Scholar

    [72] Rajkumar A. P., Qvist P., Lazarus R., et al. (2015). Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq. BMC Genomics 16:548. DOI:10.1186/s12864-015-1767-y

    View in Article CrossRef Google Scholar

    [73] Danko C. G., Hah N., Luo X., et al. (2013). Signaling pathways differentially affect RNA polymerase II initiation, pausing, and elongation rate in cells. Mol. Cell 50:212−222. DOI:10.1016/j.molcel.2013.02.015

    View in Article CrossRef Google Scholar

    [74] Jin F., Li Y., Dixon J. R., et al. (2013). A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503:290−294. DOI:10.1038/nature12644

    View in Article CrossRef Google Scholar

    [75] Azofeifa J. G., Allen M. A., Hendrix J. R., et al. (2018). Enhancer RNA profiling predicts transcription factor activity. Genome Res. 28:334−344. DOI:10.1101/gr.225755.117

    View in Article CrossRef Google Scholar

    [76] Hah N., Murakami S., Nagari A., et al. (2013). Enhancer transcripts mark active estrogen receptor binding sites. Genome Res. 23:1210−1223. DOI:10.1101/gr.152306.112

    View in Article CrossRef Google Scholar

    [77] Heintzman N. D., Hon G. C., Hawkins R. D., et al. (2009). Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459:108−112. DOI:10.1038/nature07829

    View in Article CrossRef Google Scholar

    [78] Zheng M., Lin Y., Wang W., et al. (2022). Application of nucleoside or nucleotide analogues in RNA dynamics and RNA-binding protein analysis. Wiley Interdiscip. Rev. RNA 13:e1722. DOI:10.1002/wrna.1722

    View in Article CrossRef Google Scholar

    [79] Duttke S. H., Chang M. W., Heinz S., et al. (2019). Identification and dynamic quantification of regulatory elements using total RNA. Genome Res. 29:1836−1846. DOI:10.1101/gr.253492.119

    View in Article CrossRef Google Scholar

    [80] Nechaev S., Fargo D. C., dos Santos G., et al. (2010). Global analysis of short RNAs reveals widespread promoter-proximal stalling and arrest of Pol II in Drosophila. Science 327:335−338. DOI:10.1126/science.1181421

    View in Article CrossRef Google Scholar

    [81] Chu T., Rice E. J., Booth G. T., et al. (2018). Chromatin run-on and sequencing maps the transcriptional regulatory landscape of glioblastoma multiforme. Nat. Genet. 50:1553−1564. DOI:10.1038/s41588-018-0244-3

    View in Article CrossRef Google Scholar

    [82] Churchman L. S. and Weissman J. S. (2011). Nascent transcript sequencing visualizes transcription at nucleotide resolution. Nature 469:368−373. DOI:10.1038/nature09652

    View in Article CrossRef Google Scholar

    [83] Herzog V. A., Reichholf B., Neumann T., et al. (2017). Thiol-linked alkylation of RNA to assess expression dynamics. Nat. Methods 14:1198−1204. DOI:10.1038/nmeth.4435

    View in Article CrossRef Google Scholar

    [84] Buenrostro J. D., Giresi P. G., Zaba L. C., et al. (2013). Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10:1213−1218. DOI:10.1038/nmeth.2688

    View in Article CrossRef Google Scholar

    [85] Lieberman-Aiden E., van Berkum N. L., Williams L., et al. (2009). Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326:289−293. DOI:10.1126/science.1181369

    View in Article CrossRef Google Scholar

  • Cite this article:

    Mai Z., Li D., Tang P., et al. (2025). NaRaDa: A comprehensive nascent RNA database. The Innovation Life 3:100143. https://doi.org/10.59717/j.xinn-life.2025.100143
    Mai Z., Li D., Tang P., et al. (2025). NaRaDa: A comprehensive nascent RNA database. The Innovation Life 3:100143. https://doi.org/10.59717/j.xinn-life.2025.100143

Welcome!

To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.

Figures(6)     Tables(1)

Supplementary Information

Share

  • Share the QR code with wechat scanning code to friends and circle of friends.

Article Metrics

Article views(5561) PDF downloads(2059)

Relative Articles

Cited by

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint