Cited by 1,007Cancer-related molecular mechanisms researchGenomics and Chromatin DynamicsRNA Research and Splicing

An atlas of human long non-coding RNAs with accurate 5′ ends

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Hon, Chung-Chau, Ramilowski, Jordan A., Harshbarger, Jayson, Bertin, Nicolas, Rackham, Owen J. L., Gough, Julian, Denisenko, Elena, Schmeier, Sebastian +31 more·2017·Nature

Abstract

Long non-coding RNAs (lncRNAs) are largely heterogeneous and functionally uncharacterized. Here, using FANTOM5 cap analysis of gene expression (CAGE) data, we integrate multiple transcript collections to generate a comprehensive atlas of 27,919 human lncRNA genes with high-confidence 5' ends and expression profiles across 1,829 samples from the major human primary cell types and tissues. Genomic and epigenomic classification of these lncRNAs reveals that most intergenic lncRNAs originate from enhancers rather than from promoters. Incorporating genetic and expression data, we show that lncRNAs overlapping trait-associated single nucleotide polymorphisms are specifically expressed in cell types relevant to the traits, implicating these lncRNAs in multiple diseases. We further demonstrate that lncRNAs overlapping expression quantitative trait loci (eQTL)-associated single nucleotide polymorphisms of messenger RNAs are co-expressed with the corresponding messenger RNAs, suggesting their potential roles in transcriptional regulation. Combining these findings with conservation data, we identify 19,175 potentially functional lncRNAs in the human genome.

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  • A majority of novel TSSs were supported by spliced transcripts from RNA-seq and expressed sequence tags.

    “A majority of the novel TSSs were supported by spliced transcripts (see methods) from RNA-seq25,26 and expressed sequence tag27”

    From Alternative promoters in CpG depleted regions are prevalently associated with epigenetic misregulation of liver cancer transcriptomes (2023) · cites this paper as [25]

    The ‘Building a 5′ complete transcriptome’ section describes identifying TSSs using transcript models, including an RNA-seq assembly, and considers transcript exon number. The Methods section on FANTOM5 RNA-seq also describes assembling splice junctions. Neither passage establishes that a majority of novel TSSs were supported by spliced transcripts or identifies expressed sequence tags as support. (LLM, conf=0.76)

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Research works printed but not linked: 54

  1. [1]Carninci P The transcriptional landscape of the mammalian genome. Science 309, 1559– 1563 (2005). 16141072
  2. [3]Iyer MK The landscape of long noncoding RNAs in the human transcriptome. Nature Genet. 47, 199– 208 (2015). 25599403 PMC4417758
  3. [4]Cabili MN Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 25, 1915– 1927 (2011). 21890647 PMC3185964
  4. [5]Derrien T The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 22, 1775– 1789 (2012). 22955988 PMC3431493
  5. [6]Quek XC lncRNAdb v2.0: expanding the reference database for functional long noncoding RNAs. Nucleic Acids Res. 43, D168– D173 (2015). 25332394 PMC4384040
  6. [7]Schmidt LH The long noncoding MALAT-1 RNA indicates a poor prognosis in non-small cell lung cancer and induces migration and tumor growth. J. Thorac. Oncol 6, 1984– 1992 (2011). 22088988
  7. [8]Andersson R Nuclear stability and transcriptional directionality separate functionally distinct RNA species. Nature Commun. 5, 5336 (2014). 25387874
  8. [9]Preker P RNA exosome depletion reveals transcription upstream of active human promoters. Science 322, 1851– 1854 (2008). 19056938
  9. [11]Quinn JJ & Chang HY Unique features of long non-coding RNA biogenesis and function. Nature Rev. Genet 17, 47– 62 (2016). 26666209
  10. [12]Palazzo AF & Lee ES Non-coding RNA: what is functional and what is junk? Front. Genet 6, 2 (2015). 25674102 PMC4306305
  11. [13]Engreitz JM Local regulation of gene expression by lncRNA promoters, transcription and splicing. Nature 539, 452– 455 (2016). 27783602 PMC6853796
  12. [15]Li MJ GWASdb v2: an update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res 44 (D1), D869– D876 (2016). 26615194 PMC4702921
  13. [16]Consortium GTEx. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648– 660 (2015). 25954001 PMC4547484
  14. [17]Farh KK-H Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337– 343 (2015). 25363779 PMC4336207
  15. [18]Steijger T Assessment of transcript reconstruction methods for RNA-seq. Nature Methods 10, 1177– 1184 (2013). 24185837 PMC3851240
  16. [19]Harrow J GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760– 1774 (2012). 22955987 PMC3431492
  17. [20]Shiraki T Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage. Proc. Natl Acad. Sci. USA 100, 15776– 15781 (2003). 14663149 PMC307644
  18. [21]Forrest ARR A promoter-level mammalian expression atlas. Nature 507, 462– 470 (2014). 24670764 PMC4529748
  19. [22]Arner E Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells. Science 347, 1010– 1014 (2015). 25678556 PMC4681433
  20. [24]Wang L CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model. Nucleic Acids Res. 41, e74 (2013). 23335781 PMC3616698
  21. [25]Batut P, Dobin A, Plessy C, Carninci P & Gingeras TR High-fidelity promoter profiling reveals widespread alternative promoter usage and transposon-driven developmental gene expression. Genome Res. 23, 169– 180 (2013). 22936248 PMC3530677
  22. [26]Sigova AA Divergent transcription of long noncoding RNA/mRNA gene pairs in embryonic stem cells. Proc. Natl Acad. Sci. USA 110, 2876– 2881 (2013). 23382218 PMC3581948
  23. [28]Core LJ Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers. Nature Genet. 46, 1311– 1320 (2014). 25383968 PMC4254663
  24. [29]Xiang J-F Human colorectal cancer-specific CCAT1-L lncRNA regulates long-range chromatin interactions at the mYc locus. Cell Res. 24, 513– 531 (2014). 24662484 PMC4011346
  25. [30]Ulitsky I Evolution to the rescue: using comparative genomics to understand long non-coding RNAs. Nature Rev. Genet. 17, 601– 614 (2016). 27573374
  26. [32]Villar D Enhancer evolution across 20 mammalian species. Cell 160, 554– 566 (2015). 25635462 PMC4313353
  27. [33]Ng S-Y, Johnson R & Stanton LW Human long non-coding RNAs promote pluripotency and neuronal differentiation by association with chromatin modifiers and transcription factors. EMBO J. 31, 522– 533 (2012). 22193719 PMC3273385
  28. [34]Holm H Several common variants modulate heart rate, PR interval and QRS duration. Nature Genet. 42, 117– 122 (2010). 20062063
  29. [35]Pfeufer A Genome-wide association study of PR interval. Nature Genet. 42, 153– 159 (2010). 20062060 PMC2850197
  30. [36]Smith JG Genome-wide association study of electrocardiographic conduction measures in an isolated founder population: Kosrae. Heart Rhythm 6, 634– 641 (2009). 19389651 PMC2673462
  31. [37]Paralkar VR Unlinking an lncRNA from its associated cis element. Mol. Cell 62, 104– 110 (2016). 27041223 PMC4877494
  32. [40]Lai F Activating RNAs associate with Mediator to enhance chromatin architecture and transcription. Nature 494, 497– 501 (2013). 23417068 PMC4109059
  33. [41]Clark MB The reality of pervasive transcription. PLoS Biol. 9, e1000625, (2011). 21765801 PMC3134446
  34. [42]Struhl K Transcriptional noise and the fidelity of initiation by RNA polymerase II. Nature Struct. Mol. Biol 14, 103– 105 (2007). 17277804
  35. [43]Severin J Interactive visualization and analysis of large-scale sequencing datasets using ZENBU. Nature Biotechnol. 32, 217– 219 (2014). 24727769
  36. [44]Hasegawa A, Daub C, Carninci P, Hayashizaki Y & Lassmann T MOIRAI: a compact workflow system for CAGE analysis. BMC Bioinformatics 15, 144 (2014). 24884663 PMC4033680
  37. [46]Grabherr MG Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnol. 29, 644– 652 (2011). 21572440 PMC3571712
  38. [47]Kent WJ BLAT--the BLAST-like alignment tool. Genome Res. 12, 656– 664 (2002). 11932250 PMC187518
  39. [48]Patro R, Mount SM & Kingsford C Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nature Biotechnol. 32, 462– 464 (2014). 24752080 PMC4077321
  40. [49]Ernst J & Kellis M ChromHMM: automating chromatin-state discovery and characterization. Nature Methods 9, 215– 216 (2012). 22373907 PMC3577932
  41. [50]Sloan CA ENCODE data at the ENCODE portal. Nucleic Acids Res. 44 (D1), D726– D732 (2016). 26527727 PMC4702836
  42. [51]Rice P, Longden I & Bleasby A EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 16, 276– 277 (2000). 10827456
  43. [52]Lin MF, Jungreis I & Kellis M PhyloCSF: a comparative genomics method to distinguish protein coding and non-coding regions. Bioinformatics 27, i275– i282 (2011). 21685081 PMC3117341
  44. [53]Washietl S RNAcode: robust discrimination of coding and noncoding regions in comparative sequence data. RNA 17, 578– 594 (2011). 21357752 PMC3062170
  45. [54]Olexiouk V sORFs.org: a repository of small ORFs identified by ribosome profiling. Nucleic Acids Res. 44 (D1), D324– D329 (2016). 26527729 PMC4702841
  46. [56]Wheeler TJ & Eddy SR nhmmer: DNA homology search with profile HMMs. Bioinformatics 29, 2487– 2489 (2013). 23842809 PMC3777106
  47. [57]Wheeler TJ Dfam: a database of repetitive DNA based on profile hidden Markov models. Nucleic Acids Res. 41, D70– D82 (2013). 23203985 PMC3531169
  48. [58]Robinson MD, McCarthy DJ & Smyth GK edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139– 140 (2010). 19910308 PMC2796818
  49. [60]Meehan TF Logical development of the cell ontology. BMC Bioinformatics 12, 6 (2011). 21208450 PMC3024222
  50. [62]Johnson AD SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 24, 2938– 2939 (2008). 18974171 PMC2720775
  51. [64]Sakharkar MK, Chow VTK & Kangueane P Distributions of exons and introns in the human genome. In Silico Biol. 4, 387– 393 (2004). 15217358
  52. [66]Pollard KS, Hubisz MJ, Rosenbloom KR & Siepel A Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110– 121 (2010). 19858363 PMC2798823
  53. [67]Bostock M, Ogievetsky V & Heer J D3: data-driven documents. IEEE Trans. Vis. Comput Graph 17, 2301– 2309 (2011). 22034350
  54. [68]Abugessaisa I FANTOM5 transcriptome catalog of cellular states based on Semantic MediaWiki. Database 2016, baw105 (2016). 27402679 PMC4940433

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Authors
Hon, C.-C., Ramilowski, J. A., Harshbarger, J., Bertin, N., Rackham, O. J. L., Gough, J., Denisenko, E., Schmeier, S., Poulsen, T. M., Severin, J., Lizio, M., Kawaji, H., Kasukawa, T., Itoh, M., Burroughs, A. M., Noma, S., Djebali, S., Alam, T., Medvedeva, Y. A., Testa, A. C., Lipovich, L., Yip, C.-W., Abugessaisa, I., Mendez, M., Hasegawa, A., Tang, D., Lassmann, T., Heutink, P., Babina, M., Wells, C. A., Kojima, S., Nakamura, Y., Suzuki, H., Daub, C. O., de Hoon, M. J. L., Arner, E., Hayashizaki, Y., Carninci, P., & Forrest, A. R. R.
Affiliations
  • RIKEN (JP)
  • National University of Singapore (SG)
  • University of Bristol (GB)
  • Massey University (NZ)
  • National Institute of Advanced Industrial Science and Technology (JP)
  • National Institutes of Health (US)
  • Universitat Pompeu Fabra (ES)
  • King Abdullah University of Science and Technology (SA)
  • Russian Academy of Sciences (RU)
  • The University of Western Australia (AU)
  • Wayne State University (US)
  • The Kids Research Institute Australia (AU)
  • German Center for Neurodegenerative Diseases (DE)
  • Charité – Universitätsmedizin Berlin (DE)
  • The University of Queensland (AU)
  • University of Tsukuba (JP)
  • Karolinska Institutet (SE)
Corresponding author
  • Carninci, Piero
  • Forrest, Alistair R. R.
Venue
Nature
Published
2017-03
Volume
543
Issue
7644
Pages
199-204
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
OpenAlex
W2593488635
Cited by
1,007
References
13
Citations by year
2026: 38 · 2025: 52 · 2024: 70 · 2023: 95 · 2022: 103 · 2021: 160 · 2020: 167 · 2019: 126 · 2018: 117 · 2017: 78
FWCI
45.02
Type
journal article
Language
en
Open access
Locations
  • Nature (publishedVersion)
  • UWA Profiles and Research Repository (UWA) (publishedVersion, other-oa)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • HAL (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • ePrints Soton (University of Southampton) (submittedVersion)
  • DZNE Pub (submittedVersion)
  • King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion)
Funders
  • Conquer Cancer Foundation
  • Ministry of Education, Culture, Sports, Science and Technology
  • Ministerio de Economía y Competitividad (SEV‐2012‐0208, BIO2011-26205)
  • Russian Science Foundation (15-14-30002)
  • National Institutes of Health
  • Biotechnology and Biological Sciences Research Council (BB/G022771/1)
  • Australian Research Council (DP160101960)
  • RIKEN
  • National Human Genome Research Institute (U54HG007004)
  • National Cancer Institute (DP2CA196375, P30CA022453)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
Keywords
long non-coding RNA, CAGE, lncRNA expression profiles, intergenic lncRNAs, single nucleotide polymorphisms, expression quantitative trait loci, transcriptional regulation, lncRNA conservation, tissue expression
MeSH terms
Cells, Cultured, Enhancer Elements, Genetic, Gene Expression Regulation, Humans, Organ Specificity, Promoter Regions, Genetic, RNA, Messenger, Genome, Human, Conserved Sequence, Internet, Polymorphism, Single Nucleotide, Gene Expression Profiling, RNA Stability, Genomics, Databases, Genetic, Quantitative Trait Loci, Epigenesis, Genetic, Genome-Wide Association Study, Molecular Sequence Annotation, Transcriptome, RNA, Long Noncoding, Datasets as Topic

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