Cited by 253

MirGeneDB 2.0: the metazoan microRNA complement

Fromm, Bastian, Domanska, Diana, Høye, Eirik, Ovchinnikov, Vladimir, Kang, Wenjing, Aparicio-Puerta, Ernesto, Johansen, Morten, Flatmark, Kjersti +5 more·2020·Nucleic Acids Research

Published version of a preprint

Abstract

Small non-coding RNAs have gained substantial attention due to their roles in animal development and human disorders. Among them, microRNAs are special because individual gene sequences are conserved across the animal kingdom. In addition, unique and mechanistically well understood features can clearly distinguish bona fide miRNAs from the myriad other small RNAs generated by cells. However, making this distinction is not a common practice and, thus, not surprisingly, the heterogeneous quality of available miRNA complements has become a major concern in microRNA research. We addressed this by extensively expanding our curated microRNA gene database - MirGeneDB - to 45 organisms, encompassing a wide phylogenetic swath of animal evolution. By consistently annotating and naming 10,899 microRNA genes in these organisms, we show that previous microRNA annotations contained not only many false positives, but surprisingly lacked >2000 bona fide microRNAs. Indeed, curated microRNA complements of closely related organisms are very similar and can be used to reconstruct ancestral miRNA repertoires. MirGeneDB represents a robust platform for microRNA-based research, providing deeper and more significant insights into the biology and evolution of miRNAs as well as biomedical and biomarker research. MirGeneDB is publicly and freely available at http://mirgenedb.org/.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/nar/gkz885

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • miRGeneDB has clearly established itself as the resource for bona fide miRNAs, while miRBase still includes scores of dubiously identified miRNAs.

    “miRGeneDB has clearly established itself as the resource for bona fide miRNAs, while miRBase still includes scores of dubiously identified miRNAs [6, 14, 16, 31, 32].”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 80

  1. [1]Matera A.G., Terns R.M., Terns M.P. Non-coding RNAs: lessons from the small nuclear and small nucleolar RNAs. Nat. Rev. Mol. Cell Biol. 2007; 8: 209– 220. 17318225
  2. [2]Hamilton A.J., Baulcombe D.C. A species of small antisense RNA in posttranscriptional gene silencing in plants. Science. 1999; 286: 950– 952. 10542148
  3. [3]Lau N.C., Seto A.G., Kim J., Kuramochi-Miyagawa S., Nakano T., Bartel D.P., Kingston R.E. Characterization of the piRNA complex from rat testes. Science. 2006; 313: 363– 367. 16778019
  4. [5]Lagos-Quintana M., Rauhut R., Lendeckel W., Tuschl T. Identification of novel genes coding for small expressed RNAs. Science. 2001; 294: 853– 858. 11679670
  5. [6]Lau N.C., Lim L.P., Weinstein E.G., Bartel D.P. An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science. 2001; 294: 858– 862. 11679671
  6. [7]Lee R.C., Ambros V. An extensive class of small RNAs in Caenorhabditis elegans. Science. 2001; 294: 862– 864. 11679672
  7. [8]Goodarzi H., Liu X., Nguyen H.C.B., Zhang S., Fish L., Tavazoie S.F. Endogenous tRNA-Derived fragments suppress breast cancer progression via YBX1 Displacement. Cell. 2015; 161: 790– 802. 25957686 PMC4457382
  8. [9]Chak L.-L., Mohammed J., Lai E.C., Tucker-Kellogg G., Okamura K. A deeply conserved, noncanonical miRNA hosted by ribosomal DNA. RNA. 2015; 21: 375– 384. 25605965 PMC4338334
  9. [10]Reinhart B.J., Slack F.J., Basson M., Pasquinelli A.E., Bettinger J.C., Rougvie A.E., Horvitz H.R., Ruvkun G. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature. 2000; 403: 901– 906. 10706289
  10. [11]Wheeler B.M., Heimberg A.M., Moy V.N., Sperling E.A., Holstein T.W., Heber S., Peterson K.J. The deep evolution of metazoan microRNAs. Evol. Dev. 2009; 11: 50– 68. 19196333
  11. [12]Sempere L.F., Cole C.N., McPeek M.A., Peterson K.J. The phylogenetic distribution of metazoan microRNAs: insights into evolutionary complexity and constraint. J. Exp. Zool. B. Mol. Dev. Evol. 2006; 306: 575– 588. 16838302
  12. [13]Castellano L., Stebbing J. Deep sequencing of small RNAs identifies canonical and non-canonical miRNA and endogenous siRNAs in mammalian somatic tissues. Nucleic Acids Res. 2013; 41: 3339– 3351. 23325850 PMC3597668
  13. [14]Chiang H.R., Schoenfeld L.W., Ruby J.G., Auyeung V.C., Spies N., Baek D., Johnston W.K., Russ C., Luo S., Babiarz J.E. et al. Mammalian microRNAs: experimental evaluation of novel and previously annotated genes. Genes Dev. 2010; 24: 992– 1009. 20413612 PMC2867214
  14. [15]Jones-Rhoades M.W. Conservation and divergence in plant microRNAs. Plant Mol. Biol. 2012; 80: 3– 16. 21996939
  15. [16]Ludwig N., Becker M., Schumann T., Speer T., Fehlmann T., Keller A., Meese E. Bias in recent miRBase annotations potentially associated with RNA quality issues. Sci. Rep. 2017; 7: 5162. 28701729 PMC5507985
  16. [17]Langenberger D., Bartschat S., Hertel J., Hoffmann S., Tafer H., Stadler P.F. MicroRNA or Not MicroRNA? Advances in Bioinformatics and Computational Biology. 2011; Berlin Heidelberg Springer 1– 9.
  17. [18]Meng Y., Shao C., Wang H., Chen M. Are all the miRBase-registered microRNAs true? A structure- and expression-based re-examination in plants. RNA Biol. 2012; 9: 249– 253. 22336711
  18. [19]Tarver J.E., Donoghue P.C., Peterson K.J. Do miRNAs have a deep evolutionary history. Bioessays. 2012; 34: 857– 866. 22847169
  19. [20]Taylor R.S., Tarver J.E., Hiscock S.J., Donoghue P.C. Evolutionary history of plant microRNAs. Trends Plant Sci. 2014; 19: 175– 182. 24405820
  20. [21]Wang X., Liu X.S. Systematic curation of miRBase annotation using integrated small RNA High-Throughput sequencing data for C. elegans and Drosophila. Front. Genet. 2011; 2: 25. 22303321 PMC3268580
  21. [22]Axtell M.J., Meyers B.C. Revisiting criteria for plant MicroRNA Annotation in the era of Big Data. Plant Cell. Am. Soc Plant Biol. 2018; 30: 272– 284. PMC5868703 29343505
  22. [23]Fromm B., Billipp T., Peck L.E., Johansen M., Tarver J.E., King B.L., Newcomb J.M., Sempere L.F., Flatmark K., Hovig E. et al. A uniform system for the annotation of vertebrate microRNA Genes and the evolution of the human microRNAome. Annu. Rev. Genet. 2015; 49: 213– 242. 26473382 PMC4743252
  23. [25]Jha A., Panzade G., Pandey R., Shankar R. A legion of potential regulatory sRNAs exists beyond the typical microRNAs microcosm. Nucleic Acids Res. 2015; 43: 8713– 8724. 26354861 PMC4605316
  24. [26]Cheng W.-C., Chung I.-F., Tsai C.-F., Huang T.-S., Chen C.-Y., Wang S.-C., Chang T.-Y., Sun H.-J., Chao J.Y.-C., Cheng C.-C. et al. YM500v2: a small RNA sequencing (smRNA-seq) database for human cancer miRNome research. Nucleic Acids Res. 2015; 43: D862– D867. 25398902 PMC4383957
  25. [27]Alles J., Fehlmann T., Fischer U., Backes C., Galata V., Minet M., Hart M., Abu-Halima M., Grässer F.A., Lenhof H.-P. et al. An estimate of the total number of true human miRNAs. Nucleic Acids Res. 2019; 47: 3353– 3364. 30820533 PMC6468295
  26. [28]Ebert M.S., Sharp P.A. Roles for microRNAs in conferring robustness to biological processes. Cell. 2012; 149: 515– 524. 22541426 PMC3351105
  27. [29]Gosline S.J.C., Gurtan A.M., JnBaptiste C.K., Bosson A., Milani P., Dalin S., Matthews B.J., Yap Y.S., Sharp P.A., Fraenkel E. Elucidating MicroRNA regulatory networks using transcriptional, Post-transcriptional, and histone modification measurements. Cell Rep. 2016; 14: 310– 319. 26748710 PMC4831719
  28. [30]Alberti C., Cochella L. A framework for understanding the roles of miRNAs in animal development. Development. 2017; 144: 2548– 2559. 28720652
  29. [31]Lim L.P., Lau N.C., Garrett-Engele P., Grimson A., Schelter J.M., Castle J., Bartel D.P., Linsley P.S., Johnson J.M. Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature. 2005; 433: 769– 773. 15685193
  30. [32]Lu J., Getz G., Miska E.A., Alvarez-Saavedra E., Lamb J., Peck D., Sweet-Cordero A., Ebert B.L., Mak R.H., Ferrando A.A. et al. MicroRNA expression profiles classify human cancers. Nature. 2005; 435: 834– 838. 15944708
  31. [33]Volinia S., Calin G.A., Liu C.G., Ambs S., Cimmino A., Petrocca F., Visone R., Iorio M., Roldo C., Ferracin M. et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 2257– 2261. 16461460 PMC1413718
  32. [34]Ambros V., Bartel B., Bartel D.P., Burge C.B., Carrington J.C., Chen X., Dreyfuss G., Eddy S.R., Griffiths-Jones S., Marshall M. et al. A uniform system for microRNA annotation. RNA. 2003; 9: 277– 279. 12592000 PMC1370393
  33. [35]Tosar J.P., Rovira C., Cayota A. Non-coding RNA fragments account for the majority of annotated piRNAs expressed in somatic non-gonadal tissues. Commun. Biol. 2018; 1: 2. 30271890 PMC6052916
  34. [36]Hou X.-S., Han C.-Q., Zhang W. MiR-1182 inhibited metastasis and proliferation of ovarian cancer by targeting hTERT. Eur. Rev. Med. Pharmacol. Sci. 2018; 22: 1622– 1628. 29630105
  35. [37]Zhang D., Xiao Y.-F., Zhang J.-W., Xie R., Hu C.-J., Tang B., Wang S.-M., Wu Y.-Y., Hao N.-B., Yang S.-M. miR-1182 attenuates gastric cancer proliferation and metastasis by targeting the open reading frame of hTERT. Cancer Lett. 2015; 360: 151– 159. 25662441
  36. [38]Zhou J., Dai W., Song J. miR-1182 inhibits growth and mediates the chemosensitivity of bladder cancer by targeting hTERT. Biochem. Biophys. Res. Commun. 2016; 470: 445– 452. 26772886
  37. [39]Kozomara A., Birgaoanu M., Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019; 47: D155– D162. 30423142 PMC6323917
  38. [40]Backes C., Fehlmann T., Kern F., Kehl T., Lenhof H.-P., Meese E., Keller A. miRCarta: a central repository for collecting miRNA candidates. Nucleic Acids Res. 2018; 46: D160– D167. 29036653 PMC5753177
  39. [41]Tarver J.E., Taylor R.S., Puttick M.N., Lloyd G.T., Pett W., Fromm B., Schirrmeister B.E., Pisani D., Peterson K.J., Donoghue P.C.J. Well-annotated microRNAomes do not evidence pervasive miRNA loss. Genome Biol. Evol. 2018; 10: 1457– 1470. 29788279 PMC6007596
  40. [42]Engkvist M.E., Stratford E.W., Lorenz S., Meza-Zepeda L.A., Myklebost O., Munthe E. Analysis of the miR-34 family functions in breast cancer reveals annotation error of miR-34b. Sci Rep. 2017; 7: 9655. 28848235 PMC5573726
  41. [43]Fromm B., Tosar J.P., Lu Y., Halushka M.K., Witwer K.W. Human and Cow Have Identical miR-21-5p and miR-30a-5p Sequences, Which Are Likely Unsuited to Study Dietary Uptake from Cow Milk. The Journal of Nutrition. 2018; 148: 1506– 1507. 30184228
  42. [44]Van Peer G., Lefever S., Anckaert J., Beckers A., Rihani A., Van Goethem A., Volders P.J., Zeka F., Ongenaert M., Mestdagh P. et al. miRBase Tracker: keeping track of microRNA annotation changes. Database. 2014; 2014: bau080. 25157074 PMC4142392
  43. [45]Zhong X., Heinicke F., Rayner S. miRBaseMiner, a tool for investigating miRBase content. RNA Biol. 2019; 16: 1534– 1546. 31251108 PMC6779376
  44. [46]Lu T.-P., Lee C.-Y., Tsai M.-H., Chiu Y.-C., Hsiao C.K., Lai L.-C., Chuang E.Y. miRSystem: an integrated system for characterizing enriched functions and pathways of microRNA targets. PLoS One. 2012; 7: e42390. 22870325 PMC3411648
  45. [47]Bonnal R.J.P., Rossi R.L., Carpi D., Ranzani V., Abrignani S., Pagani M. miRiadne: a web tool for consistent integration of miRNA nomenclature. Nucleic Acids Res. 2015; 43: W487– W492. 25897123 PMC4489305
  46. [48]Xu T., Su N., Liu L., Zhang J., Wang H., Zhang W., Gui J., Yu K., Li J., Le T.D. miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase. BMC Bioinformatics. BioMed Central. 2018; 19: 514. PMC6311916 30598108
  47. [49]Haunsberger S.J., Connolly N.M.C., Prehn J.H.M. miRNAmeConverter: an R/bioconductor package for translating mature miRNA names to different miRBase versions. Bioinformatics. 2017; 33: 592– 593. 27797767
  48. [50]Budak H., Bulut R., Kantar M., Alptekin B. MicroRNA nomenclature and the need for a revised naming prescription. Brief. Funct. Genomics. 2016; 15: 65– 71. 26148500
  49. [51]Grimson A (Early origins and evolution of microRNAs and Piwi-interacting RNAs in animals. Nature. 2008; 455: 1193– 1197. 18830242 PMC3837422
  50. [52]Jan C.H., Friedman R.C., Ruby J.G., Bartel D.P. Formation, regulation and evolution of Caenorhabditis elegans 3′UTRs. Nature. 2011; 469: 97– 101. 21085120 PMC3057491
  51. [53]Rajagopalan R., Vaucheret H., Trejo J., Bartel D.P. A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana. Genes Dev. 2006; 20: 3407– 3425. 17182867 PMC1698448
  52. [54]Ruby J.G. Large-scale sequencing reveals 21U-RNAs and additional microRNAs and endogenous siRNAs in C. elegans. Cell. 2006; 127: 1193– 1207. 17174894
  53. [55]Ruby J.G. Evolution, biogenesis, expression, and target predictions of a substantially expanded set of Drosophila microRNAs. Genome Res. 2007; 17: 1850– 1864. 17989254 PMC2099593
  54. [56]Aparicio-Puerta E., Lebrón R., Rueda A., Gómez-Martín C., Giannoukakos S., Jaspez D., Medina J.M., Zubkovic A., Jurak I., Fromm B. et al. sRNAbench and sRNAtoolbox 2019: intuitive fast small RNA profiling and differential expression. Nucleic Acids Res. 2019; 47: W530– W535. 31114926 PMC6602500
  55. [57]Kang W., Eldfjell Y., Fromm B., Estivill X., Biryukova I., Friedländer M.R. miRTrace reveals the organismal origins of microRNA sequencing data. Genome Biol. BioMed Central. 2018; 19: 213. PMC6280396 30514392
  56. [58]de Rie D., Abugessaisa I., Alam T., Arner E., Arner P., Ashoor H., Åström G., Babina M., Bertin N., Burroughs A.M. et al. An integrated expression atlas of miRNAs and their promoters in human and mouse. Nat. Biotechnol. 2017; 35: 872– 878. 28829439 PMC5767576
  57. [59]Kim Y.-K., Kim B., Kim V.N. Re-evaluation of the roles of DROSHA, Export in 5, and DICER in microRNA biogenesis. Proc. Natl. Acad. Sci. U.S.A. 2016; 113: E1881– E1889. 26976605 PMC4822641
  58. [61]Juzenas S., Venkatesh G., Hübenthal M., Hoeppner M.P., Du Z.G., Paulsen M., Rosenstiel P., Senger P., Hofmann-Apitius M., Keller A. et al. A comprehensive, cell specific microRNA catalogue of human peripheral blood. Nucleic Acids Res. 2017; 45: 9290– 9301. 28934507 PMC5766192
  59. [62]Halushka M.K., Fromm B., Peterson K.J., McCall M.N. Big strides in cellular MicroRNA Expression. Trends Genet. Elsevier Curr. Trends. 2018; 34: 165– 167. PMC5834366 29361313
  60. [63]Desvignes T., Loher P., Eilbeck K., Ma J., Urgese G., Fromm B., Sydes J., Aparicio-Puerta E., Barrera V., Espín R. et al. Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API. Bioinformatics. 2019; doi:10.1093/bioinformatics/btz675. PMC7566869 31504201 · DOI
  61. [64]Neilsen C.T., Goodall G.J., Bracken C.P. IsomiRs–the overlooked repertoire in the dynamic microRNAome. Trends Genet. 2012; 28: 544– 549. 22883467
  62. [65]Kim B., Ha M., Loeff L., Chang H., Simanshu D.K., Li S., Fareh M., Patel D.J., Joo C., Kim V.N. TUT7 controls the fate of precursor microRNAs by using three different uridylation mechanisms. EMBO J. 2015; 34: 1801– 1815. 25979828 PMC4516432
  63. [66]Heo I., Ha M., Lim J., Yoon M.-J., Park J.-E., Kwon S.C., Chang H., Kim V.N. Mono-uridylation of pre-microRNA as a key step in the biogenesis of group II let-7 microRNAs. Cell. 2012; 151: 521– 532. 23063654
  64. [67]Fang W., Bartel D.P. The menu of features that define primary MicroRNAs and Enable de novo design of MicroRNA Genes. Mol. Cell. 2015; 60: 131– 145. 26412306 PMC4613790
  65. [68]Bartel D.P. Metazoan MicroRNAs. Cell. 2018; 173: 20– 51. 29570994 PMC6091663
  66. [69]Auyeung V.C., Ulitsky I., McGeary S.E., Bartel D.P. Beyond secondary structure: primary-sequence determinants license pri-miRNA hairpins for processing. Cell. 2013; 152: 844– 858. 23415231 PMC3707628
  67. [70]Manzano M., Forte E., Raja A.N., Schipma M.J., Gottwein E. Divergent target recognition by coexpressed 5′-isomiRs of miR-142-3p and selective viral mimicry. RNA. 2015; 21: 1606– 1620. 26137849 PMC4536321
  68. [71]Tyler D.M., Okamura K., Chung W.-J., Hagen J.W., Berezikov E., Hannon G.J., Lai E.C. Functionally distinct regulatory RNAs generated by bidirectional transcription and processing of microRNA loci. Genes Dev. 2008; 22: 26– 36. 18172163 PMC2151012
  69. [73]Kozomara A., Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014; 42: D68– D73. 24275495 PMC3965103
  70. [74]Tarver J.E., Sperling E.A., Nailor A., Heimberg A.M., Robinson J.M., King B.L., Pisani D., Donoghue P.C., Peterson K.J. miRNAs: small genes with big potential in metazoan phylogenetics. Mol. Biol. Evol. 2013; 30: 2369– 2382. 23913097
  71. [75]Backes C., Meder B., Hart M., Ludwig N., Leidinger P., Vogel B., Galata V., Roth P., Menegatti J., Grässer F. et al. Prioritizing and selecting likely novel miRNAs from NGS data. Nucleic Acids Res. 2016; 44: e53. 26635395 PMC4824081
  72. [76]Thomson R.C., Plachetzki D.C., Mahler D.L., Moore B.R. A critical appraisal of the use of microRNA data in phylogenetics. Proc. Natl. Acad. Sci. U.S.A. 2014; 111: E3659– E3668. 25071211 PMC4156711
  73. [77]Zhang Z., Schwartz S., Wagner L., Miller W. A greedy algorithm for aligning DNA sequences. J. Comput. Biol. 2000; 7: 203– 214. 10890397
  74. [78]Griffiths-Jones S., Grocock R.J., van Dongen S., Bateman A., Enright A.J. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006; 34: D140– D144. 16381832 PMC1347474
  75. [79]Pasquinelli A.E. Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA. Nature. 2000; 408: 86– 89. 11081512
  76. [80]Pasquinelli A.E., McCoy A., Jiménez E., Salo E., Ruvkun G., Martindale M.Q., Baguna J. Expression of the 22 nucleotide let-7 heterochronic RNA throughout the Metazoa: a role in life history evolution? Evol Dev. Wiley Online Library. 2003; 5: 372– 378. 12823453
  77. [81]Delsuc F., Brinkmann H., Chourrout D., Philippe H. Tunicates and not cephalochordates are the closest living relatives of vertebrates. Nature. 2006; 439: 965– 968. 16495997
  78. [82]Hertel J., Bartschat S., Wintsche A., Otto C., Stadler P.F. SBCL Evolution of the let-7 microRNA Family. RNA Biol. 2012; 9: 231– 241. 22617875 PMC3384580
  79. [83]Lai E.C. Micro RNAs are complementary to 3′ UTR sequence motifs that mediate negative post-transcriptional regulation. Nat. Genet. 2002; 30: 363– 364. 11896390
  80. [84]Lewis B.P., Shih I.H., Jones-Rhoades M.W., Bartel D.P., Burge C.B. Prediction of mammalian microRNA targets. Cell. 2003; 115: 787– 798. 14697198

Cited by

Sorted by citations

Authors
Fromm, B., Domanska, D., Høye, E., Ovchinnikov, V., Kang, W., Aparicio-Puerta, E., Johansen, M., Flatmark, K., Mathelier, A., Hovig, E., Hackenberg, M., Friedländer, M. R., & Peterson, K. J.
Affiliations
  • Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
  • Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
  • Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
  • School of Life Sciences, Faculty of Health and Life Sciences, University of Nottingham, UK
  • Department of Genetics, Faculty of Sciences, University of Granada, Granada, Spain
  • Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo, Norway
  • Department of Biological Sciences, Dartmouth College, Hanover, NH, USA
Venue
Nucleic Acids Research
Published
2019-10-10
Volume
48
Issue
D1
Pages
D132-D141
ISSN
  • 0305-1048
  • 1362-4962
Cited by
253
References
5
Language
en
Funders
  • Swedish Research Council
  • Southern and Eastern Norway Regional Health Authority (2014041, 2018014)
  • Russian Science Foundation (18-15-00098)
  • University of Nottingham
  • Norwegian Research Council
  • National Science Foundation
  • NASA-Ames
  • Dartmouth College

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review