CoChat
Sign Up
Cited by 177

Comprehensive multi-center assessment of small RNA-seq methods for quantitative miRNA profiling

Giraldez, Maria D, Spengler, Ryan M, Etheridge, Alton, Godoy, Paula M, Barczak, Andrea J, Srinivasan, Srimeenakshi, De Hoff, Peter L, Tanriverdi, Kahraman +19 more·2018·Nature Biotechnology
DOI 10.1038/nbt.4183PMC PMC6078798
Open this paper in CoChat — free account

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 · 10.1038/nbt.4183

Cites

12 of 12 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?

  • Excluding other library preparation cases would improve batch correction.

    “We are aware of large expression estimate differences by library preparation methods due to ligation biases and other differences and felt excluding these other cases would improve batch correction [28, 29].”

    From A curated human cellular microRNAome based on 196 primary cell types (2022) · cites this paper as [29]

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: 43

  1. [3]Levin JZ Comprehensive comparative analysis of strand-specific RNA sequencing methods Nat Methods 7 709 715 2010 20711195 PMC3005310
  2. [4]Jayaprakash AD Jabado O Brown BD Sachidanandam R Identification and remediation of biases in the activity of RNA ligases in small-RNA deep sequencing Nucleic Acids Res 39 e141 2011 21890899 PMC3241666
  3. [5]Viollet S Fuchs RT Munafo DB Zhuang F Robb GB T4 RNA ligase 2 truncated active site mutants: improved tools for RNA analysis BMC Biotechnol 11 72 2011 21722378 PMC3149579
  4. [6]Zhang Z Lee JE Riemondy K Anderson EM Yi R High-efficiency RNA cloning enables accurate quantification of miRNA expression by deep sequencing Genome Biol 14 R109 2013 24098942 PMC3983620
  5. [7]Song Y Liu KJ Wang TH Elimination of ligation dependent artifacts in T4 RNA ligase to achieve high efficiency and low bias microRNA capture PLoS One 9 e94619 2014 24722341 PMC3983213
  6. [8]Baran-Gale J Addressing Bias in Small RNA Library Preparation for Sequencing: A New Protocol Recovers MicroRNAs that Evade Capture by Current Methods Front Genet 6 352 2015 26734062 PMC4686641
  7. [9]Sorefan K Reducing ligation bias of small RNAs in libraries for next generation sequencing Silence 3 4 2012 22647250 PMC3489589
  8. [10]Bellingham SA Coleman BM Hill AF Small RNA deep sequencing reveals a distinct miRNA signature released in exosomes from prion-infected neuronal cells Nucleic Acids Res 40 10937 10949 2012 22965126 PMC3505968
  9. [11]Nolte E Buermans H Waasdorp M Deep sequencing of RNA from immune cell-derived vesicles uncovers the selective incorporation of small non-coding RNA biotypes with potential regulatory functions Nucleic acids 2012 PMC3467056 22821563
  10. [12]Huang X Characterization of human plasma-derived exosomal RNAs by deep sequencing BMC Genomics 14 319 2013 23663360 PMC3653748
  11. [13]Tietje A Maron KN Wei Y Feliciano DM Cerebrospinal fluid extracellular vesicles undergo age dependent declines and contain known and novel non-coding RNAs PLoS One 9 e113116 2014 25420022 PMC4242609
  12. [14]Lunavat TR Small RNA deep sequencing discriminates subsets of extracellular vesicles released by melanoma cells–Evidence of unique microRNA cargos RNA Biol 12 810 823 2015 26176991 PMC4615768
  13. [15]Tosar JP Assessment of small RNA sorting into different extracellular fractions revealed by high-throughput sequencing of breast cell lines Nucleic Acids Res 43 5601 5616 2015 25940616 PMC4477662
  14. [16]van Balkom BWM Eisele AS Pegtel DM Bervoets S Verhaar MC Quantitative and qualitative analysis of small RNAs in human endothelial cells and exosomes provides insights into localized RNA processing, degradation and sorting Journal of extracellular vesicles 4 2015 PMC4450249 26027894
  15. [17]Burgos KL Identification of extracellular miRNA in human cerebrospinal fluid by next-generation sequencing RNA 19 712 722 2013 23525801 PMC3677285
  16. [18]Bahn JH The landscape of microRNA, Piwi-interacting RNA, and circular RNA in human saliva Clin Chem 61 221 230 2015 25376581 PMC4332885
  17. [20]Wecker T MicroRNA Profiling in Aqueous Humor of Individual Human Eyes by Next-Generation Sequencing Invest Ophthalmol Vis Sci 57 1706 1713 2016 27064390
  18. [21]Yuan T Plasma extracellular RNA profiles in healthy and cancer patients Sci Rep 6 19413 2016 26786760 PMC4726401
  19. [23]Risso D Ngai J Speed TP Dudoit S Normalization of RNA-seq data using factor analysis of control genes or samples Nat Biotechnol 32 896 902 2014 25150836 PMC4404308
  20. [24]Lin Y Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster BMC Genomics 17 28 2016 26732976 PMC4702322
  21. [27]Leinonen R Sugawara H Shumway M International Nucleotide Sequence Database Collaboration The sequence read archive Nucleic Acids Res 39 D19 21 2011 21062823 PMC3013647
  22. [28]Kodama Y Shumway M Leinonen R International Nucleotide Sequence Database Collaboration The Sequence Read Archive: explosive growth of sequencing data Nucleic Acids Res 40 D54 6 2012 22009675 PMC3245110
  23. [29]Kalra H Vesiclepedia: a compendium for extracellular vesicles with continuous community annotation PLoS Biol 10 e1001450 2012 23271954 PMC3525526
  24. [30]Simpson RJ Kalra H Mathivanan S ExoCarta as a resource for exosomal research J Extracell Vesicles 1 2012 PMC3760644 24009883
  25. [31]Kim DK EVpedia: an integrated database of high-throughput data for systemic analyses of extracellular vesicles J Extracell Vesicles 2 2013 PMC3760654 24009897
  26. [32]Weinstein JN The cancer genome atlas pan-cancer analysis project Nat Genet 45 1113 1120 2013 24071849 PMC3919969
  27. [33]Subramanian SL Integration of extracellular RNA profiling data using metadata, biomedical ontologies and Linked Data technologies J Extracell Vesicles 4 27497 2015 26320941 PMC4553261
  28. [34]Ainsztein AM The NIH Extracellular RNA Communication Consortium J Extracell Vesicles 4 27493 2015 26320938 PMC4553264
  29. [36]Hansen KD Brenner SE Dudoit S Biases in Illumina transcriptome sequencing caused by random hexamer priming Nucleic Acids Res 38 e131 2010 20395217 PMC2896536
  30. [37]Hafner M RNA-ligase-dependent biases in miRNA representation in deep-sequenced small RNA cDNA libraries RNA 17 1697 1712 2011 21775473 PMC3162335
  31. [38]Fuchs RT Sun Z Zhuang F Robb GB Bias in ligation-based small RNA sequencing library construction is determined by adaptor and RNA structure PLoS One 10 e0126049 2015 25942392 PMC4420488
  32. [39]Robinson MD McCarthy DJ Smyth G K edgeR: a Bioconductor package for differential expression analysis of digital gene expression data Bioinformatics 26 139 140 2010 19910308 PMC2796818
  33. [40]McCarthy DJ Chen Y Smyth GK Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation Nucleic Acids Res 40 4288 4297 2012 22287627 PMC3378882
  34. [41]Love MI Huber W Anders S Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biol 15 550 2014 25516281 PMC4302049
  35. [42]Law CW Chen Y Shi W Smyth G K voom: Precision weights unlock linear model analysis tools for RNA-seq read counts Genome Biol 15 R29 2014 24485249 PMC4053721
  36. [43]Cortez MA MicroRNAs in body fluids—the mix of hormones and biomarkers Nat Rev Clin Oncol 8 467 477 2011 21647195 PMC3423224
  37. [44]Yang W Modulation of microRNA processing and expression through RNA editing by ADAR deaminases Nat Struct Mol Biol 13 13 21 2006 16369484 PMC2950615
  38. [45]Kawahara Y Redirection of silencing targets by adenosine-to-inosine editing of miRNAs Science 315 1137 1140 2007 17322061 PMC2953418
  39. [46]Wang Y Systematic characterization of A-to-I RNA editing hotspots in microRNAs across human cancers Genome Res 27 1112 1125 2017 28411194 PMC5495064
  40. [47]Warnefors M Liechti A Halbert J Valloton D Kaessmann H Conserved microRNA editing in mammalian evolution, development and disease Genome Biol 15 R83 2014 24964909 PMC4197820
  41. [48]Linsen SEV Limitations and possibilities of small RNA digital gene expression profiling Nat Methods 6 474 476 2009 19564845
  42. [55]Chu A Large-scale profiling of microRNAs for The Cancer Genome Atlas Nucleic Acids Res 44 e3 2016 26271990 PMC4705681
  43. [56]Markham NR Zuker M UNAFold: software for nucleic acid folding and hybridization Methods Mol Biol 453 3 31 2008 18712296

Cited by

Sorted by citations

Authors
Giraldez, M. D., Spengler, R. M., Etheridge, A., Godoy, P. M., Barczak, A. J., Srinivasan, S., De Hoff, P. L., Tanriverdi, K., Courtright, A., Lu, S., Khoory, J., Rubio, R., Baxter, D., Driedonks, T. A. P., Buermans, H. P. J., Nolte-'t Hoen, E. N. M., Jiang, H., Wang, K., Ghiran, I., Wang, Y. E., Van Keuren-Jensen, K., Freedman, J. E., Woodruff, P. G., Laurent, L. C., Erle, D. J., Galas, D. J., & Tewari, M.
ORCID
Venue
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Published
2018-07-16
Volume
36
Issue
8
Pages
746-757
ISSN
  • 1087-0156
  • 1546-1696
DOI
10.1038/nbt.4183
PMC
PMC6078798
Cited by
177
References
12
Language
en
Update notices
erratum (2018-10-01)
Licence

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