CoChat
Sign Up
Cited by 89

Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

Wright, Carrie, Rajpurohit, Anandita, Burke, Emily E., Williams, Courtney, Collado-Torres, Leonardo, Kimos, Martha, Brandon, Nicholas J., Cross, Alan J. +3 more·2019·BMC Genomics
DOI 10.1186/s12864-019-5870-3PMC PMC6588940

Published version of a preprint

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 · 10.1186/s12864-019-5870-3

Cites

4 of 4 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 [28]

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: 58

  1. [1]Bandiera S Hatem E Lyonnet S Henrion-Caude A microRNAs in diseases: from candidate to modifier genes Clin Genet 2010 77 306 313 10.1111/j.1399-0004.2010.01370.x 20132241 · DOI
  2. [2]Miller BH Wahlestedt C MicroRNA dysregulation in psychiatric disease Brain Res 2010 1338 89 99 10.1016/j.brainres.2010.03.035 20303342 PMC2891055 · DOI
  3. [3]Basak I Patil KS Alves G Larsen JP Møller SG microRNAs as neuroregulators, biomarkers and therapeutic agents in neurodegenerative diseases Cell Mol Life Sci 2016 73 811 827 10.1007/s00018-015-2093-x 26608596 PMC11108480 · DOI
  4. [4]Reid G Kirschner MB van Zandwijk N Circulating microRNAs: association with disease and potential use as biomarkers Crit Rev Oncol Hematol 2011 80 193 208 10.1016/j.critrevonc.2010.11.004 21145252 · DOI
  5. [5]Eminaga Seda Christodoulou Danos C. Vigneault Francois Church George M. Seidman J.G. Quantification of microRNA Expression with Next-Generation Sequencing Current Protocols in Molecular Biology 2013 103 1 4.17.1-4.17.14 10.1002/0471142727.mb0417s103 PMC4138881 23821442 · DOI
  6. [6]Neilsen CT Goodall GJ Bracken CP IsomiRs – the overlooked repertoire in the dynamic microRNAome Trends Genet 2012 28 544 549 10.1016/j.tig.2012.07.005 22883467 · DOI · Paper page
  7. [7]Morin RD O’Connor MD Griffith M Kuchenbauer F Delaney A Prabhu A-L Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells Genome Res 2008 18 610 621 10.1101/gr.7179508 18285502 PMC2279248 · DOI · Paper page
  8. [9]Nejad C Pillman KA Siddle KJ Pépin G Änkö M-L McCoy CE miR-222 isoforms are differentially regulated by type-I interferon RNA. 2018 24 332 341 10.1261/rna.064550.117 29263133 PMC5824353 · DOI
  9. [10]Yu F Pillman KA Neilsen CT Toubia J Lawrence DM Tsykin A Naturally existing isoforms of miR-222 have distinct functions Nucleic Acids Res 2017 45 11371 11385 10.1093/nar/gkx788 28981911 PMC5737821 · DOI
  10. [11]Burroughs AM Ando Y de Hoon MJL Tomaru Y Nishibu T Ukekawa R A comprehensive survey of 3′ animal miRNA modification events and a possible role for 3′ adenylation in modulating miRNA targeting effectiveness Genome Res 2010 20 1398 1410 10.1101/gr.106054.110 20719920 PMC2945189 · DOI
  11. [12]Tan GC Dibb N IsomiRs have functional importance Malaysian J Pathol 2015 37 73 81 26277662
  12. [13]Telonis AG Magee R Loher P Chervoneva I Londin E Rigoutsos I Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types Nucleic Acids Res 2017 45 2973 2985 10.1093/nar/gkx082 28206648 PMC5389567 · DOI
  13. [14]Baran-Gale J Fannin EE Kurtz CL Sethupathy P Beta cell 5′-shifted isomiRs are candidate regulatory hubs in type 2 diabetes PLoS One 2013 8 e73240 10.1371/journal.pone.0073240 24039891 PMC3767796 · DOI
  14. [15]Martí E Pantano L Bañez-Coronel M Llorens F Miñones-Moyano E Porta S A myriad of miRNA variants in control and Huntington’s disease brain regions detected by massively parallel sequencing Nucleic Acids Res 2010 38 7219 7235 10.1093/nar/gkq575 20591823 PMC2978354 · DOI
  15. [16]Witwer KW Halushka MK Toward the promise of microRNAs – enhancing reproducibility and rigor in microRNA research RNA Biol 2016 13 1103 1116 10.1080/15476286.2016.1236172 27645402 PMC5100345 · DOI · Paper page
  16. [17]Linsen SEV de Wit E Janssens G Heater S Chapman L Parkin RK Limitations and possibilities of small RNA digital gene expression profiling Nat Methods 2009 6 4734 4476 10.1038/nmeth0709-474 19564845 · DOI
  17. [18]Raabe CA Tang T-H Brosius J Rozhdestvensky TS Biases in small RNA deep sequencing data Nucleic Acids Res 2014 42 1414 1426 10.1093/nar/gkt1021 24198247 PMC3919602 · DOI
  18. [19]Buschmann D Haberberger A Kirchner B Spornraft M Riedmaier I Schelling G Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow Nucleic Acids Res 2016 44 5995 6018 10.1093/nar/gkw545 27317696 PMC5291277 · DOI
  19. [20]Lopez JP, Diallo A, Cruceanu C, Fiori LM, Laboissiere S, Guillet I, et al. Biomarker discovery: quantification of microRNAs and other small non-coding RNAs using next generation sequencing. BMC Med Genet. 2015;8. 10.1186/s12920-015-0109-x. PMC4487992 26130076 · DOI
  20. [21]Head SR, Komori HK, LaMere SA, Whisenant T, Van Nieuwerburgh F, Salomon DR, et al. Library construction for next-generation sequencing: overviews and challenges. BioTechniques. 2014;56. 10.2144/000114133. PMC4351865 24502796 · DOI
  21. [22]Pritchard CC Cheng HH Tewari M MicroRNA profiling: approaches and considerations Nat Rev Genet 2012 13 358 369 10.1038/nrg3198 22510765 PMC4517822 · DOI · Paper page
  22. [23]Kim Y-K Yeo J Kim B Ha M Kim VN Short structured RNAs with low GC content are selectively lost during extraction from a small number of cells Mol Cell 2012 46 893 895 10.1016/j.molcel.2012.05.036 22749402 · DOI
  23. [24]Dard-Dascot C, Naquin D, d’Aubenton-Carafa Y, Alix K, Thermes C, van Dijk E. Systematic comparison of small RNA library preparation protocols for next-generation sequencing. BMC Genomics. 2018;19. 10.1186/s12864-018-4491-6. PMC5799908 29402217 · DOI
  24. [25]Sorefan K Pais H Hall AE Kozomara A Griffiths-Jones S Moulton V Reducing ligation bias of small RNAs in libraries for next generation sequencing Silence. 2012 3 4 10.1186/1758-907X-3-4 22647250 PMC3489589 · DOI
  25. [26]Giraldez Maria D Spengler Ryan M Etheridge Alton Godoy Paula M Barczak Andrea J Srinivasan Srimeenakshi De Hoff Peter L Tanriverdi Kahraman Courtright Amanda Lu Shulin Khoory Joseph Rubio Renee Baxter David Driedonks Tom A P Buermans Henk P J Nolte-'t Hoen Esther N M Jiang Hui Wang Kai Ghiran Ionita Wang Yaoyu E Van Keuren-Jensen Kendall Freedman Jane E Woodruff Prescott G Laurent Louise C Erle David J Galas David J Tewari Muneesh Comprehensive multi-center assessment of small RNA-seq methods for quantitative miRNA profiling Nature Biotechnology 2018 36 8 746 757 10.1038/nbt.4183 PMC6078798 30010675 · DOI · Paper page
  26. [27]Yeri A, Courtright A, Danielson K, Hutchins E, Alsop E, Carlson E, et al. Evaluation of commercially available small RNASeq library preparation kits using low input RNA. BMC Genomics. 2018;19. 10.1186/s12864-018-4726-6. PMC5936030 29728066 · DOI
  27. [28]Baran-Gale J, Kurtz CL, Erdos MR, Sison C, Young A, Fannin EE, et al. Addressing Bias in small RNA library preparation for sequencing: a new protocol recovers MicroRNAs that evade capture by current methods. Front Genet. 2015;6. 10.3389/fgene.2015.00352. PMC4686641 26734062 · DOI · Paper page
  28. [29]Fu Y, Wu P-H, Beane T, Zamore PD, Weng Z. Elimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers. BMC Genomics. 2018;19. 10.1186/s12864-018-4933-1. PMC6044086 30001700 · DOI
  29. [30]Faridani OR Abdullayev I Hagemann-Jensen M Schell JP Lanner F Sandberg R Single-cell sequencing of the small-RNA transcriptome Nat Biotechnol 2016 34 1264 1266 10.1038/nbt.3701 27798564 · DOI · Paper page
  30. [31]Hafner M Renwick N Brown M Mihailovic A Holoch D Lin C RNA-ligase-dependent biases in miRNA representation in deep-sequenced small RNA cDNA libraries RNA. 2011 17 1697 1712 10.1261/rna.2799511 21775473 PMC3162335 · DOI
  31. [32]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 2011 39 e141 10.1093/nar/gkr693 21890899 PMC3241666 · DOI
  32. [33]Dabney J, Meyer M. Length and GC-biases during sequencing library amplification: a comparison of various polymerase-buffer systems with ancient and modern DNA sequencing libraries. BioTechniques. 2012;52. 10.2144/000113809. 22313406 · DOI · Paper page
  33. [34]Hong J, Gresham D. Incorporation of unique molecular identifiers in TruSeq adapters improves the accuracy of quantitative sequencing. BioTechniques. 2017;63. 10.2144/000114608. PMC7359820 29185922 · DOI
  34. [36]Sena JA, Galotto G, Devitt NP, Connick MC, Jacobi JL, Umale PE, et al. Unique molecular identifiers reveal a novel sequencing artefact with implications for RNA-Seq based gene expression analysis. Sci Rep. 2018;8. 10.1038/s41598-018-31064-7. PMC6120941 30177820 · DOI
  35. [37]Alon S Vigneault F Eminaga S Christodoulou DC Seidman JG Church GM Barcoding bias in high-throughput multiplex sequencing of miRNA Genome Res 2011 21 1506 1511 10.1101/gr.121715.111 21750102 PMC3166835 · DOI
  36. [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 2015 10 e0126049 10.1371/journal.pone.0126049 25942392 PMC4420488 · DOI
  37. [39]Langmead B Trapnell C Pop M Salzberg SL Ultrafast and memory-efficient alignment of short DNA sequences to the human genome Genome Biol 2009 10 R25 10.1186/gb-2009-10-3-r25 19261174 PMC2690996 · DOI · Paper page
  38. [40]Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15. 10.1186/s13059-014-0550-8. PMC4302049 25516281 · DOI · Paper page
  39. [41]Tang DTP Plessy C Salimullah M Suzuki AM Calligaris R Gustincich S Suppression of artifacts and barcode bias in high-throughput transcriptome analyses utilizing template switching Nucleic Acids Res 2013 41 e44 10.1093/nar/gks1128 23180801 PMC3562004 · DOI
  40. [43]Zhuang F Fuchs RT Sun Z Zheng Y Robb GB Structural bias in T4 RNA ligase-mediated 3′-adapter ligation Nucleic Acids Res 2012 40 e54 10.1093/nar/gkr1263 22241775 PMC3326334 · DOI
  41. [44]Huang X Yuan T Tschannen M Sun Z Jacob H Du M Characterization of human plasma-derived exosomal RNAs by deep sequencing BMC Genomics 2013 14 319 10.1186/1471-2164-14-319 23663360 PMC3653748 · DOI
  42. [45]Barberán-Soler S, Vo JM, Hogans RE, Dallas A, Johnston BH, Kazakov SA. Decreasing miRNA sequencing bias using a single adapter and circularization approach. Genome Biol. 2018;19. 10.1186/s13059-018-1488-z. PMC6120088 30173660 · DOI
  43. [47]Honda S Kirino Y Dumbbell-PCR: a method to quantify specific small RNA variants with a single nucleotide resolution at terminal sequences Nucleic Acids Res 2015 43 e77 10.1093/nar/gkv218 25779041 PMC4499115 · DOI
  44. [48]Androvic P Valihrach L Elling J Sjoback R Kubista M Two-tailed RT-qPCR: a novel method for highly accurate miRNA quantification Nucleic Acids Res 2017 45 e144 10.1093/nar/gkx588 28911110 PMC5587787 · DOI
  45. [49]Choi YS Edwards LO DiBello A Jose AM Removing bias against short sequences enables northern blotting to better complement RNA-seq for the study of small RNAs Nucleic Acids Res 2017 45 e87 10.1093/nar/gkx091 28180294 PMC5449620 · DOI
  46. [50]Smith TS Heger A Sudbery I UMI-tools: modelling sequencing errors in unique molecular identifiers to improve quantification accuracy Genome Res 2017 27 491 499 10.1101/gr.209601.116 28100584 PMC5340976 · DOI
  47. [51]Baras AS Mitchell CJ Myers JR Gupta S Weng L-C Ashton JM miRge - a multiplexed method of processing small RNA-Seq data to determine MicroRNA entropy PLoS One 2015 10 e0143066 10.1371/journal.pone.0143066 26571139 PMC4646525 · DOI
  48. [52]Martin M Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet J 2011 17 10 12 10.14806/ej.17.1.200 · DOI · Paper page
  49. [53]Griffiths-Jones S miRBase: microRNA sequences, targets and gene nomenclature Nucleic Acids Res 2006 34 D140 D144 10.1093/nar/gkj112 16381832 PMC1347474 · DOI
  50. [54]Garmire LX Subramaniam S The poor performance of TMM on microRNA-Seq RNA. 2013 19 735 736 10.1261/rna.039271.113 23616641 PMC3683907 · DOI
  51. [55]Garmire LX Subramaniam S Evaluation of normalization methods in mammalian microRNA-Seq data RNA. 2012 18 1279 1288 10.1261/rna.030916.111 22532701 PMC3358649 · DOI
  52. [56]Dillies M-A Rau A Aubert J Hennequet-Antier C Jeanmougin M Servant N A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis Brief Bioinform 2013 14 671 683 10.1093/bib/bbs046 22988256 · DOI
  53. [57]Evans C Hardin J Stoebel DM Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions Brief Bioinform 2018 19 776 792 10.1093/bib/bbx008 28334202 PMC6171491 · DOI
  54. [58]Hofacker IL Fontana W Stadler PF Bonhoeffer LS Tacker M Schuster P Fast folding and comparison of RNA secondary structures Monatshefte fur Chemie Chemical Monthly 1994 125 167 188 10.1007/BF00818163 · DOI
  55. [59]Lorenz R Bernhart SH Höner zu Siederdissen C Tafer H Flamm C Stadler PF ViennaRNA Package 2.0 Algorithms Mol Biol 2011 6 26 10.1186/1748-7188-6-26 22115189 PMC3319429 · DOI · Paper page
  56. [62]Sarkar A Maji R Saha S Ghosh Z piRNAQuest: searching the piRNAome for silencers BMC Genomics 2014 15 555 10.1186/1471-2164-15-555 24997126 PMC4227290 · DOI
  57. [63]Karolchik D The UCSC table browser data retrieval tool Nucleic Acids Res 2004 32 493D 4496 10.1093/nar/gkh103 PMC308837 14681465 · DOI
  58. [64]Lestrade L snoRNA-LBME-db, a comprehensive database of human H/ACA and C/D box snoRNAs Nucleic Acids Res 2006 34 D158 D162 10.1093/nar/gkj002 16381836 PMC1347365 · DOI

Cited by

Sorted by citations

Authors
Wright, C., Rajpurohit, A., Burke, E. E., Williams, C., Collado-Torres, L., Kimos, M., Brandon, N. J., Cross, A. J., Jaffe, A. E., Weinberger, D. R., & Shin, J. H.
Venue
BMC Genomics
Publisher
Springer Science and Business Media LLC
Published
2019-06-21
Volume
20
Issue
1
ISSN
1471-2164
DOI
10.1186/s12864-019-5870-3
PMC
PMC6588940
Cited by
89
References
4
Language
en
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